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Due to the fast retrieval speed and low storage cost, cross-modal hashing has become the primary method for cross-modal retrieval. Since the emergence of deep cross-modal hashing methods, cross-modal retrieval significantly improved. However, the existing cross-modal hash retrieval methods still need to effectively utilize the dataset's supervisory information and the lack of similarity expression ability. This means that the label information needs to be maximized, and the potential semantic relationship between two modalities cannot be fully explored, thus affecting the judgment of semantic similarity between two modalities. To address these problems, this paper proposes Tri-CMH, a cross-modal hash retrieval method with fused triples, which is an end-to-end modeling framework consisting of two parts: feature extraction and hash learning. Firstly, the multi-modal data are preprocessing into the form of triple groups. The data supervision matrix is constructed so that the samples with labels and their meanings are aggregated together. In contrast, the samples with labels and their opposite meanings are separated, thus avoiding the problem of the under-utilization of supervisory information in the data set and achieving the effect of efficiently utilizing the global supervisory information. Meanwhile, the loss function of the hash learning part is optimized by considering the Hamming distance loss, single-modality internal loss, cross-modality loss, and quantization loss to explicitly constrain semantically similar hash codes and semantically dissimilar hash codes and to improve the model's ability to judge cross-modality semantic similarity. The method is trained and tested on the IAPR-TC12, MIRFLICKR-25K, and NUS-WIDE datasets, and the experimental evaluation criteria are mAP and PR curve, and the experimental results show the effectiveness and practicality of the method. [ABSTRACT FROM AUTHOR]

Skeleton-based action recognition, renowned for its computational efficiency and indifference to lighting variations, has become a focal point in the realm of motion analysis. However, most current methods typically only extract global skeleton features, overlooking the potential semantic relationships among various partial limb motions. For instance, the subtle differences between actions such as "brush teeth" and "brush hair" are mainly distinguished by specific elements. Although combining limb movements provides a more holistic representation of an action, relying solely on skeleton points proves inadequate for capturing these nuances. Therefore, integrating detailed linguistic descriptions into the learning process of skeleton features is essential. This motivates us to explore integrating fine-grained language descriptions into the learning process of skeleton features to capture more discriminative skeleton behavior representations. To this end, we introduce a new Linguistic-Driven Partial Semantic Relevance Learning framework (LPSR) in this work. While using state-of-the-art large language models to generate linguistic descriptions of local limb motions and further constrain the learning of local motions, we also aggregate global skeleton point representations and textual representations (which generated from an LLM) to obtain a more generalized cross-modal behavioral representation. On this basis, we propose a cyclic attentional interaction module to model the implicit correlations between partial limb motions. Numerous ablation experiments demonstrate the effectiveness of the method proposed in this paper, and our method also obtains state-of-the-art results. [ABSTRACT FROM AUTHOR]

Tomato leaf disease control in the field of smart agriculture urgently requires attention and reinforcement. This paper proposes a method called LAFANet for image-text retrieval, which integrates image and text information for joint analysis of multimodal data, helping agricultural practitioners to provide more comprehensive and in-depth diagnostic evidence to ensure the quality and yield of tomatoes. First, we focus on six common tomato leaf disease images and text descriptions, creating a Tomato Leaf Disease Image-Text Retrieval Dataset (TLDITRD), introducing image-text retrieval into the field of tomato leaf disease retrieval. Then, utilizing ViT and BERT models, we extract detailed image features and sequences of textual features, incorporating contextual information from image-text pairs. To address errors in image-text retrieval caused by complex backgrounds, we propose Learnable Fusion Attention (LFA) to amplify the fusion of textual and image features, thereby extracting substantial semantic insights from both modalities. To delve further into the semantic connections across various modalities, we propose a False Negative Elimination-Adversarial Negative Selection (FNE-ANS) approach. This method aims to identify adversarial negative instances that specifically target false negatives within the triplet function, thereby imposing constraints on the model. To bolster the model's capacity for generalization and precision, we propose Adversarial Regularization (AR). This approach involves incorporating adversarial perturbations during model training, thereby fortifying its resilience and adaptability to slight variations in input data. Experimental results show that, compared with existing ultramodern models, LAFANet outperformed existing models on TLDITRD dataset, with top1, top5, and top10 reaching 83.3% and 90.0%, and top1, top5, and top10 reaching 80.3%, 93.7%, and 96.3%. LAFANet offers fresh technical backing and algorithmic insights for the retrieval of tomato leaf disease through image-text correlation. [ABSTRACT FROM AUTHOR]

