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Traditional methods for producing architectural drawings require extensive manual labor. This paper proposes an automated method for generating a comprehensive set of three‐view drawings, including the standardized labeling of doors and annotation of dimensions and areas. The output drawings are software‐readable and editable, and the method is applicable to intricate structures with non‐orthogonal or curved walls. To fully validate the accuracy of the proposed method, two distinct building scenarios were selected for experimentation. [ABSTRACT FROM AUTHOR]

When the data distributions between the source and target domains are inconsistent, unsupervised cross domain object detection improves the performance of the detector without the need to add any additional annotation information. However, existing advanced methods overlook the semantic consistency of images, which leads to incorrect transfer knowledge in feature learning and reduces adaptive performance. Therefore, we propose a feature correlation learning method, called Semantic Consistency Knowledge Transfer (SCKT) for unsupervised cross domain object detection, which focuses on similar semantic information and achieves fine-grained corresponding scales domain adaptation. Specifically, we design a Multi-channel Adaptive Correction (MAC) module for image enhancement, which alleviates pixel distortion and maintains semantic consistency. Furthermore, the proposed Related Knowledge Mining (RKM) module consists of Feature Correlation Learning (FCL) and Consistent Knowledge Learning (CKL), which use generated semantic consistency features to capture different levels of image details and semantic information, achieving corresponding domain alignment at multiple levels. Most importantly, SCKT does not rely on a specific network structure, and does not reduce inference speed, which is suitable for different object detectors. Extensive experiments show that our SCKT can exhibit optimal performance in multiple domain migration scenarios. Especially for the challenging PASCAL VOC → Clipart, SCKT generates 54.6% mAP. [ABSTRACT FROM AUTHOR]

Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotation methods is influenced by inductive bias, which refers to a set of assumptions that describe the behavior of model predictions. Different annotation methods possess distinct inductive biases, leading to varying degrees of generalizability and interpretability. Given that certain cell types exhibit unique functional patterns, we hypothesize that the inductive biases of cell annotation methods should align with these biological patterns to produce meaningful predictions. In this study, we propose KIDA, Knowledge-based Inductive bias and Domain Adaptation. The knowledge-based inductive bias constrains the prediction rules learned from the reference dataset, composed of multiple batches, to functional patterns relevant to biology, thereby enhancing the generalization of the model to unseen batches. Since the query dataset also contains gaps from multiple batches, KIDA's domain adaptation employs pseudo labels for self-knowledge distillation, effectively narrowing the distribution gap between model predictions and the query dataset. Benchmark experiments demonstrate that KIDA is capable of achieving accurate cross-batch cell type annotation. Knowledge-based inductive bias and domain adaptation can enhance the cell type annotation accuracy of deep learning models. [ABSTRACT FROM AUTHOR]

Incorporating medical text annotations compensates for the quality deficiencies of image data, effectively overcoming the limitations of medical image segmentation. Many existing approaches achieve high-quality segmentation results by integrating text into the image modality. However, these approaches require matched image-text pairs during inference to maintain their performance, and the absence of corresponding text annotations results in degraded model performance. Additionally, these methods often assume that the input text annotations are ideal, overlooking the impact of poor-quality text on model performance in practical scenarios. To address these issues, we propose a novel generative medical image segmentation model, Cap2Seg (Leveraging Caption Generation for Enhanced Segmentation of COVID-19 Medical Images). Cap2Seg not only segments lesion areas but also generates related medical text descriptions, guiding the segmentation process. This design enables the model to perform optimal segmentation without requiring text input during inference. To mitigate the impact of inaccurate text on model performance, we consider the consistency between generated textual features and visual features and introduce the Scale-aware Textual Attention Module (SATaM), which reduces the model's dependency on irrelevant or misleading text information. Subsequently, we design a word-pixel fusion decoding mechanism that effectively integrates textual features into visual features, ensuring that the text information effectively supplements and enhances the image segmentation task. Extensive experiments on two public datasets, MosMedData+ and QaTa-COV19, demonstrate that our method outperforms the current state-of-the-art models under the same conditions. Additionally, ablation studies have been conducted to demonstrate the effectiveness of each proposed module. The code is available at https://github.com/AllenZzzzzzzz/Cap2Seg. [ABSTRACT FROM AUTHOR]

This study explored the pupil's reading comprehension using the Preview, Question, Read, Reflect, Recite, and Review (PQ4R) and Annotations as a basis for an instructional tool. Grade six learners are struggling with reading comprehension, especially during the pandemic. It is pivotal that educational strategy and technique should be tested and explored to address the gap. This study used the PQ4R and annotations to help the learners understand the concepts and subject matter. This study utilized a sequential explanatory mixed-method research design. This design followed a sequence of gathering data from quantitative for analysis and interpretation to qualitative for exploration and triangulation. For the specific design of this method, a Quasi-experimental will be used to understand the numerical value of this study and a descriptive qualitative for the qualitative data. Purposive sampling design will be used in this study following criteria to identify the prospected respondents. There were 30 respondents from Tuble Elementary School. This study utilized a validated instrument. The findings of the study stipulated that the results of the experimental group (p-value = 0.28) and the control group (p-value = 0.34) established no significant difference. With this, teachers must explore more strategies and techniques in education so that learners can get the best learning experiences that they deserve. This will provide a reference on how teachers can create instructional tools to increase classroom productivity. [ABSTRACT FROM AUTHOR]

State-of-the-art instance segmentation approaches, such as Mask R-CNN, have exhibited remarkable performance under clear weather conditions. However, their effectiveness is significantly compromised in foggy environments, primarily due to reduced visibility and obscured object details. To address this challenge, we introduce a joint defogging learning with boundary refinement (JDLMask) framework. Unlike conventional strategies that treat image dehazing as a preprocessing step, JDLMask employs a shared structure that enables the joint learning of defogging and instance segmentation. This integrated approach greatly bolsters the model's adaptability to foggy scenarios. Recognizing challenges in feature extraction due to fog interference, we propose a multi-scale feature fusion mask head, based on the encoder–decoder architecture. This component is designed to acquire both local and global information, thereby enhancing the model's feature representation capacity. Furthermore, we integrate a boundary refinement module, which sharpens the model's localization accuracy by focusing on critical boundary details. Addressing the scarcity of datasets tailored, for instance, segmentation in real-world foggy scenes, we have enriched the Foggy Driving dataset with meticulously crafted instance mask annotations and named it the Foggy Driving InstanceSeg. Comprehensive experiments demonstrate JDLMask's superiority. Compared to the baseline Mask R-CNN, JDLMask achieves improvements of 4.4% and 3.8% in mask AP on the Foggy Cityscapes and Cityscapes validation sets, respectively, and a 2.5% gain on the Foggy Driving InstanceSeg dataset. [ABSTRACT FROM AUTHOR]

