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- Jinhui TangSuggested from co-authorship
- Shuicheng YanSuggested from co-authorship
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Works157 from public data
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- Weakly-Shared Deep Transfer Networks for Heterogeneous-Domain Knowledge Propagation214
This paper develops a novel deep network structure, capable of transferring labeling information across heterogeneous domains, especially from text domain to image domain, and presents a novel architecture of DTNs to translate cross-domain information from text to image.
- Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition213
A novel Hierarchical Long Short-Term Concurrent Memory (H-LSTCM) is proposed to model the long-term inter-related dynamics among a group of persons for recognizing human interactions by comparing against baseline and state-of-the-art methods.
- Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition197
This work attempts to effectively aggregate the discriminative information of actions and interactions from both RGB videos and skeleton sequences by attentively fusing multi-modal features by implementing a novel Expansion-Squeeze-Excitation Fusion Network (ESE-FN).
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- Personalized Age Progression with Aging Dictionary151
The advantages of the proposed solution over other state-of-the-arts in term of personalized aging progression, as well as the performance gain for cross-age face verification by synthesizing aging faces are demonstrated.
- Generalized Deep Transfer Networks for Knowledge Propagation in Heterogeneous Domains144
A novel generalized deep transfer networks (DTNs) capable of transferring label information across heterogeneous domains, textual domain to visual domain, and to share the labels between two domains are proposed, able to generate domain-specific and shared interdomain features.
- Participation-Contributed Temporal Dynamic Model for Group Activity Recognition140
A novel Participation-Contributed Temporal Dynamic Model (PC-TDM) to recognize group activity, which mainly consists of a "One" network and a " one to Key" network, which aims at modeling the individual dynamic of each person.
- Social Adaptive Module for Weakly-Supervised Group Activity Recognition121
This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data.
- Face Aging with Contextual Generative Adversarial Nets114
A novel Contextual Generative Adversarial Nets (C-GANs) is proposed to specifically take face aging into consideration, and produces appealing results by comparing with the state-of-the-art and ground truth.
- Personalized Age Progression with Bi-Level Aging Dictionary Learning111
This work aims to automatically render aging faces in a personalized way by proposing a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method, formulated to learn the aging dictionaries based on face pairs from neighboring age groups.
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- Pedestrian-specific Bipartite-aware Similarity Learning for Text-based Person Retrieval105
A novel Pedestrian-specific Bipartite-aware Similarity Learning (PBSL) framework is proposed that efficiently reveals the plausible and credible levels of contribution of pedestrian-specific mismatched and matched region-word pairs towards overall similarity.
- Wavelet-Attention CNN for image classification105
A Wavelet-Attention convolutional neural network (WA-CNN) for image classification that decomposes the feature maps into low-frequency and high-frequency components for storing the structures of the basic objects, as well as the detailed information and noise, respectively.
- UATVR: Uncertainty-Adaptive Text-Video Retrieval102
This paper proposes an Uncertainty-Adaptive Text-Video Retrieval approach, termed UATVR, which models each lookup as a distribution matching procedure and adds additional learnable tokens in the encoders to adaptively aggregate multi-grained semantics for flexible high-level reasoning.
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- Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semisupervised Skeleton-Based Action Recognition98
A novel spatiotemporal decouple-and-squeeze contrastive learning (SDS-CL) framework to comprehensively learn more abundant representations of skeleton-based actions by jointly contrasting spatial-squeezing features, temporal-squeezing features, and global features is proposed.
- Deep Ordinal Hashing With Spatial Attention95
A novel deep ordinal hashing (DOH) method is proposed, which learns ordinal representations to generate ranking-based hash codes by leveraging the ranking structure of feature space from both local and global views.
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- Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging81
This work proposes a novel Social anchor-Unit GrAph Regularized Tensor Completion (SUGAR-TC) method to efficiently refine the tags of social images, which is insensitive to the scale of data.
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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 2026-10-13. 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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