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The advent of deep learning has provided solutions to many challenges posed by the Internet. However, efficient localization and recognition of vulgar segments within videos remain formidable tasks. This difficulty arises from the blurring of spatial features in vulgar actions, which can render them indistinguishable from general actions. Furthermore, issues of boundary ambiguity and over-segmentation complicate the segmentation of vulgar actions. To address these issues, we present the Boundary-Match U-shaped Temporal Convolutional Network (BMUTCN), a novel approach for the segmentation of vulgar actions. The BMUTCN employs a U-shaped architecture within an encoder–decoder temporal convolutional network to bolster feature recognition by leveraging the context of the video. Additionally, we introduce a boundary-match map that fuses action boundary inform ation with greater precision for frames that exhibit ambiguous boundaries. Moreover, we propose an adaptive internal block suppression technique, which substantially mitigates over-segmentation errors while preserving accuracy. Our methodology, tested across several public datasets as well as a bespoke vulgar dataset, has demonstrated state-of-the-art performance on the latter. [ABSTRACT FROM AUTHOR]

The recurrent network architecture is a widely used model in sequence modeling, but its serial dependency hinders the computation parallelization, which makes the operation inefficient. The same problem was encountered in serial adder at the early stage of digital electronics. In this paper, we discuss the similarities between recurrent neural network (RNN) and serial adder. Inspired by carry-lookahead adder, we introduce carry-lookahead module to RNN, which makes it possible for RNN to run in parallel. Then, we design the method of parallel RNN computation, and finally Carry-lookahead RNN (CL-RNN) is proposed. CL-RNN takes advantages in parallelism and flexible receptive field. Through a comprehensive set of tests, we verify that CL-RNN can perform better than existing typical RNNs in sequence modeling tasks which are specially designed for RNNs. Code and models are available at: https://github.com/WinnieJiangHW/Carry-lookahead_RNN. [ABSTRACT FROM AUTHOR]

Traditional clinical diagnostic aid systems for medical images are facing challenges of reliability and interpretability. Artificial intelligence has the potential to bring driving changes to disease diagnosis methods through rapid traversal of medical images and efficient classification. However, the application of artificial intelligence in the field of medical image still faces challenges. Our method combines the multiple modalities of attention which consider the most discriminative part in the images. The proposed classification method is tested on the microscopic image dataset with 40 leukocyte categories, which achieves top-1 accuracy of 84.21% and top-5 accuracy of 99.44% during the testing procedure. And experiments on the dermoscopic image dataset show that our method has good generalization ability across multiple imaging modalities. [ABSTRACT FROM AUTHOR]

We present a novel active learning approach for shape cosegmentation based on graph convolutional networks (GCNs). The premise of our approach is to represent the collections of three-dimensional shapes as graph-structured data, where each node in the graph corresponds to a primitive patch of an oversegmented shape, and is associated with a representation initialized by extracting features. Then, the GCN operates directly on the graph to update the representation of each node based on a layer-wise propagation rule, which aggregates information from its neighbors, and predicts the labels for unlabeled nodes. Additionally, we further suggest an active learning strategy that queries the most informative samples to extend the initial training samples of GCN to generate more accurate predictions of our method. Our experimental results on the Shape COSEG dataset demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The necessary step in the diagnosis of leukemia by the attending physician is to classify the white blood cells in the bone marrow, which requires the attending physician to have a wealth of clinical experience. Now the deep learning is very suitable for the study of image recognition classification, and the effect is not good enough to directly use some famous convolution neural network (CNN) models, such as AlexNet model, GoogleNet model, and VGGFace model. In this paper, we construct a new CNN model called WBCNet model that can fully extract features of the microscopic white blood cell image by combining batch normalization algorithm, residual convolution architecture, and improved activation function. WBCNet model has 33 layers of network architecture, whose speed has greatly been improved compared with the traditional CNN model in training period, and it can quickly identify the category of white blood cell images. The accuracy rate is 77.65% for Top-1 and 98.65% for Top-5 on the training set, while 83% for Top-1 on the test set. This study can help doctors diagnose leukemia, and reduce misdiagnosis rate. [ABSTRACT FROM AUTHOR]