The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has progressed by using acoustic and semantic information as input and adopting a classification method to identify the person's ID and emotion for driving co-speech gesture generation. However, this endeavor still faces significant challenges. These challenges go beyond the intricate interplay among co-speech gestures, speech acoustic, and semantics; they also encompass the complexities associated with personality, emotion, and other obscure but important factors. This paper introduces "DiT-Gestures", a speech-conditional diffusion-based and non-autoregressive transformer-based generative model with the WavLM pre-trained model and a dynamic mask attention network (DMAN). It can produce individual and stylized full-body co-speech gestures by only using raw speech audio, eliminating the need for complex multimodal processing and manual annotation. Firstly, considering that speech audio contains acoustic and semantic features and conveys personality traits, emotions, and more subtle information related to accompanying gestures, we pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract the style from raw audio information. Secondly, we replace the causal mask by introducing a learnable dynamic mask for better local modeling in the neighborhood of the target frames. Extensive subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT datasets to confirm WavLM's and the model's ability to synthesize natural co-speech gestures with various styles. [ABSTRACT FROM AUTHOR]

Historically, cross-modal plasticity following early blindness has been largely studied in the context of visual deprivation. However, more recently, there has been a shift in focus towards understanding cross-modal plasticity from the perspective of skill acquisition: the striking plasticity observed in early blind individuals reflects the extraordinary perceptual and cognitive challenges they solve. Here, inspired by two seminal papers on skill learning (the "cortical recycling" theory) and cross-modal plasticity (the "metamodal" hypothesis) respectively, we present a unified hypothesis of cortical specialization that describes how shared functional, algorithmic, and structural constraints might mediate both types of plasticity. [ABSTRACT FROM AUTHOR]

The goal of RGB-D salient object detection is to aggregate the information of the two modalities of RGB and depth to accurately detect and segment salient objects. Existing RGB-D SOD models can extract the multilevel features of single modality well and can also integrate cross-modal features, but it can rarely handle both at the same time. To tap into and make the most of the correlations of intra- and inter-modality information, in this paper, we proposed an attention-guided cross-modal multi-feature aggregation network for RGB-D SOD. Our motivation was that both cross-modal feature fusion and multilevel feature fusion are crucial for RGB-D SOD task. The main innovation of this work lies in two points: One is the cross-modal pyramid feature interaction (CPFI) module that integrates multilevel features from both RGB and depth modalities in a bottom-up manner, and the other is cross-modal feature decoder (CMFD) that aggregates the fused features to generate the final saliency map. Extensive experiments on six benchmark datasets showed that the proposed attention-guided cross-modal multiple feature aggregation network (ACFPA-Net) achieved competitive performance over 15 state of the art (SOTA) RGB-D SOD methods, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB–RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB–D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB–RGB or Visible-IR scenarios, there are none that consider both RGB–D and RGB–IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB–D and RGB–IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB–D and RGB–IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems. [ABSTRACT FROM AUTHOR]

Biomedical image registration refers to aligning corresponding anatomical structures among different images, which is critical tomany tasks, such as brain atlas building, tumor growth monitoring, and image fusion-based medical diagnosis. However, high-throughput biomedical image registration remains challenging due to inherent variations in the intensity, texture, and anatomy resulting from different imaging modalities, different sample preparation methods, or different developmental stages of the imaged subject. Recently, Generative Adversarial Networks (GAN) have attracted increasing interest in both mono- and cross-modal biomedical image registrations due to their special ability to eliminate the modal variance and their adversarial training strategy. This paper provides a comprehensive survey of the GAN-based mono- and cross-modal biomedical image registration methods. According to the different implementation strategies, we organize the GAN-basedmono- and cross-modal biomedical image registration methods into four categories: modality translation, symmetric learning, adversarial strategies, and joint training. The key concepts, the main contributions, and the advantages and disadvantages of the different strategies are summarized and discussed. Finally, we analyze the statistics of all the cited works from different points of view and reveal future trends for GAN-based biomedical image registration studies. [ABSTRACT FROM AUTHOR]

We consider the problem of referring segmentation in images and videos with natural language. Given an input image (or video) and a referring expression, the goal is to segment the entity referred by the expression in the image or video. In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the visual input. We further propose a gated multi-level fusion (GMLF) module to selectively integrate self-attentive cross-modal features corresponding to different levels of visual features. This module controls the feature fusion of information flow of features at different levels with high-level and low-level semantic information related to different attentive words. Besides, we introduce cross-frame self-attention (CFSA) module to effectively integrate temporal information in consecutive frames which extends our method in the case of referring segmentation in videos. Experiments on benchmark datasets of four referring image datasets and two actor and action video segmentation datasets consistently demonstrate that our proposed approach outperforms existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Fat and sugar-reduced foods and beverages have become increasingly popular for a variety of reasons, mainly relating to health and wellbeing. Depending on the food or beverage, it may be difficult to reduce the fat and/or sugar content and still maintain optimal sensory properties for the specific product. One way of approaching the problem is to gain a better understanding of how a product is affected by a reduction in fat and/or sugar. This paper aims to investigate the sensory interactions between fat and sweetness perception in a chocolate-flavored milk beverage by using a descriptive analysis with a trained sensory panel. The reduction of fat significantly reduced the sweetness intensity of the chocolate milk, while the reduction of sucrose significantly decreased the cream flavor and the fruity and lactic flavor. The perception of acesulfame-K was affected by fat concentration, similarly to sucrose. These results highlight the importance of considering the effects of reducing either sugar and fat on product attributes that are not directly related to the sugar or fat. [ABSTRACT FROM AUTHOR]