With the rapid development of depth sensor, more and more RGB-D videos could be obtained. Identifying the foreground in RGB-D videos is a fundamental and important task. However, the existing salient object detection (SOD) works only focus on either static RGB-D images or RGB videos, ignoring the collaborating of RGB-D and video information. In this paper, we first collect a new annotated RGB-D video SOD (ViDSOD-100) dataset, which contains 100 videos within a total of 9362 frames, acquired from diverse natural scenes. All the frames in each video are manually annotated to a high-quality saliency annotation. Moreover, we propose a new baseline model, named attentive triple-fusion network (ATF-Net), for RGB-D video salient object detection. Our method aggregates the appearance information from an input RGB image, spatio-temporal information from an estimated motion map, and the geometry information from the depth map by devising three modality-specific branches and a multi-modality integration branch. The modality-specific branches extract the representation of different inputs, while the multi-modality integration branch combines the multi-level modality-specific features by introducing the encoder feature aggregation (MEA) modules and decoder feature aggregation (MDA) modules. The experimental findings conducted on both our newly introduced ViDSOD-100 dataset and the well-established DAVSOD dataset highlight the superior performance of the proposed ATF-Net.This performance enhancement is demonstrated both quantitatively and qualitatively, surpassing the capabilities of current state-of-the-art techniques across various domains, including RGB-D saliency detection, video saliency detection, and video object segmentation. We shall release our data, our results, and our code upon the publication of this work. [ABSTRACT FROM AUTHOR]

Amplicon sequencing has long served as a robust method for characterising microbial communities, and despite inherent resolution limitations, it remains a preferred technique, offering cost‐ and time‐effective insights into bacterial compositions. Here, we introduce ONT‐AmpSeq, a user‐friendly pipeline designed for processing amplicon sequencing data generated from Oxford Nanopore Technology (ONT) devices. Our pipeline enables efficient creation of taxonomically annotated operational taxonomic unit (OTU) tables from ONT sequencing data, with the flexibility to multiplex amplicons on the same barcode. The pipeline encompasses six main steps—statistics, quality filtering, alignment, clustering, polishing, and taxonomic classification—integrating various state‐of‐the‐art software tools. We provide a detailed description of each step, along with performance tests and robustness evaluations using both test data and a ZymoBIOMICS® Microbial Community Standard mock community dataset. Our results demonstrate the ability of ONT‐AmpSeq to effectively process ONT amplicon data, offering valuable insights into microbial community composition. Additionally, we discuss the influence of polishing tools on taxonomic insight and the impact of taxonomic annotation methods on the derived microbial composition. Overall, ONT‐AmpSeq represents a comprehensive solution for analysing ONT amplicon sequencing data, facilitating streamlined and reliable microbial community analysis. The pipeline, along with test data, is freely available for public use. [ABSTRACT FROM AUTHOR]

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Accurate crack detection is essential for structural damage assessment after earthquake disasters. However, due to the gap between the target domain of the detected structure and the source domain, it is challenging to achieve high-precision crack segmentation when performing crack detection based on deep learning (DL) in actual engineering. This article proposes a crack segmentation transfer learning method based on a self-supervised learning mechanism and a high-quality pseudo-label generation method, which can significantly improve the detection accuracy in the target domain without pre-made annotations. Besides, to improve the crack segmentation model's ability to extract local and global features, this article proposes a SegCrackFormer model, which embeds convolutional layers and multi-head self-attention modules. An experiment of the crack segmentation transfer learning method is performed on two open-source crack datasets, METU and Crack500, and a newly proposed LD dataset. The experimental results show that the crack segmentation transfer learning method proposed in this article can improve the mean intersection over union (mIoU) by 38.41% and 15.66% on the Crack500 and LD datasets, respectively. The proposed SegCrackFormer is evaluated through comparative experiments, which demonstrate its superiority over existing crack segmentation models on the METU dataset. Additionally, the proposed method is shown to require significantly less computational resources than other existing models, which highlights the potential of SegCrackFormer as a powerful and efficient model for crack segmentation in practical applications. [ABSTRACT FROM AUTHOR]

Sparsely annotated image segmentation has gained popularity due to its ability to significantly reduce the labeling burden on training data. However, existing methods still struggle to learn complete object structures, especially for complex shadow objects. This paper discusses two prevalent issues existing in previous methods, i.e., generating noisy pseudo labels and misdetecting ambiguous regions. To tackle these challenges, we propose a novel weakly-supervised learning framework to boost sparsely annotated shadow detection. Concretely, a reliable label propagation (RLP) scheme is first designed to diffuse sparse annotations into unlabeled regions, thereby generating denser pseudo shadow masks. This scheme effectively reduces the number of noisy labels by incorporating uncertainty analysis. Then, a multi-cue semantic calibration (MSC) strategy is presented to refine the semantic features extracted from the backbone by employing edge, global, and adjacent priors. Embedded with MSC, the detection network becomes more discriminative against ambiguous regions. By combining RLP and MSC, the proposed weakly-supervised framework can detect complete and accurate shadow regions from sparse annotations. Experimental results on three benchmark datasets demonstrate that our method achieves comparable performance to recent fully-supervised methods, while requiring only about 4.5% of the pixels to be labeled. Boosting sparsely annotated shadow detection [ABSTRACT FROM AUTHOR]

This article presents a pipeline for automated fact-checking leveraging publicly available language models and data. The objective is to assess the accuracy of textual claims using evidence from a ground-truth evidence corpus. The pipeline consists of two main modules—the evidence retrieval and the claim veracity evaluation. Our primary focus is on the ease of deployment in various languages that remain unexplored in the field of automated fact-checking. Unlike most similar pipelines, which work with evidence sentences, our pipeline processes data on a paragraph level, simplifying the overall architecture and data requirements. Given the high cost of annotating language-specific fact-checking training data, our solution builds on the question answering for claim generation method, which we adapt and use to generate the data for all models of the pipeline. Our strategy enables the introduction of new languages through machine translation of only two fixed datasets of moderate size. Subsequently, any number of training samples can be generated based on an evidence corpus in the target language. We provide open access to all data and fine-tuned models for Czech, English, Polish, and Slovak pipelines, as well as to our codebase that may be used to reproduce the results. We comprehensively evaluate the pipelines for all four languages, including human annotations and per-sample difficulty assessment using Pointwise V -information. The presented experiments are based on full Wikipedia snapshots to promote reproducibility. To facilitate implementation and user interaction, we develop the FactSearch application featuring the proposed pipeline and the preliminary feedback on its performance. [ABSTRACT FROM AUTHOR]

