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Works219 from public data
- Hierarchical LSTM for Sign Language Translation216
A hierarchical-LSTM (HLSTM) encoder-decoder model with visual content and word embedding for SLT exhibits promising performance on singer-independent test with seen sentences and also outperforms the comparison algorithms on unseen sentences.
- Benchmarking Micro-Action Recognition: Dataset, Methods, and Applications175
A new micro-action dataset designated as Micro-action-52 (MA-52), and a benchmark named micro-action network (MANet) for micro-action recognition (MAR) task are proposed, which assesses MANet and other nine prevalent action recognition methods.
- DADNet174
A novel deep model called Dilated-Attention-Deformable ConvNet (DADNet), which consists of two schemes: multi-scale dilated attention and deformable convolutional DME (Density Map Estimation).
- Proposal-Free Video Grounding with Contextual Pyramid Network148
This paper proposes a novel proposal-free framework named Contextual Pyramid Network (CPNet) to investigate multi-scale temporal correlation in the video and implements a temporal self-attentive regression to predict the activity boundary from the above hierarchical context-aware 2D correlation maps.
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- Contrastive Positive Sample Propagation Along the Audio-Visual Event Line115
A new contrastive positive sample propagation (CPSP) method is proposed for better deep feature representation learning and is more helpful to obtain the refined audio-visual features that are distinguishable from the negatives.
- Connectionist Temporal Fusion for Sign Language Translation108
A hybrid deep architecture which consists of a temporal convolution module (TCOV), a bidirectional gated recurrent unit module (BGRU), and a fusion layer module (FL) to address the CSLT problem and achieves comparable performance to other existing methods with multiple EM iterations.
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- Audio-Visual Segmentation with Semantics104
A new baseline method is proposed that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process, and a regularization loss is designed to encourage audio-visual mapping during training.
- Dense Temporal Convolution Network for Sign Language Translation99
A dense temporal convolution network, termed DenseTCN which captures the actions in hierarchical views and addresses the SLT problem by different views, including embedded short-term and extended longterm sequential learning.
- MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation83
A novel model, multimodal entity graph collaborative filtering, short for MEGCF, is developed that captures the semantic correlation between interactions and the features obtained from MFE, thus making a better match between MFE and UIM.
- ViGT: proposal-free video grounding with a learnable token in the transformer79
A novel boundary regression paradigm that performs regression token learning in a transformer, namely video grounding transformer (ViGT), which predicts the temporal boundary using a learnable regression token rather than multi- modal or cross-modal features.
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- Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition76
A novel Prototypical Calibrating Ambiguous Network (PCAN) is proposed to unleash and mitigate the ambiguity of MAR and proposes a prototype-guided rectification to rectify prediction by incorporating the representability of prototypes.
- Transformer-Based Visual Grounding with Cross-Modality Interaction75
This article presents an effective transformer-based end-to-end visual grounding approach, which focuses on capturing the cross-modality correlations between the referring expression and visual regions for accurately reasoning the location of the target region.
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- PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation68
This article proposes a specialized psychological LLM, named PsycoLLM, trained on a proposed high-quality psychological dataset, including single-turn QA, multiturn dialogues, and knowledge-based QA, and develops a comprehensive psychological benchmark based on authoritative psychological counseling examinations in China.
- Gloss Semantic-Enhanced Network with Online Back-Translation for Sign Language Production68
A Gloss semantic-Enhanced Network is proposed with Online Back-Translation (GEN-OBT) for G2P in the SLP task, which outperforms the state-of-the-art models and designs a CTC-based reverse decoder to convert the generated poses backward into glosses, which guarantees the semantic consistency during the processes of gloss- to-pose and pose-to-gloss.
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- Online Early-Late Fusion Based on Adaptive HMM for Sign Language Recognition65
This work proposes an adaptive HMM method to obtain the hidden state number of each sign by affinity propagation clustering and discovers that inherent latent change states of eachsign are related not only to the number of key gestures and body poses but also to their translation relationships.
- EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering61
This work introduces EgoTextVQA, a novel and rigorously constructed benchmark for egocentric QA assistance involving scene text, and comprehensively evaluates 10 prominent multimodal large language models.
- Iterative Context-Aware Graph Inference for Visual Dialog61
This work proposes a novel Context-Aware Graph (CAG) neural network, where each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations.
- Joint Multi-Grained Popularity-Aware Graph Convolution Collaborative Filtering for Recommendation57
This work develops a Joint Multigrained Popularity-aware Graph Convolution Collaborative Filtering model, short for JMP-GCF, which uses a popularity-aware embedding generation to construct multigrained popularity features and uses the idea of joint learning to capture the signals within and between different granularities of popularity features that are relevant for modeling user preferences.
- Connectionist Temporal Modeling of Video and Language: a Joint Model for Translation and Sign Labeling57
A Connectionist Temporal Modeling (CTM) network for sentence translation and sign labeling is proposed and dynamic programming is embedded into the decoding scheme, which learns temporal mapping among features, sign labels, and the generated sentence directly.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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