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- Changsheng XuSuggested from co-authorship
- Qi Chuan TianSuggested from co-authorship
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Works227 from public data
- NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding1,846
This work introduces a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames, and investigates a novel one-shot 3D activity recognition problem on this dataset.
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- Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning452
A data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation and develops customized label sampling and class-level ensemble to derive maximum utilization of knowledge.
- Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation346
This work proposes a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains, which outperforms other global feature alignment based and class-wise alignment based counterparts.
- Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics271
The definition, formulation, and paradigm of VCM are given, and the preliminary results have demonstrated the performance and efficiency gains, and state-of-the-art techniques in video compression and feature compression are systematically reviewed from the unique perspective of MPEG standardization.
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- Location Discriminative Vocabulary Coding for Mobile Landmark Search191
A Location Discriminative Vocabulary Coding (LDVC) scheme, which achieves extremely low bit rate query transmission, discriminative landmark description, as well as scalable descriptor delivery in a unified framework is presented.
- A mid-level representation framework for semantic sports video analysis190
A generic mid-level representation framework is introduced between the low-level audio-visual processing and high-level semantic analysis and can be used for event detection, highlight extraction, summarization and personalization of many types of sports video.
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- Generalizable Person Re-identification with Relevance-aware Mixture of Experts174
This work proposes a novel method called the relevance-aware mixture of experts (RaMoE), using an effective voting-based mixture mechanism to dynamically leverage source domains’ diverse characteristics to improve the model’s generalization.
- Skeleton-Based Online Action Prediction Using Scale Selection Network174
A dilated convolutional network is introduced to model the motion dynamics in temporal dimension via a sliding window over the temporal axis and a novel window scale selection method is proposed to make the network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step.
- IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID154
An Intermediate Domain Module (IDM) is proposed to generate intermediate domains’ representations on-the-fly by mixing the source and target domains�’ hidden representations using two domain factors and ensures two properties that these intermediate domains should satisfy.
- HMM-Based Audio Keyword Generation148
Experimental results show that, for audio keyword generation, the proposed HMM-based method outperforms the previous hierarchical SVM, and is motivated by the successful story of HMM in speech recognition.
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- CRRN: Multi-scale Guided Concurrent Reflection Removal Network137
This paper proposes the Concurrent Reflection Removal Network (CRRN), a network that integrates image appearance information and multi-scale gradient information with human perception inspired loss function, and is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes.
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- Feature Boosting Network For 3D Pose Estimation135
A context consistency gate (CCG) is introduced in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations, to improve the reliability of the features for representing each body part and enhance the LSTD module.
- Dual-Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re-Identification114
This work proposes a novel approach, called Dual-Refinement, that jointly refines pseudo labels at the off-line clustering phase and features at the on-line training phase, to alternatively boost the label purity and feature discriminability in the target domain for more reliable re-ID.
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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 2026-10-12. 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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