Gesture recognition and its applications have been widely studied and received much attention in recent years. Existing works on hand gesture recognition aim to train classification models based on several discrete categories, which suffers from time-consuming data collection and low perceptual granularity. Differently, this work proposes a contrastive framework for fine-grained few-shot gesture recognition. To achieve this, we construct a general and compact gesture embedding space to represent arbitrary intricate hand gestures. The embedding distance between hand gestures is consistent with their similarity, reflecting their subtle variations accurately. To learn such an embedding space, we build up a large-scale hand gesture similarity dataset named SimGesture, relying on 944,482 hand image triplets of gesture comparison annotations. Based on SimGesture, we utilize contrastive learning to train a neural network named SimGesNet being capable of projecting arbitrary hand images into a compact gesture embedding space. Our experimental results demonstrate that the learned embedding can be used with great success for few-shot gesture recognition and achieves SOTA results. We also show that our proposed gesture embedding outperforms existing embeddings in representing fine-grained gestures. [ABSTRACT FROM AUTHOR]

Genome annotation is an important but challenging task. Accurate identification of short interspersed nuclear elements (SINEs) is particularly difficult due to their lack of highly conserved sequences. AnnoSINE is state-of-the-art software for annotating SINEs in plant genomes, but it is computationally inefficient for large genomes. Moreover, its applicability to animals is limited due to the absence of animal pHMMs in its HMM library. Therefore, we propose AnnoSINE_v2, which extends accurate SINE annotation for animal genomes with greatly optimized computational efficiency. Our results show that AnnoSINE_v2's annotation of SINEs has over 20% higher F1-score compared to the existing tools on animal genomes and enables the processing of complicated genomes, like human and zebrafish, which were beyond the capabilities of AnnoSINE_v1. AnnoSINE_v2 is freely available on Conda and GitHub: https://github.com/liaoherui/AnnoSINE_v2. [ABSTRACT FROM AUTHOR]

Generating pixel-level annotations for semantic segmentation tasks of high-resolution remote sensing images is both time-consuming and labor-intensive, which has led to increased interest in unsupervised methods. Therefore, in this paper, we propose an unsupervised multi-scale hybrid feature extraction network based on the CNN-Transformer architecture, referred to as MSHFE-Net. The MSHFE-Net consists of three main modules: a Multi-Scale Pixel-Guided CNN Encoder, a Multi-Scale Aggregation Transformer Encoder, and a Parallel Attention Fusion Module. The Multi-Scale Pixel-Guided CNN Encoder is designed for multi-scale, fine-grained feature extraction in unsupervised tasks, efficiently recovering local spatial information in images. Meanwhile, the Multi-Scale Aggregation Transformer Encoder introduces a multi-scale aggregation module, which further enhances the unsupervised acquisition of multi-scale contextual information, obtaining global features with stronger feature representation. The Parallel Attention Fusion Module employs an attention mechanism to fuse global and local features in both channel and spatial dimensions in parallel, enriching the semantic relations extracted during unsupervised training and improving the performance of unsupervised semantic segmentation. K-means clustering is then performed on the fused features to achieve high-precision unsupervised semantic segmentation. Experiments with MSHFE-Net on the Potsdam and Vaihingen datasets demonstrate its effectiveness in significantly improving the accuracy of unsupervised semantic segmentation. [ABSTRACT FROM AUTHOR]

Electric bicycle datasets for lift control have played a crucial role in the development of electric bicycle lift entry detection algorithms. However, existing datasets often encounter issues, including a small dataset size, a high false detection rate, and the absence of a publicly available benchmark dataset. This paper addresses these challenges by constructing a new electric bicycle detection dataset for lift control based on the research demands of the electric bicycle detection task for lift control. XHNet_EB, a car scene image dataset covering various types of electric bicycles, was constructed based on images taken by a camera from above. Segmented annotation was employed, framing the front and rear of the electric bicycle independently. This approach effectively addressed the problems of many irrelevant features and a high false detection rate caused by the use of whole-electric-bicycle annotations in mainstream datasets. AP_50, AP_75, the mAP, and the number of false detections in the images were used for quantitative analysis of the dataset. The experimental results indicated that the object detection accuracy of the XHNet_EB dataset was excellent. Quantitative analysis and evaluation of the number of false detections in images were conducted using four mainstream lightweight detection model algorithms. The results demonstrated that segmented annotation reduced the false detection rate more effectively than entire electric bicycle annotation. This study identified drawbacks in existing datasets. The dataset proposed in this paper overcomes the shortcomings of existing commercial data and solves problems such as the high false detection rate caused by the inclusion of many irrelevant features caused by the "whole-electric-bicycle annotation" method, which could help with the development of electric bicycle detection applications in lifts. [ABSTRACT FROM AUTHOR]

In this rapidly evolving era of multimodal generation, diffusion models exhibit impressive generative capabilities, significantly enhancing the realm of creative image synthesis by intricately textual prompts. Yet, their effectiveness is limited in certain niche sectors, like depicting Chinese ancient architecture. This limitation is primarily due to the insufficient data that fails to encompass the unique architectural features and corresponding text information. Hence, we build an extensive multimodal dataset capturing the essence of Chinese architectures mostly from the Tang to the Yuan Dynasties. The dataset is categorized on the types, including image&text, video, and style models. In details, images and videos are methodically categorized based on locations. All images are annotated at two levels: initial annotations and descriptive terms based on distinctive characteristics and official information. Moreover, seven artistic styles fine-tuning models are provided in our dataset for further innovations. Significantly, this is the first Chinese ancient architecture dataset and the instance of using the Pinyin system to annotate unique terms related to Chinese architectural styles. [ABSTRACT FROM AUTHOR]

