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- Jinhui TangSuggested 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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- MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition69
A novel Multi-Velocity Progressive-alignment (MVP-Shot) framework to progressively learn and align semantic-related action features at multi-velocity levels and outperforms current state-of-the-art methods on multiple standard few-shot benchmarks.
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- Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification66
A novel approach to remove irrelevant samples from the real-world web images during training, and only utilize useful images for updating the networks is proposed, which can alleviate the harmful effects caused by irrelevant noisy web images to achieve better performance.
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- Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition49
This work proposes a novel Pyramid Self-attention Polymerization Learning framework to jointly learn body-level, part- level, and joint-level action representations of joint and motion data containing abundant and complementary semantic information via contrastive learning covering coarse-to-fine granularity.
- Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-Identification47
A triplet contrastive representation learning (TCRL) framework, which leverages cluster features to bridge the part features and global features for unsupervised vehicle re-identification and outperforms many state-of-the-art unsupervised vehicle re-identification approaches.
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- Facial Age Synthesis With Label Distribution-Guided Generative Adversarial Network45
This paper proposes label distribution-guided generative adversarial network (ldGAN), where each sample is associated with an age label distribution rather than a single age group, and demonstrates the effectiveness of ldGAN in both learning short-term aging patterns and coping with the lack of training data.
- Deep Face Beautification44
A deep face beautification framework, which is able to automatically modify the geometrical structure of a face so as to boost the attractiveness via training the Deep Beauty Predictor (DBP).
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- CAN-GAN: Conditioned-attention normalized GAN for face age synthesis38
This work proposes a novel Conditioned-Attention Normalization GAN for age synthesis by leveraging the aging difference between two age groups to capture facial aging regions with different attention factors and presents a Contribution-Aware Age Classifier that finely measures the importance of face vector’s elements in terms of the age classification.
- Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition35
A novel Concurrent Long Short-Term Memories (Co-LSTM) is proposed to model the long-term inter-related dynamics between two interacting people on the bonding boxes covering people and is shown to have superior performance compared with the state-of-the-arts methods.
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- Spatio-Temporal Decoupled Knowledge Compensator for Few-Shot Action Recognition26
DiST, an innovative Decomposition-incorporation framework for FSAR that makes use of decoupled Spatial and Temporal knowledge provided by large language models to learn expressive multi-granularity prototypes, is proposed.
- Data-driven Meta-set Based Fine-Grained Visual Recognition26
This work proposes a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition that can alleviate the harmful effects caused by out-of-distribution noise and properly exploit the in-dist distribution noisy samples for training.
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- GPT4Ego: Unleashing the Potential of Pre-Trained Models for Zero-Shot Egocentric Action Recognition24
A straightforward yet remarkably potent VLM framework, GPT4Ego, designed to enhance the fine-grained alignment of concept and description between vision and language, which can sustainably evolve with the advancement of ever-growing pre-trained foundational models.
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- Surgical video workflow analysis via visual-language learning23
This work proposes a vision-language deep learning framework that incorporates intra- and inter- triplet modeling, termed I2TM, to explore the relationships among triplets and leverage the model understanding of the entire surgical process, thereby enhancing the accuracy and robustness of recognition.
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- Look Less Think More: Rethinking Compositional Action Recognition19
A simple yet effective framework, Look Less Think More (LLTM), to reduce the strong association between visual objects and action-level labels (Look Less), and then discover the commonsense relationships between object categories and human actions (Think More).
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- FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data16
A benchmark to evaluate the performance of federated fine-tuning of MLLMs across various multimodal heterogeneous scenarios is introduced and extensive experimental results show that the proposed FL paradigm improves the performance of MLLMs by broadening the range of training data and mitigating multimodal heterogeneity.
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- DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting13
Experimental results on diverse datasets consistently demonstrate that DoFIT excels in cross-domain collaborative training and exhibits significant advantages over conventional FIT methods in alleviating catastrophic forgetting.
- MUP: Multi-granularity Unified Perception for Panoramic Activity Recognition13
A novel Multi-granularity Unified Perception (MUP) framework that perceives different granularity behaviors universally to explore the co-occurrence motion pattern via the same parameters in an end-to-end fashion is proposed.
- Skip-attention encoder–decoder framework for human motion prediction13
A novel skip-attention encoder–decoder (SAED) framework to model human motion dependences in spatiotemporal space, by utilizing the encoder and decoder to encode the observed motions, and decode the predicted motions, respectively is proposed.
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- Storyboard relational model for group activity recognition10
A Storyboard Relational Model (SRM) is proposed to address the problem of Group Activity Recognition by splitting and reintegrating the group activity based on the small yet compact Storyboards.
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- Foreground/Background-Masked Interaction Learning for Spatio-temporal Action Detection8
A novel Foreground/Background-masked Interaction Learning framework to learn the multi-actor features by attentively interacting with the hands-down foreground and background frames and a new Actor-guided Feature Aggregation scheme that integrates foreground- and background-interacted actor features with the learnable actor-based weights are presented.
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- AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition6
A novel Adapt-Focused bi-Propagating Prototype learning (AdaFPP) framework to jointly recognize individual, group, and global activities in panoramic activity scenes by learning an adapt-focused detector and multi-granularity prototypes as the pretext tasks in an end-to-end way is proposed.
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- Leveraging Frame- and Feature-level Progressive Augmentation for Semi-supervised Action Recognition5
A new Progressive Augmentation mechanism that implements the weak/strong augmentations first at the frame level, and further implements the perturbation at the feature level, to obtain abundant four types of augmented features in broader perturbation spaces is designed.
- Slowfast Diversity-aware Prototype Learning for Egocentric Action Recognition5
A new Part-to-Prototype (P2P) scheme to learn prototypes from raw videos covering the interacting objects by refining the semantic information from part level to prototype level is presented, and a new Slow-Fast Context (SFC) mechanism is designed that explores the Up/Down augmentations for the prototype representation at the semantic level.
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- Partially Common-Semantic Pursuit for RGB-D Object Recognition5
A novel supervised feature learning method for RGB-D object recognition, named Partially Common-Semantic Learning (PCSL), which jointly captures the complementary and consistency semantic information from RGB and depth modalities.
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- Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning3
In A 2 MC, Attack-Augmentation (Att-Aug) is designed to collaboratively perform targeted and untargeted perturbations of skeletons via attack and augmentation respectively, for generating high-quality hard positive features and hard negative features.
- Dilation-erosion for single-frame supervised temporal action localization3
The Snippet Classification model and the Dilation-Erosion module are presented, which relies on the single-frame annotation and the output of the snippet classification, and a new embedding loss is proposed to aggregate the features of action instances with the same label and separate the Features of actions from backgrounds.
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- Reliable and Diverse Hierarchical Adapter for Zero-shot Video Classification2
This work proposes a reliable and diverse Hierarchical Adapter for zero-shot video classification, which consists of Frame-level Cache Refiner and Video-level Cache Updater, and updates the cache during inference based on the principle of diversity.
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- FedIGA: Improving Global Model Aggregation for Federated Self-Supervised Skeletal Action Recognition–
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- Author response for "Multi-Granularity Aggregation Network for Remote Sensing Few-Shot Segmentation"–
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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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