Cross-Modal Hashing (CMH) retrieval methods have garnered increasing attention within the information retrieval research community due to their capability to deal with large amounts of data thanks to the computational efficiency of hash-based methods. To date, the focus of cross-modal hashing methods has been on training with paired data. Paired data refers to samples with one-to-one correspondence across modalities, e.g., image and text pairs where the text sample describes the image. However, real-world applications produce unpaired data that cannot be utilised by most current CMH methods during the training process. Models that can learn from unpaired data are crucial for real-world applications such as cross-modal neural information retrieval where paired data is limited or not available to train the model. This paper provides (1) an overview of the CMH methods when applied to unpaired datasets, (2) proposes a framework that enables pairwise-constrained CMH methods to train with unpaired samples, and (3) evaluates the performance of state-of-the-art CMH methods across different pairing scenarios. [ABSTRACT FROM AUTHOR]

With the ever-increasing urbanization process, modeling people's spatiotemporal activities from their online traces has become a crucial task. State-of-the-art methods for this task rely on cross-modal embedding, which maps items from different modalities (e.g., location, time, text) into the same latent space. Despite their inspiring results, existing cross-modal embedding methods merely capture co-occurrences between items without modeling their high-order interactions. In this paper, we first construct two graphs from raw data records to represent the user interaction graph layer and activity graph layer and propose a hierarchical cross-modal embedding method that takes the high-order relationships into consideration. The key notion behind our method is a novel hierarchical embedding framework with meta-graphs connecting different layers. We introduce both inter-record and intra-record meta-graph structures, which enable learning distributed representations that preserve high-order proximities across graphs from different layers. Our empirical experiments on three real-world datasets demonstrate that our method not only outperforms state-of-the-art methods for spatiotemporal activity prediction, but also captures cross-modal proximity at a finer granularity. [ABSTRACT FROM AUTHOR]

Visual-semantic embedding (VSE) networks create joint image-text representations to map images and texts in a shared embedding space to enable various information retrieval-related tasks, such as image-text retrieval, image captioning, and visual question answering. The most recent state-of-the-art VSE-based networks are: VSE++, SCAN, VSRN, and UNITER. This study evaluates the performance of those VSE networks for the task of image-to-text retrieval and identifies and analyses their strengths and limitations to guide future research on the topic. The experimental results on Flickr30K revealed that the pre-trained network, UNITER, achieved 61.5% on average Recall@5 for the task of retrieving all relevant descriptions. The traditional networks, VSRN, SCAN, and VSE++, achieved 50.3%, 47.1%, and 29.4% on average Recall@5, respectively, for the same task. An additional analysis was performed on image-text pairs from the top 25 worst-performing classes using a subset of the Flickr30K-based dataset to identify the limitations of the performance of the best-performing models, VSRN and UNITER. These limitations are discussed from the perspective of image scenes, image objects, image semantics, and basic functions of neural networks. This paper discusses the strengths and limitations of VSE networks to guide further research into the topic of using VSE networks for cross-modal information retrieval tasks. [ABSTRACT FROM AUTHOR]

The incorporation of thermal imaging data in RGB-T images has demonstrated its usefulness in cross-modal crowd counting by offering complementary information to RGB representations. Despite achieving satisfactory results in RGB-T crowd counting, many existing methods still face two significant limitations: (1) The oversight of the heterogeneous gap between modalities complicates the effective integration of multimodal features. (2) The absence of mining consistency hinders the full exploitation of the unique complementary strengths inherent in each modality. To this end, we present C4-MIM, a novel Consistency-constrained RGB-T Crowd Counting approach via Mutual Information Maximization. It effectively leverages multimodal information by learning the consistency between the RGB and thermal modalities, thereby enhancing the performance of cross-modal counting. Specifically, we first advocate extracting feature representations of different modalities in a shared encoder to moderate the heterogeneous gap since they obey the identical coding rules with shared parameters. Then, we intend to mine the consistent information of different modalities to better learn conducive information and improve the performance of feature representations. To this end, we formulate the complementarity of multimodality representations as a mutual information maximization regularizer to maximize the consistent information of different modalities, in which the consistency would be maximally attained before combining the multimodal information. Finally, we simply aggregate the feature representations of the different modalities and send them into a regressor to output the density maps. The proposed approach can be implemented by arbitrary backbone networks and is quite robust in the face of single modality unavailable or serious compromised. Extensively experiments have been conducted on the RGBT-CC and DroneRGBT benchmarks to evaluate the effectiveness and robustness of the proposed approach, demonstrating its superior performance compared to the SOTA approaches. [ABSTRACT FROM AUTHOR]

Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB–RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB–D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB–RGB or Visible-IR scenarios, there are none that consider both RGB–D and RGB–IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB–D and RGB–IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB–D and RGB–IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems.

Fine-grained representation is fundamental to species classification based on deep learning, and in this context, cross-modal contrastive learning is an effective method. The diversity of species coupled with the inherent contextual ambiguity of natural language poses a primary challenge in the cross-modal representation alignment of conservation area image data. Integrating cross-modal retrieval tasks with generation tasks contributes to cross-modal representation alignment based on contextual understanding. However, during the contrastive learning process, apart from learning the differences in the data itself, a pair of encoders inevitably learns the differences caused by encoder fluctuations. The latter leads to convergence shortcuts, resulting in poor representation quality and an inaccurate reflection of the similarity relationships between samples in the original dataset within the shared space of features. To achieve fine-grained cross-modal representation alignment, we first propose a residual attention network to enhance consistency during momentum updates in cross-modal encoders. Building upon this, we propose momentum encoding from a multi-task perspective as a bridge for cross-modal information, effectively improving cross-modal mutual information, representation quality, and optimizing the distribution of feature points within the cross-modal shared semantic space. By acquiring momentum encoding queues for cross-modal semantic understanding through multi-tasking, we align ambiguous natural language representations around the invariant image features of factual information, alleviating contextual ambiguity and enhancing model robustness. Experimental validation shows that our proposed multi-task perspective of cross-modal momentum encoders outperforms similar models on standardized image classification tasks and image–text cross-modal retrieval tasks on public datasets by up to 8% on the leaderboard, demonstrating the effectiveness of the proposed method. Qualitative experiments on our self-built conservation area image–text paired dataset show that our proposed method accurately performs cross-modal retrieval and generation tasks among 8142 species, proving its effectiveness on fine-grained cross-modal image–text conservation area image datasets. [ABSTRACT FROM AUTHOR]

In this study, we adopt a monolingual, comparable, and cross-modal approach to investigate the use of hedges in interpreted and non-interpreted English speeches. To achieve this, we compile a corpus comprising speech data sourced from the interpreter-mediated Chinese government press conferences and the White House press conferences, respectively. Two research questions are formulated to guide our examination of hedging-related features in the corpus: (1) How does the use of hedges differ between interpreted English produced in different modes of interpreting (i.e., consecutive and simultaneous)? (2) How does the use of hedges in interpreted English differ from non-interpreted English? The results of our study show that interpreters, regardless of the consecutive and simultaneous mode, tend to employ significantly fewer hedges than spontaneous speakers of English. In addition, a vast majority of hedges in interpreted English stem from a relatively narrow range of core hedging devices. We posit that the underuse of hedges in interpreted English may be linked to constraints such as high cognitive pressure, rigid interactive structure, and source-language-related speech rhetoric conventions. Preliminary findings also suggest that the interpreting mode appears to exert little influence on the hedging patterns in the interpreters' renditions. Potential factors underlying such a trend are briefly discussed. [ABSTRACT FROM AUTHOR]

Early diagnosis and intervention of depression promote complete recovery, with its traditional clinical assessments depending on the diagnostic scales, clinical experience of doctors and patient cooperation. Recent researches indicate that functional near-infrared spectroscopy (fNIRS) based on deep learning provides a promising approach to depression diagnosis. However, collecting large fNIRS datasets within a standard experimental paradigm remains challenging, limiting the applications of deep networks that require more data. To address these challenges, in this paper, we propose an fNIRS-driven depression recognition architecture based on cross-modal data augmentation (fCMDA), which converts fNIRS data into pseudo-sequence activation images. The approach incorporates a time-domain augmentation mechanism, including time warping and time masking, to generate diverse data. Additionally, we design a stimulation task-driven data pseudo-sequence method to map fNIRS data into pseudo-sequence activation images, facilitating the extraction of spatial-temporal, contextual and dynamic characteristics. Ultimately, we construct a depression recognition model based on deep classification networks using the imbalance loss function. Extensive experiments are performed on the two-class depression diagnosis and five-class depression severity recognition, which reveal impressive results with accuracy of 0.905 and 0.889, respectively. The fCMDA architecture provides a novel solution for effective depression recognition with limited data.