Motivation Recent advances in sequencing technology provide opportunities to study biological processes at a higher resolution. Cell type annotation is an important step in scRNA-seq analysis, which often relies on established marker genes. However, most of the previous methods divide the identification of cell types into two stages, clustering and assignment, whose performances are susceptible to the clustering algorithm, and the marker information cannot effectively guide the clustering process. Furthermore, their linear heuristic-based cell assignment process is often insufficient to capture potential dependencies between cells and types. Results Here, we present I nterpretable C ell T ype A nnotation based on s elf-training (sICTA), a marker-based cell type annotation method that combines the self-training strategy with pseudo-labeling and the nonlinear association capturing capability of Transformer. In addition, we incorporate biological priori knowledge of genes and pathways into the classifier through an attention mechanism to enhance the transparency of the model. A benchmark analysis on 11 publicly available single-cell datasets demonstrates the superiority of sICTA compared to state-of-the-art methods. The robustness of our method is further validated by evaluating the prediction accuracy of the model on different cell types for each single-cell data. Moreover, ablation studies show that self-training and the ability to capture potential dependencies between cells and cell types, both of which are mutually reinforcing, work together to improve model performance. Finally, we apply sICTA to the pancreatic dataset, exemplifying the interpretable attention matrix captured by sICTA. Availability and implementation The source code of sICTA is available in public at https://github.com/nbnbhwyy/sICTA. The processed datasets can be found at https://drive.google.com/drive/folders/1jbqSxacL%5fIDIZ4uPjq220C9Kv024m9eL. The final version of the model will be permanently available at https://doi.org/10.5281/zenodo.13474010 [ABSTRACT FROM AUTHOR]

Tarikh-e Beyhaghi (Beyhaghi’s History) is a seminal work in Persian literature and historiography. Fayyaz dedicated his life to correcting and improving this text. In 2009, a revised edition of the work edited by Yahagghi and Seyyedi was published. The primary advantage of this new edition was the detailed introduction, annotations, and supplementary materials, which greatly facilitated the work of readers and researchers. However, the editors had made numerous unjustified interventions in the text, to the extent that this benefit was largely overshadowed by the distortion of the original work of such great significance. It was anticipated that after more than a decade since the initial publication and appearance of numerous scholarly articles on the topic, many of the problems would have been addressed in the new edition. Regrettably, the 2022 edition, which claimed to incorporate "complete revision, additions, and corrections", failed to live up to these expectations as only a few of the numerous issues were rectified. Due to the impracticality of addressing all the flaws of this editorial effort in a single article, the present work examined select problems under the following headings: "Departure from the normative contextual meaning", "Disregard for Bayhaghi's stylistic features", "Ignoring the frequency of evidence", "Unjustified textual changes", "Unsupported readings of the text", and "Neglect of literary sensibilities in text correction". [ABSTRACT FROM AUTHOR]

Recent advancements in neural machine translation (NMT) offer promising potential for generating cross-language clinical natural language processing (NLP) resources. There is a pressing need to be able to foster the development of clinical NLP tools that extract key clinical entities in a comparable way for a multitude of medical application scenarios that are hindered by lack of multilingual annotated data. This study explores the efficacy of using NMT and annotation projection techniques with expert-in-the-loop validation to develop named entity recognition (NER) systems for an under-resourced target language (Catalan) by leveraging Spanish clinical corpora annotated by domain experts. We employed a state-of-the-art NMT system to translate three clinical case corpora. The translated annotations were then projected onto the target language texts and subsequently validated and corrected by clinical domain experts. The efficacy of the resulting NER systems was evaluated against manually annotated test sets in the target language. Our findings indicate that this approach not only facilitates the generation of high-quality training data for the target language (Catalan) but also demonstrates the potential to extend this methodology to other languages, thereby enhancing multilingual clinical NLP resource development. The generated corpora and components are publicly accessible, potentially providing a valuable resource for further research and application in multilingual clinical settings. [ABSTRACT FROM AUTHOR]

This article proposes a weakly supervised multi-feature fusion pedestrian re-identification method, which introduces a multi-feature fusion mechanism to extract feature information from different layers into the same feature space and fuse them into the deep and shallow joint features. The goal is to fully utilize the rich information in the image and improve the performance and robustness of the pedestrian re-identification model. Secondly, by matching the target character with unprocessed surveillance videos, one only needs to know that the identity of a person appears in the video, without annotating the identity of a person in any of the frames of the video during the training process. This simplifies the annotation of training images by replacing accurate annotations with broad annotations; that is, it puts the pedestrian identities that appeared in the video in one package and assigns a video-level label to each package. This greatly reduces the annotation work and transforms this weakly supervised pedestrian re-identification challenge into a multi-instance and multi-label learning problem. The experimental results show that the method proposed in this paper is effective and can significantly improve mAP. [ABSTRACT FROM AUTHOR]

Interactive image segmentation extremely accelerates the generation of high-quality annotation image datasets, which are the pillars of the applications of deep learning. However, these methods suffer from the insignificance of interaction information and excessively high optimization costs, resulting in unexpected segmentation outcomes and increased computational burden. To address these issues, this paper focuses on interactive information mining from the network architecture and optimization procedure. In terms of network architecture, the issue mentioned above arises from two perspectives: the less representative feature of interactive regions in each layer and the interactive information weakened by the network hierarchy structure. Therefore, the paper proposes a network called EnNet. The network addresses the two aforementioned issues by employing attention mechanisms to integrate user interaction information across the entire image and incorporating interaction information twice in a design that progresses from coarse to fine. In terms of optimization, this paper proposes a method of using zero-order optimization during the first four iterations of training. This approach can reduce computational overhead with only a minimal reduction in accuracy. The experimental results on GrabCut, Berkeley, DAVIS, and SBD datasets validate the effectiveness of the proposed method, with our approach achieving an average NOC@90 that surpasses RITM by 0.35. [ABSTRACT FROM AUTHOR]

How to effectively leverage the plentiful existing datasets to train a robust and high-performance model is of great significance for many practical applications. However, a model trained on a naive merge of different datasets tends to obtain poor performance due to annotation conflicts and domain divergence. In this paper, we attempt to train a unified model that is expected to perform well across domains on several popularity segmentation datasets. We conduct a comprehensive analysis to assess the impact of various training schemes and model selection on multi-domain learning with extensive experiments. Based on the analysis, we propose a robust solution that consistently enhances the model performance across different domains. Our solution ranks 2nd on RVC 2022 semantic segmentation task, with a dataset only 1/3 size of the 1st model used. [ABSTRACT FROM AUTHOR]