Background and objective: Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past development of machine learning techniques, for example, extracting distinctive features from raw microscopic images are difficult, the widely used SVM classifier only has relative few parameters to tune, these methods cannot efficiently handle fine-grained classification cases when the white blood cells have up to 40 categories. Methods: Based on deep learning theory, a systematic study is conducted on finer leukocyte classification in this paper. A deep residual neural network based leukocyte classifier is constructed at first, which can imitate the domain expert’s cell recognition process, and extract salient features robustly and automatically. Then the deep neural network classifier’s topology is adjusted according to the prior knowledge of white blood cell test. After that the microscopic image dataset with almost one hundred thousand labeled leukocytes belonging to 40 categories is built, and combined training strategies are adopted to make the designed classifier has good generalization ability. Results: The proposed deep residual neural network based classifier was tested on microscopic image dataset with 40 leukocyte categories. It achieves top-1 accuracy of 77.80%, top-5 accuracy of 98.75% during the training procedure. The average accuracy on the test set is nearly 76.84%. Conclusions: This paper presents a fine-grained leukocyte classification method for microscopic images, based on deep residual learning theory and medical domain knowledge. Experimental results validate the feasibility and effectiveness of our approach. Extended experiments support that the fine-grained leukocyte classifier could be used in real medical applications, assist doctors in diagnosing diseases, reduce human power significantly. [ABSTRACT FROM AUTHOR]

How to quickly, accurately retrieve and effectively reuse 3D CAD models that conform to user's design intention has become an urgent problem in product design. However, there are several problems with the existing retrieval methods, like not being fast, or accurate, or hard to use. Hence it is difficult to meet the actual needs of the industry. In this paper, we propose a 3D CAD model retrieval approach that considers the speed, accuracy and ease of use at the same time, based on sketches and unsupervised learning. Firstly, the loop is used as the fundamental element of sketch/view, and automatic structural semantics capture algorithms are proposed to extract and construct attributed loop relation tree; Secondly, the recursive neural network based deep variational autoencoders is constructed and optimized to transform arbitrary shapes and sizes of loop relation tree into fixed length descriptor; Finally, based on the fixed length vector descriptor, the sketches and views of 3D CAD models are embedded into the same target feature space, and k -nearest neighbors algorithm is adopted to conduct fast CAD model matching on the feature space. In this manner, a prototype 3D CAD model retrieval system is developed. Experiments on the dataset containing about two thousand 3D CAD models validate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Occlusion is unavoidable in real-world applications of person re-identification (ReID). To alleviate the occlusion problem, this work proposes the detection of the occluded and visible regions of the human body by suppressing the occluded region during feature generation and matching, and enhancing the significance of the visible region. This paper introduces a novel method based on pose-guided spatial attention (PGSA) and activation-based attention (AA) called dual-attention re-identification (DAReID). DAReID consists of a mask branch and a global branch and uses ResNet-50 as the backbone network. The mask branch uses PGSA to obtain the visible and occluded regions of a person and constructs pose guided coarse labels for the occluded region through keypoints of the human body, driving the network to obtain robust local features. The global branch obtains the visual activation levels of different regions through AA, and combines this with human pose information to define weighted local distances(WLD). The WLD learning strategy is applied to drive the network to learn new and more discriminative local features. Experimental results show that DAReID achieves comparable performance on the Market1501, DukeMTMC-reID, and CUHK-03 datasets. And on the Occluded-DukeMTMC dataset, DAReID outperforms the existing methods. [ABSTRACT FROM AUTHOR]

Most previous methods of bas-relief generation run slow, or require tuning several important parameters. These issues seriously reduce the efficiency of bas-relief modeling. We introduce a fast generation method for high-quality bas-reliefs from 3D objects based on a deep learning technique. Unlike neural networks for image tasks, the proposed network for reliefs (ReliefNet) is elaborately designed to deal with a modeling problem in the field of graphics. We design our ReliefNet and equip it with a special loss function with the aim that the network can solve the essential problem of bas-relief modeling. Our network eliminates the height gaps and maintains the rich details simultaneously. The advantage over previous methods is that our method does not require parameter tuning and is a very efficient. Once the ReliefNet has been trained, a bas-relief can be produced by one feed-forward pass of the network instantly. To demonstrate the performance and effectiveness of our method, extensive experiments on a range of 3D scenes with high resolutions and comparisons to state-of-the-art methods are conducted. • The first end-to-end trained deep neural network for relief modeling from 3D scenes. • The proposed loss function is critical for the essential problem of relief modeling. • Our network makes it very efficient to create high-quality relief from a 3D scene. • A relief dataset is first constructed to train the neural network. [ABSTRACT FROM AUTHOR]