The goal of RGB-D salient object detection is to aggregate the information of the two modalities of RGB and depth to accurately detect and segment salient objects. Existing RGB-D SOD models can extract the multilevel features of single modality well and can also integrate cross-modal features, but it can rarely handle both at the same time. To tap into and make the most of the correlations of intra- and inter-modality information, in this paper, we proposed an attention-guided cross-modal multi-feature aggregation network for RGB-D SOD. Our motivation was that both cross-modal feature fusion and multilevel feature fusion are crucial for RGB-D SOD task. The main innovation of this work lies in two points: One is the cross-modal pyramid feature interaction (CPFI) module that integrates multilevel features from both RGB and depth modalities in a bottom-up manner, and the other is cross-modal feature decoder (CMFD) that aggregates the fused features to generate the final saliency map. Extensive experiments on six benchmark datasets showed that the proposed attention-guided cross-modal multiple feature aggregation network (ACFPA-Net) achieved competitive performance over 15 state of the art (SOTA) RGB-D SOD methods, both qualitatively and quantitatively.

Visible and infrared image fusion is an important image enhancement technique that aims to generate high-quality fused images with prominent targets and rich textures in extreme environments. However, most of the current image fusion methods have poor visual perception of the generated fused images due to severe degradation of texture details of the visible light images in scenes with extreme lighting, which seriously affects the application of subsequent advanced vision tasks such as target detection and tracking. To address these challenges, this paper bridges the gap between image fusion and advanced vision tasks by proposing an efficient fusion method for mutual enhancement of spatial and radiometric information. First, we design the gradient residual dense block (LGCnet) to improve the description of fine spatial details in the fusion network. Then, we developed a cross-modal perceptual fusion (CMPF) module to facilitate modal interactions in the network, which effectively enhances the fusion of complementary information between different modalities and reduces redundant learning. Finally, we designed an adaptive light-aware network (ALPnet) to guide the training of the fusion network to facilitate the fusion network to adaptively select more effective information for fusion under different lighting conditions. Extensive experiments show that the proposed fusion approach has competitive advantages over six current state-of-the-art deep-learning methods in highlighting target features and describing the global scene.

Neuroimaging studies suggest cross-sensory visual influences in human auditory cortices (ACs). Whether these influences reflect active visual processing in human ACs, which drives neuronal firing and concurrent broadband high-frequency activity (BHFA; >70 Hz), or whether they merely modulate sound processing is still debatable. Here, we presented auditory, visual, and audiovisual stimuli to 16 participants (7 women, 9 men) with stereo-EEG depth electrodes implanted near ACs for presurgical monitoring. Anatomically normalized group analyses were facilitated by inverse modeling of intracranial source currents. Analyses of intracranial event-related potentials (iERPs) suggested cross-sensory responses to visual stimuli in ACs, which lagged the earliest auditory responses by several tens of milliseconds. Visual stimuli also modulated the phase of intrinsic low-frequency oscillations and triggered 15–30 Hz event-related desynchronization in ACs. However, BHFA, a putative correlate of neuronal firing, was not significantly increased in ACs after visual stimuli, not even when they coincided with auditory stimuli. Intracranial recordings demonstrate cross-sensory modulations, but no indication of active visual processing in human ACs. [ABSTRACT FROM AUTHOR]

At present, Domain generalization for semantic segmentation relying on deep neural networks has made little progress. Most of the current methods are mainly divided into domain randomization, standardization, and whitening. We propose a novel approach to achieve domain generalization for semantic segmentation: leveraging cross-modal information to supervise the model training and improve the generalization ability of the network. We align visual features with textual features in a subspace and enhance the contrast between categories. Our method enables the network to learn rich semantic knowledge from text features and clearer category boundaries. Our experiments also prove that our method can effectively improve the generalization ability of the network. We are the first to exploit multi-modal information for domain-generalized semantic segmentation. [ABSTRACT FROM AUTHOR]

Skeleton-based action recognition, renowned for its computational efficiency and indifference to lighting variations, has become a focal point in the realm of motion analysis. However, most current methods typically only extract global skeleton features, overlooking the potential semantic relationships among various partial limb motions. For instance, the subtle differences between actions such as “brush teeth” and “brush hair” are mainly distinguished by specific elements. Although combining limb movements provides a more holistic representation of an action, relying solely on skeleton points proves inadequate for capturing these nuances. Therefore, integrating detailed linguistic descriptions into the learning process of skeleton features is essential. This motivates us to explore integrating fine-grained language descriptions into the learning process of skeleton features to capture more discriminative skeleton behavior representations. To this end, we introduce a new Linguistic-Driven Partial Semantic Relevance Learning framework (LPSR) in this work. While using state-of-the-art large language models to generate linguistic descriptions of local limb motions and further constrain the learning of local motions, we also aggregate global skeleton point representations and textual representations (which generated from an LLM) to obtain a more generalized cross-modal behavioral representation. On this basis, we propose a cyclic attentional interaction module to model the implicit correlations between partial limb motions. Numerous ablation experiments demonstrate the effectiveness of the method proposed in this paper, and our method also obtains state-of-the-art results.