Markerless methods for animal posture tracking have been rapidly developing recently, but frameworks and benchmarks for tracking large animal groups in 3D are still lacking. To overcome this gap in the literature, we present 3D-MuPPET, a framework to estimate and track 3D poses of up to 10 pigeons at interactive speed using multiple camera views. We train a pose estimator to infer 2D keypoints and bounding boxes of multiple pigeons, then triangulate the keypoints to 3D. For identity matching of individuals in all views, we first dynamically match 2D detections to global identities in the first frame, then use a 2D tracker to maintain IDs across views in subsequent frames. We achieve comparable accuracy to a state of the art 3D pose estimator in terms of median error and Percentage of Correct Keypoints. Additionally, we benchmark the inference speed of 3D-MuPPET, with up to 9.45 fps in 2D and 1.89 fps in 3D, and perform quantitative tracking evaluation, which yields encouraging results. Finally, we showcase two novel applications for 3D-MuPPET. First, we train a model with data of single pigeons and achieve comparable results in 2D and 3D posture estimation for up to 5 pigeons. Second, we show that 3D-MuPPET also works in outdoors without additional annotations from natural environments. Both use cases simplify the domain shift to new species and environments, largely reducing annotation effort needed for 3D posture tracking. To the best of our knowledge we are the first to present a framework for 2D/3D animal posture and trajectory tracking that works in both indoor and outdoor environments for up to 10 individuals. We hope that the framework can open up new opportunities in studying animal collective behaviour and encourages further developments in 3D multi-animal posture tracking. [ABSTRACT FROM AUTHOR]

Shadow detection is essential for scene understanding and image restoration. Existing paradigms for producing shadow detection training data usually rely on densely labeling each image pixel, which will lead to a bottleneck when scaling up the number of images. To tackle this problem, by labeling shadow images with only a few strokes, this paper designs a learning framework for Weakly supervised Shadow Detection, namely WSD. Firstly, it creates two shadow detection datasets with scribble annotations, namely Scr-SBU and Scr-ISTD. Secondly, it proposes an uncertainty-guided label augmentation scheme based on graph convolutional networks, which can propagate the sparse scribble annotations to more reliable regions, and then avoid the model converging to an undesired local minima as intra-class discontinuity. Finally, it introduces a multi-task learning framework to jointly learn for shadow detection and edge detection, which encourages generated shadow maps to be comprehensive and well aligned with shadow boundaries. Experimental results on benchmark datasets demonstrate that our framework even outperforms existing semi-supervised and fully supervised shadow detectors requiring only 2% pixels to be labeled. [ABSTRACT FROM AUTHOR]

Gene-based burden tests are a popular and powerful approach for analysis of exome-wide association studies. These approaches combine sets of variants within a gene into a single burden score that is then tested for association. Typically, a range of burden scores are calculated and tested across a range of annotation classes and frequency bins. Correlation between these tests can complicate the multiple testing correction and hamper interpretation of the results. We introduce a method called the sparse burden association test (SBAT) that tests the joint set of burden scores under the assumption that causal burden scores act in the same effect direction. The method simultaneously assesses the significance of the model fit and selects the set of burden scores that best explain the association at the same time. Using simulated data, we show that the method is well calibrated and highlight scenarios where the test outperforms existing gene-based tests. We apply the method to 73 quantitative traits from the UK Biobank, showing that SBAT is a valuable additional gene-based test when combined with other existing approaches. This test is implemented in the REGENIE software. Gene-based burden tests are commonly used in exome-wide association studies. We introduce SBAT (sparse burden association test), which jointly models a set of burden scores under the assumption that the causal burden scores act in the same effect direction. We apply SBAT to 73 quantitative traits in the UK Biobank. [ABSTRACT FROM AUTHOR]

The current paradigm of joint detection and tracking still requires a large amount of instance-level trajectory annotation, which incurs high annotation costs. Moreover, treating embedding training as a classification problem would lead to difficulties in model fitting. In this paper, we propose a new self-supervised multi-object tracking based on the real-time joint detection and embedding (JDE) framework, which we termed as self-supervised multi-object tracking (SS-MOT). In SS-MOT, the short-term temporal correlations between objects within and across adjacent video frames are both considered as self-supervised constraints, where the distances between different objects are enlarged while the distances between same object of adjacent frames are brought closer. In addition, short trajectories are formed by matching pairs of adjacent frames using a matching algorithm, and these matched pairs are treated as positive samples. The distances between positive samples are then minimized for futher the feature representation of the same object. Therefore, our method can be trained on videos without instance-level annotations. We apply our approach to state-of-the-art JDE models, such as FairMOT, Cstrack, and SiamMOT, and achieve comparable results to these supevised methods on the widely used MOT17 and MOT20 challenges. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) plays a crucial role in various applications, including human–computer interaction and affective computing. However, the joint training of an FER network with multiple datasets is a promising strategy to enhance its performance. Nevertheless, widespread annotation inconsistencies and class imbalances among FER datasets pose significant challenges to this approach. This paper proposes a new multi-dataset joint training method, Sample Selection and Paired Augmentation Joint Training (SSPA-JT), to address these challenges. SSPA-JT models annotation inconsistency as a label noise problem and selects clean samples from auxiliary datasets to expand the overall dataset size while maintaining consistent annotation standards. Additionally, a dynamic matching algorithm is developed to pair clean samples of the tail class with noisy samples, which enriches the tail classes with diverse background information. Experimental results demonstrate that SSPA-JT achieved superior or comparable performance compared with the existing methods by addressing both annotation inconsistencies and class imbalance during multi-dataset joint training. It achieved state-of-the-art performance on RAF-DB and CAER-S datasets with accuracies of 92.44% and 98.22%, respectively, reflecting improvements of 0.2% and 3.65% over existing methods. [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) is considered an important subtask in information extraction that aims to identify Named Entities (NM) within a given text and classify them into predefined categories (e.g., person, location, organization, and miscellaneous). The use of an appropriate annotation scheme is crucial to label multi-word NEs and enhance recognition performance. This study investigates the effects of using different annotation schemes on NER systems for the Arabic language. The impact of seven annotation schemes, namely IO, IOB, IOE, IOBE, IOBS, IOES, and IOBES, on Arabic NER is examined by applying conditional random fields, multinomial Naive Bayes, and support vector machine classifiers. The experimental results reveal the importance of selecting an optimal annotation scheme and show that annotating NEs based on the simple IO scheme yields a higher performance in terms of precision, recall, and F-measure compared to the other schemes. [ABSTRACT FROM AUTHOR]

Despite most state-of-the-art models having achieved amazing performance in Visual Question Answering (VQA), they usually utilize biases to answer the question. Recently, some studies synthesize counterfactual training samples to help the model to mitigate the biases. However, these synthetic samples need extra annotations and often contain noises. Moreover, these methods simply add synthetic samples to the training data to train the model with the cross-entropy loss, which cannot make the best use of synthetic samples to mitigate the biases. In this article, to mitigate the biases in VQA more effectively, we propose a Hierarchical Counterfactual Contrastive Learning (HCCL) method. Firstly, to avoid introducing noises and extra annotations, our method automatically masks the unimportant features in original pairs to obtain positive samples and create mismatched question-image pairs as negative samples. Then our method uses feature-level and answer-level contrastive learning to make the original sample close to positive samples in the feature space, while away from negative samples in both feature and answer spaces. In this way, the VQA model can learn the robust multimodal features and focus on both visual and language information to produce the answer. Our HCCL method can be adopted in different baselines, and the experimental results on VQA v2, VQA-CP, and GQA-OOD datasets show that our method is effective in mitigating the biases in VQA, which improves the robustness of the VQA model. [ABSTRACT FROM AUTHOR]

Text-to-Video Retrieval is a typical cross-modal retrieval task that has been studied extensively under a conventional supervised setting. Recently, some works have sought to extend the problem to a weakly supervised formulation, which can be more consistent with real-life scenarios and more efficient in annotation cost. In this context, a new task called Partially Relevant Video Retrieval (PRVR) is proposed, which aims to retrieve videos that are partially relevant to a given textual query, i.e., the videos containing at least one semantically relevant moment. Formulating the task as a Multiple Instance Learning (MIL) ranking problem, prior arts rely on heuristics algorithms such as a simple greedy search strategy and deal with each query independently. Although these early explorations have achieved decent performance, they may not fully utilize the bag-level label and only consider the local optimum, which could result in suboptimal solutions and inferior final retrieval performance. To address this problem, in this paper, we propose to exploit the relationships between instances to boost retrieval performance. Based on this idea, we creatively put forward: (1) a new matching scheme for pairing queries and their related moments in the video; and (2) a new loss function to facilitate cross-modal alignment between two views of an instance. Extensive validations on three publicly available datasets have demonstrated the effectiveness of our solution and verified our hypothesis that modeling instance-level relationships is beneficial in the MIL ranking setting. Our code will be publicly available at https://github.com/xjtupanda/BGM-Net. [ABSTRACT FROM AUTHOR]

Segmentation of structural components in infrastructure inspection images is crucial for automated and accurate condition assessment. While deep neural networks hold great potential for this task, existing methods typically require fully annotated ground truth masks, which are time‐consuming and labor‐intensive to create. This paper introduces Scribble‐supervised Structural Component Segmentation Network (ScribCompNet), the first weakly‐supervised method requiring only scribble annotations for multiclass structural component segmentation. ScribCompNet features a dual‐branch architecture with higher‐resolution refinement to enhance fine detail detection. It extends supervision from labeled to unlabeled pixels through a combined objective function, incorporating scribble annotation, dynamic pseudo label, semantic context enhancement, and scale‐adaptive harmony losses. Experimental results show that ScribCompNet outperforms other scribble‐supervised methods and most fully‐supervised counterparts, achieving 90.19% mean intersection over union (mIoU) with an 80% reduction in labeling time. Further evaluations confirm the effectiveness of the novel designs and robust performance, even with lower‐quality scribble annotations. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of VLMs enable new opportunities for the annotation and analysis of images. We test the potential of VLMs for landscape scenicness prediction, i.e., the aesthetic quality of a landscape, using zero- and few-shot methods. We experiment with few-shot learning by fine-tuning a single linear layer on a pre-trained VLM representation. We find that a model fitted to just a few hundred samples performs favourably compared to a model trained on hundreds of thousands of examples in a fully supervised way. We also explore the zero-shot prediction potential of contrastive prompting using positive and negative landscape aesthetic concepts. Our results show that this method outperforms a linear probe with few-shot learning when using a small number of samples to tune the prompt configuration. We introduce Landscape Prompt Ensembling (LPE), which is an annotation method for acquiring landscape scenicness ratings through rated text descriptions without needing an image dataset during annotation. We demonstrate that LPE can provide landscape scenicness assessments that are concordant with a dataset of image ratings. The success of zero- and few-shot methods combined with their ability to use text-based annotations highlights the potential for VLMs to provide efficient landscape scenicness assessments with greater flexibility. [ABSTRACT FROM AUTHOR]

AbstractThis mixed-methods study investigates the effects of highlights and annotations on reading comprehension among Chinese EFL students majoring in teaching English as a foreign language, within a computer-assisted interactive reading model. The study involved 183 participants, randomly assigned to three experimental groups and a control group, each utilizing distinct combinations of highlights and annotations to evaluate their impact on reading comprehension. The results indicate that the intervention groups exhibited significant improvements in reading comprehension compared to the control group. Qualitative feedback from participants revealed increased engagement and metacognitive awareness, suggesting that these interactive features function as effective cognitive scaffolds. By facilitating information processing and reducing cognitive load, highlights and annotations enhance the reading experience and comprehension. These findings support cognitive load theory and underscore the pedagogical potential of integrating interactive elements into digital reading materials. This integration can promote active reading strategies and learner-centered instruction, offering valuable insights for educators and curriculum designers. By demonstrating the efficacy of technology-enhanced reading interventions, this research contributes to the broader field of Computer-Assisted Language Learning (CALL), emphasizing the role of interactive tools in improving language proficiency and academic success among EFL learners. [ABSTRACT FROM AUTHOR]

For taxonomy based classification of metagenomics assembled contigs, current methods use sequence similarity to identify their most likely taxonomy. However, in the related field of metagenomic binning, contigs are routinely clustered using information from both the contig sequences and their abundance. We introduce Taxometer, a neural network based method that improves the annotations and estimates the quality of any taxonomic classifier using contig abundance profiles and tetra-nucleotide frequencies. We apply Taxometer to five short-read CAMI2 datasets and find that it increases the average share of correct species-level contig annotations of the MMSeqs2 tool from 66.6% to 86.2%. Additionally, it reduce the share of wrong species-level annotations in the CAMI2 Rhizosphere dataset by an average of two-fold for Metabuli, Centrifuge, and Kraken2. Futhermore, we use Taxometer for benchmarking taxonomic classifiers on two complex long-read metagenomics data sets where ground truth is not known. Taxometer is available as open-source software and can enhance any taxonomic annotation of metagenomic contigs. Taxonomic classification of DNA sequences is typically performed on each sequence individually. Here the authors present a deep learning-based method that utilizes information across a dataset to enhance taxonomic annotations of any microbiome. [ABSTRACT FROM AUTHOR]