To probe the presence of mirror neurons in the human brain, cross-modal fMRI adaptation has been suggested as a suitable technique. The rationale behind this suggestion is that this technique allows making more accurate inferences about neural response properties underlying fMRI voxel activations, beyond merely showing shared voxels that are active during both action observation and execution. However, the validity of using cross-modal fMRI adaptation to demonstrate the presence of mirror neurons in parietal and premotor brain regions has been questioned given the inconsistent and weak results obtained in human studies. A better understanding of cross-modal fMRI adaptation effects in the macaque brain is required as the rationale for using this approach is based on several assumptions related to macaque mirror neuron response properties that still need validation. Here, we conducted a cross-modal fMRI adaptation study in macaque monkeys, using the same action execution and action observation tasks that successfully yielded mirror neuron region cross-modal action decoding in a previous monkey MVPA study. We scanned two male rhesus monkeys while they first executed a sequence of either reach-and-grasp or reach-and-touch hand actions and then observed a video of a human actor performing these motor acts. Both whole-brain and region-of-interest analyses failed to demonstrate cross-modal fMRI adaptation effects in parietal and premotor mirror neuron regions. Our results, in line with previous findings in non-human primates, show that cross-modal motor-to-visual fMRI adaptation is not easily detected in monkey brain regions known to house mirror neurons. Thus, our results advocate caution in using cross-modal fMRI adaptation as a method to infer whether mirror neurons can be found in the primate brain. [ABSTRACT FROM AUTHOR]

Cross-modal medical imaging techniques are predominantly being used in the clinical suite. The ensemble learning methods using cross-modal medical imaging adds reliability to several medical image analysis tasks. Motivated by the performance of deep learning in several medical imaging tasks, a deep learning-based denoising method Cross-Modality Guided Denoising Network C M G D N e t for removing Rician noise in T1-weighted (T1-w) Magnetic Resonance Images (MRI) is proposed in this paper. C M G D N e t uses a guidance image, which is a cross-modal (T2-w) image of better perceptual quality to guide the model in denoising its noisy T1-w counterpart. This cross-modal combination allows the network to exploit complementary information existing in both images and therefore improve the learning capability of the model. The proposed framework consists of two components: Paired Hierarchical Learning (PHL) module and Cross-Modal Assisted Reconstruction (CMAR) module. PHL module uses Siamese network to extract hierarchical features from dual images, which are then combined in a densely connected manner in the CMAR module to finally reconstruct the image. The impact of using registered guidance data is investigated in removing noise as well as retaining structural similarity with the original image. Several experiments were conducted on two publicly available brain imaging datasets available on the IXI database. The quantitative assessment using Peak Signal to noise ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) demonstrates that the proposed method exhibits 4.7% and 2.3% gain (average), respectively, in SSIM and FSIM values compared to other state-of-the-art denoising methods that do not integrate cross-modal image information in removing various levels of noise. [ABSTRACT FROM AUTHOR]

It has been postulated that the brain is organized by “metamodal,” sensory-independent cortical modules capable of performing tasks (e.g., word recognition) in both “standard” and novel sensory modalities. Still, this theory has primarily been tested in sensory-deprived individuals, with mixed evidence in neurotypical subjects, thereby limiting its support as a general principle of brain organization. Critically, current theories of metamodal processing do not specify requirements for successful metamodal processing at the level of neural representations. Specification at this level may be particularly important in neurotypical individuals, where novel sensory modalities must interface with existing representations for the standard sense. Here we hypothesized that effective metamodal engagement of a cortical area requires congruence between stimulus representations in the standard and novel sensory modalities in that region. To test this, we first used fMRI to identify bilateral auditory speech representations. We then trained 20 human participants (12 female) to recognize vibrotactile versions of auditory words using one of two auditory-to-vibrotactile algorithms. The vocoded algorithm attempted to match the encoding scheme of auditory speech while the token-based algorithm did not. Crucially, using fMRI, we found that only in the vocoded group did trained-vibrotactile stimuli recruit speech representations in the superior temporal gyrus and lead to increased coupling between them and somatosensory areas. Our results advance our understanding of brain organization by providing new insight into unlocking the metamodal potential of the brain, thereby benefitting the design of novel sensory substitution devices that aim to tap into existing processing streams in the brain. [ABSTRACT FROM AUTHOR]