Temporal action localization (TAL) is one of the most important tasks in video understanding. Weakly supervised temporal action localization (WTAL) involves classifying and localizing all the action instances in untrimmed videos under the supervision of only video-level category labels, which is a challenging task because of the absence of frame-level annotations. In this study, first, we review the development process of the WTAL task in recent years, summarize and analyze the main problems of WTAL. Second, we classify and compare the research approaches of existing models and thoroughly discuss methods based on multiple instance learning (MIL), feature erasing, the attention mechanism, similarity propagation, pseudo-ground truth generation, contrastive learning, and adversarial learning. Then, we present the datasets and evaluation criteria for the WTAL task. Finally, we discuss the main application areas and further developments in WTAL. [ABSTRACT FROM AUTHOR]

With the rapid advancement of intelligent manufacturing technologies, the operating environments of modern robotic arms are becoming increasingly complex. In addition to the diversity of objects, there is often a high degree of similarity between the foreground and the background. Although traditional RGB-based object-detection models have achieved remarkable success in many fields, they still face the challenge of effectively detecting targets with textures similar to the background. To address this issue, we introduce the WoodenCube dataset, which contains over 5000 images of 10 different types of blocks. All images are densely annotated with object-level categories, bounding boxes, and rotation angles. Additionally, a new evaluation metric, Cube-mAP, is proposed to more accurately assess the detection performance of cube-like objects. In addition, we have developed a simple, yet effective, framework for WoodenCube, termed CS-SKNet, which captures strong texture features in the scene by enlarging the network's receptive field. The experimental results indicate that our CS-SKNet achieves the best performance on the WoodenCube dataset, as evaluated by the Cube-mAP metric. We further evaluate the CS-SKNet on the challenging DOTAv1.0 dataset, with the consistent enhancement demonstrating its strong generalization capability. [ABSTRACT FROM AUTHOR]

Object detectors are used for searching all objects belonging to a pre-defined set of categories contained in a given picture. However, users are often not interested in finding all objects, but only those that pertain to a small set of categories or concepts. Nowadays, the standard approach to solve this task involves initially employing an object detector to identify all objects within the image, followed by refining the outcomes to retain only the ones of interest. Nevertheless, the object detector does not take advantage of the user's prior intent that, when used, can potentially improve the detection performance of the model. This work presents a method to condition an existing object detector with the user's intent, encoded as one or more concepts from the WordNet graph, to find just those objects of interest. The proposed approach takes advantage of existing datasets for object detection without the need for new annotations, and it allows to adapt the already existing object detector models with minor changes. The evaluation, performed on the COCO and the Visual Genome datasets considering several object detector architectures, shows that conditioning the search on concepts is actually beneficial. The code and the pre-trained model weights are released at: https://github.com/drigoni/Concept-Conditioned-Object-Detector. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) aims to solve the lack of annotation in a new dataset which has non-independent identity distribution compare with training data. It has the potential to help the annotation process in medical image segmentation. Existing self-training based UDA approaches utilize the pseudo labels as ground truth labels for domain adaptation, whereas the generated pseudo labels inevitably introducing the noise when training the model for the target domain, which make the training process unstable and the model is difficult to converge. In the meanwhile, most of the methods ignore the class imbalanced problem. To tackle the issue, we propose a class-aware multi-stage unsupervised domain adaptation framework for prostate zonal segmentation task. We devise a class-specific knowledge guidance strategy for training a better pseudo labels generation model. Extensive experimental results show the effectiveness of our approach against existing state-of-the-art approaches on the UDA problem of prostate zonal segmentation benchmark. [ABSTRACT FROM AUTHOR]

Deformable image registration (DIR) plays an important part in many clinical tasks, and deep learning has made significant progress in DIR over the past few years.To propose a fast multiscale unsupervised deformable image registration (referred to as FMIRNet) method for monomodal image registration.We designed a multiscale fusion module to estimate the large displacement field by combining and refining the deformation fields of three scales. The spatial attention mechanism was employed in our fusion module to weight the displacement field pixel by pixel. Except mean square error (MSE), we additionally added structural similarity (ssim) measure during the training phase to enhance the structural consistency between the deformed images and the fixed images.Our registration method was evaluated on EchoNet, CHAOS and SLIVER, and had indeed performance improvement in terms of SSIM, NCC and NMI scores. Furthermore, we integrated the FMIRNet into the segmentation network (FCN, UNet) to boost the segmentation task on a dataset with few manual annotations in our joint leaning frameworks. The experimental results indicated that the joint segmentation methods had performance improvement in terms of Dice, HD and ASSD scores.Our proposed FMIRNet is effective for large deformation estimation, and its registration capability is generalizable and robust in joint registration and segmentation frameworks to generate reliable labels for training segmentation tasks. [ABSTRACT FROM AUTHOR]

Giovanni Battista Piranesi's enchanting images are often also word-image composites that demand significant work from their readers. "The Digital Piranesi" animates the referential links that Piranesi forges within pages and across volumes. The digital environment offers a way of experiencing his works that library collections can rarely offer but that his original publications summoned. Building a digital repository requires a fresh perspective on print collections; such perspectives as they emerge through a careful study of Piranesi's works stand to enhance understandings of literary Romanticism's own uses of different media—the page the book and print itself. [ABSTRACT FROM AUTHOR]

This article aims to explore the application of 3D vision techniques in the automation of logistical processes, using deep learning for object identification and manipulation in industrial environments. The work develops a 3D vision system that employs object and keypoint detection models trained with tools such as Roboflow and YOLOv8 (You Only Look Once version 8). The methodology includes data collection and annotation, development of deep learning models, and analysis of the obtained results. The models demonstrated high precision and recall in the block and keypoint identification, with a slight reduction in keypoint model accuracy due to variability in annotations. Integrating the models presented computational challenges, but the combined approach proved effective in precise detection. Limitations include the need for resource optimization and improvement in annotation processes. In addition, the accuracy obtained is due to the fact that the object detection system was trained with large amounts of data to provide this high accuracy. The model was trained and evaluated based on measures of average precision (mAP) and recovery, obtaining a mAP of 98.3%, a precision of 96.4% and a recall of 95.6%. [ABSTRACT FROM AUTHOR]

Summary The burgeoning high-throughput technologies have led to a significant surge in the scale of pharmacotranscriptomic datasets, especially for oncology. Signature search methods (SSMs), utilizing oncogenic signatures formed by differentially expressed genes through sequencing, have been instrumental in anti-cancer drug screening and identifying mechanisms of action without relying on prior knowledge. However, various studies have found that different SSMs exhibit varying performance across pharmacotranscriptomic datasets. In addition, the size of the oncogenic signature can also significantly impact the result of drug repurposing. Therefore, finding the optimal SSMs and customized oncogenic signature for a specific disease remains a challenge. To address this, we introduce Signature Search Polestar (SSP), a webserver integrating the largest pharmacotranscriptomic datasets of anti-cancer drugs from LINCS L1000 with five state-of-the-art SSMs (XSum, CMap, GSEA, ZhangScore, XCos). SSP provides three main modules: Benchmark, Robustness, and Application. Benchmark uses two indices, Area Under the Curve and Enrichment Score, based on drug annotations to evaluate SSMs at different oncogenic signature sizes. Robustness, applicable when drug annotations are insufficient, uses a performance score based on drug self-retrieval for evaluation. Application provides three screening strategies, single method, SS_all, and SS_cross, allowing users to freely utilize optimal SSMs with tailored oncogenic signature for drug repurposing. Availability and implementation SSP is free at https://web.biotcm.net/SSP/. The current version of SSP is archived in https://doi.org/10.6084/m9.figshare.26524741.v1 , allowing users to directly use or customize their own SSP webserver. [ABSTRACT FROM AUTHOR]

Summary Identification of allelic or corresponding genes (pan-genes) within a species or genus is important for discovery of biologically significant genetic conservation and variation. Similarly, identification of orthologs (gene families) across wider evolutionary distances is important for understanding the genetic basis for similar or differing traits. Especially in plants, several complications make identification of pan-genes and gene families challenging, including whole-genome duplications, evolutionary rate differences among lineages, and varying qualities of assemblies and annotations. Here, we document and distribute a set of workflows that we have used to address these problems. Results Pandagma is a set of configurable workflows for identifying and comparing pan-gene sets and gene families for annotation sets from eukaryotic genomes, using a combination of homology, synteny, and expected rates of synonymous change in coding sequence. Availability and implementation The Pandagma workflows, example configurations, implementation details, and scripts for retrieving public datasets, are available at https://github.com/legumeinfo/pandagma [ABSTRACT FROM AUTHOR]

Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods. [ABSTRACT FROM AUTHOR]

Recent advances in single-cell technologies enable the rapid growth of multi-omics data. Cell type annotation is one common task in analyzing single-cell data. It is a challenge that some cell types in the testing set are not present in the training set (i.e. unknown cell types). Most scATAC-seq cell type annotation methods generally assign each cell in the testing set to one known type in the training set but neglect unknown cell types. Here, we present OVAAnno, an automatic cell types annotation method which utilizes open-set domain adaptation to detect unknown cell types in scATAC-seq data. Comprehensive experiments show that OVAAnno successfully identifies known and unknown cell types. Further experiments demonstrate that OVAAnno also performs well on scRNA-seq data. Our codes are available online at https://github.com/lisaber/OVAAnno/tree/master. [ABSTRACT FROM AUTHOR]

The annotation of enzyme function is a fundamental challenge in industrial biotechnology and pathologies. Numerous computational methods have been proposed to predict enzyme function by annotating enzyme labels with Enzyme Commission number. However, the existing methods face difficulties in modelling the hierarchical structure of enzyme label in a global view. Moreover, they haven't gone entirely to leverage the mutual interactions between different levels of enzyme label. In this paper, we formulate the hierarchy of enzyme label as a directed enzyme graph and propose a hierarchy-GCN (Graph Convolutional Network) encoder to globally model enzyme label dependency on the enzyme graph. Based on the enzyme hierarchy encoder, we develop an end-to-end hierarchical-aware global model named GloEC to predict enzyme function. GloEC learns hierarchical-aware enzyme label embeddings via the hierarchy-GCN encoder and conducts deductive fusion of label-aware enzyme features to predict enzyme labels. Meanwhile, our hierarchy-GCN encoder is designed to bidirectionally compute to investigate the enzyme label correlation information in both bottom-up and top-down manners, which has not been explored in enzyme function prediction. Comparative experiments on three benchmark datasets show that GloEC achieves better predictive performance as compared to the existing methods. The case studies also demonstrate that GloEC is capable of effectively predicting the function of isoenzyme. GloEC is available at: https://github.com/hyr0771/GloEC. [ABSTRACT FROM AUTHOR]

In this paper, we examine three important issues in the practical use of state‐of‐the‐art facial landmark detectors and show how a combination of specific architectural modifications can directly improve their accuracy and temporal stability. First, many facial landmark detectors require a face normalization step as a pre‐process, often accomplished by a separately trained neural network that crops and resizes the face in the input image. There is no guarantee that this pre‐trained network performs optimal face normalization for the task of landmark detection. Thus, we instead analyse the use of a spatial transformer network that is trained alongside the landmark detector in an unsupervised manner, jointly learning an optimal face normalization and landmark detection by a single neural network. Second, we show that modifying the output head of the landmark predictor to infer landmarks in a canonical 3D space rather than directly in 2D can further improve accuracy. To convert the predicted 3D landmarks into screen‐space, we additionally predict the camera intrinsics and head pose from the input image. As a side benefit, this allows to predict the 3D face shape from a given image only using 2D landmarks as supervision, which is useful in determining landmark visibility among other things. Third, when training a landmark detector on multiple datasets at the same time, annotation inconsistencies across datasets forces the network to produce a sub‐optimal average. We propose to add a semantic correction network to address this issue. This additional lightweight neural network is trained alongside the landmark detector, without requiring any additional supervision. While the insights of this paper can be applied to most common landmark detectors, we specifically target a recently proposed continuous 2D landmark detector to demonstrate how each of our additions leads to meaningful improvements over the state‐of‐the‐art on standard benchmarks. [ABSTRACT FROM AUTHOR]

We propose a method for inferring the internal anisotropic volumetric texture of a given wood block from annotated photographs of its external surfaces. The global structure of the annual ring pattern is represented using a continuous spatial scalar field referred to as the growth time field (GTF). First, we train a generic neural model that can represent various GTFs using procedurally generated training data. Next, we fit the generic model to the GTF of a given wood block based on surface annotations. Finally, we convert the GTF to an annual ring field (ARF) revealing the layered pattern and apply neural style transfer to render orientation‐dependent small‐scale features and colors on a cut surface. We show rendered results of various physically cut real wood samples. Our method has physical and virtual applications such as cut‐preview before subtractive fabricating solid wood artifacts and simulating object breaking. [ABSTRACT FROM AUTHOR]