Traditional cross-modal hash models enable efficient and fast retrieval between multimodal data by training high-quality hash representations. The key to the cross-modal hashing model is feature extraction. However, the quality of the features largely depends on the semantic similarity between the multi-modal data, and the existing methods do not effectively utilize the semantic information between the data. In this paper, we attempt to explore the semantic information inherent within the data using contrastive learning. Specifically, we propose a end-to-end cluster-level contrastive learning method (SCCDH) for cross-modal hashing. The method utilizes the clustering results to guide feature learning in an appropriately designed contrast framework. In SCCDH, feature-level and hash cluster-level contrastive learning are used to help the model learn discriminative features among multimodal data. In addition, we propose a distillation filtering method to filter out a large amount of noise in the data. Extensive experiments were conducted on the MIRFLICKR-25K, NUS-WIDE, and MS-COCO datasets, and the results demonstrate that the proposed method outperformed several state-of-the-art methods.

Emotion recognition plays an essential role in interpersonal communication. However, existing recognition systems use only features of a single modality for emotion recognition, ignoring the interaction of information from the different modalities. Therefore, in our study, we propose a global-aware Cross-modal feature Fusion Network (GCF2-Net) for recognizing emotion. We construct a residual cross-modal fusion attention module (ResCMFA) to fuse information from multiple modalities and design a global-aware module to capture global details. More specifically, we first use transfer learning to extract wav2vec 2.0 features and text features fused by the ResCMFA module. Then, cross-modal fusion features are fed into the global-awaremodule to capture themost essential emotional information globally. Finally, the experiment results have shown that our proposed method has significant advantages than state-of-the-art methods on the IEMOCAP and MELD datasets, respectively. [ABSTRACT FROM AUTHOR]

The multisensory (deep) layers of the superior colliculus (SC) play an important role in detecting, localizing, and guiding orientation responses to salient events in the environment. Essential to this role is the ability of SC neurons to enhance their responses to events detected by more than one sensory modality and to become desensitized ('attenuated' or 'habituated') or sensitized ('potentiated') to events that are predictable via modulatory dynamics. To identify the nature of these modulatory dynamics, we examined how the repetition of different sensory stimuli affected the unisensory and multisensory responses of neurons in the cat SC. Neurons were presented with 2HZ stimulus trains of three identical visual, auditory, or combined visual–auditory stimuli, followed by a fourth stimulus that was either the same or different ('switch'). Modulatory dynamics proved to be sensory-specific: they did not transfer when the stimulus switched to another modality. However, they did transfer when switching from the visual–auditory stimulus train to either of its modality-specific component stimuli and vice versa. These observations suggest that predictions, in the form of modulatory dynamics induced by stimulus repetition, are independently sourced from and applied to the modality-specific inputs to the multisensory neuron. This falsifies several plausible mechanisms for these modulatory dynamics: they neither produce general changes in the neuron's transform, nor are they dependent on the neuron's output. [ABSTRACT FROM AUTHOR]

Hemianopia (unilateral blindness), a common consequence of stroke and trauma to visual cortex, is a debilitating disorder for which there are few treatments. Research in an animal model has suggested that visual-auditory stimulation therapy, which exploits the multisensory architecture of the brain, may be effective in restoring visual sensitivity in hemianopia. It was tested in two male human patients who were hemianopic for at least 8 months following a stroke. The patients were repeatedly exposed to congruent visual-auditory stimuli within their blinded hemifield during 2 h sessions over several weeks. The results were dramatic. Both recovered the ability to detect and describe visual stimuli throughout their formerly blind field within a few weeks. They could also localize these stimuli, identify some of their features, and perceive multiple visual stimuli simultaneously in both fields. These results indicate that the multisensory therapy is a rapid and effective method for restoring visual function in hemianopia. [ABSTRACT FROM AUTHOR]

Due to the fast retrieval speed and low storage cost, cross-modal hashing has become the primary method for cross-modal retrieval. Since the emergence of deep cross-modal hashing methods, cross-modal retrieval significantly improved. However, the existing cross-modal hash retrieval methods still need to effectively utilize the dataset’s supervisory information and the lack of similarity expression ability. This means that the label information needs to be maximized, and the potential semantic relationship between two modalities cannot be fully explored, thus affecting the judgment of semantic similarity between two modalities. To address these problems, this paper proposes Tri-CMH, a cross-modal hash retrieval method with fused triples, which is an end-to-end modeling framework consisting of two parts: feature extraction and hash learning. Firstly, the multi-modal data are preprocessing into the form of triple groups. The data supervision matrix is constructed so that the samples with labels and their meanings are aggregated together. In contrast, the samples with labels and their opposite meanings are separated, thus avoiding the problem of the under-utilization of supervisory information in the data set and achieving the effect of efficiently utilizing the global supervisory information. Meanwhile, the loss function of the hash learning part is optimized by considering the Hamming distance loss, single-modality internal loss, cross-modality loss, and quantization loss to explicitly constrain semantically similar hash codes and semantically dissimilar hash codes and to improve the model’s ability to judge cross-modality semantic similarity. The method is trained and tested on the IAPR-TC12, MIRFLICKR-25K, and NUS-WIDE datasets, and the experimental evaluation criteria are mAP and PR curve, and the experimental results show the effectiveness and practicality of the method.

Fat and sugar-reduced foods and beverages have become increasingly popular for a variety of reasons, mainly relating to health and wellbeing. Depending on the food or beverage, it may be difficult to reduce the fat and/or sugar content and still maintain optimal sensory properties for the specific product. One way of approaching the problem is to gain a better understanding of how a product is affected by a reduction in fat and/or sugar. This paper aims to investigate the sensory interactions between fat and sweetness perception in a chocolate-flavored milk beverage by using a descriptive analysis with a trained sensory panel. The reduction of fat significantly reduced the sweetness intensity of the chocolate milk, while the reduction of sucrose significantly decreased the cream flavor and the fruity and lactic flavor. The perception of acesulfame-K was affected by fat concentration, similarly to sucrose. These results highlight the importance of considering the effects of reducing either sugar and fat on product attributes that are not directly related to the sugar or fat.

Effective feature description for cross-modal remote sensing matching is challenging due to the complex geometric and radiometric differences between multimodal images. Currently, Siamese or pseudo-Siamese networks directly describe features from multimodal remote sensing images at the fully connected layer, however, the similarity of cross-modal features during feature extraction is barely considered. Therefore, we construct a cross-modal feature description matching network (CM-Net) for remote sensing image matching in this paper. First, a contextual self-attention module is proposed to add semantic global dependency information using candidate and non-candidate keypoint patches. Then, a cross-fusion module is designed to obtain cross-modal feature descriptions through information interaction. Finally, a similarity matching loss function is presented to optimize discriminative feature representations, converting a matching task into a classification task. The proposed CM-Net model is evaluated by qualitative and quantitative experiments on four multimodal image datasets, which achieves the average Matching score (M.S.), Mean Matching Accuracy (MMA), and average Root-mean-square error (aRMSE) of 0.781, 0.275, and 1.726, respectively. The comparative study demonstrates the superior performance of the proposed CM-Net for the remote sensing image matching.

Cross-Modal Hashing (CMH) retrieval methods have garnered increasing attention within the information retrieval research community due to their capability to deal with large amounts of data thanks to the computational efficiency of hash-based methods. To date, the focus of cross-modal hashing methods has been on training with paired data. Paired data refers to samples with one-to-one correspondence across modalities, e.g., image and text pairs where the text sample describes the image. However, real-world applications produce unpaired data that cannot be utilised by most current CMH methods during the training process. Models that can learn from unpaired data are crucial for real-world applications such as cross-modal neural information retrieval where paired data is limited or not available to train the model. This paper provides (1) an overview of the CMH methods when applied to unpaired datasets, (2) proposes a framework that enables pairwise-constrained CMH methods to train with unpaired samples, and (3) evaluates the performance of state-of-the-art CMH methods across different pairing scenarios.

Cross-modal similarity query has become a highlighted research topic for managing multimodal datasets such as images and texts. Existing researches generally focus on query accuracy by designing complex deep neural network models and hardly consider query efficiency and interpretability simultaneously, which are vital properties of cross-modal semantic query processing system on large-scale datasets. In this work, we investigate multi-grained common semantic embedding representations of images and texts and integrate interpretable query index into the deep neural network by developing a novel Multi-grained Cross-modal Query with Interpretability (MCQI) framework. The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability and generality of query processing. (2) In order to capture the latent semantic relation between images and texts, the framework combines LSTM and attention mode, which enhances query accuracy for the cross-modal query and constructs the foundation for interpretable query processing. (3) Index structure and corresponding nearest neighbor query algorithm are proposed to boost the efficiency of interpretable queries. (4) A distributed query algorithm is proposed to improve the scalability of our framework. Comparing with state-of-the-art methods on widely used cross-modal datasets, the experimental results show the effectiveness of our MCQI approach. [ABSTRACT FROM AUTHOR]