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Jiahang Zhang

7 accepted papers

2026

DeepMed Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification

IJCAI 2026

Navigating the deluge of heterogeneous medical data, from academic literature (PubMed) to clinical guidelines (Web) and private knowledge bases remains a critical bottleneck for evidence-based medicine. While commercial black-box tools lack transparency, standard open-source RAG implementations freq

Cited by 0Scholar
2025

SGAR: Structural Generative Augmentation for 3D Human Motion Retrieval

NeurIPS 2025poster

3D human motion-text retrieval is essential for accurate motion understanding, targeted at cross-modal alignment learning. Existing methods typically align the global motion-text concepts directly, suffering from sub-optimal generalization due to the uncertainty of correspondence learning between mu…

Cited by 0SourceScholar
2024

Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition

ECCV 2024poster

"Generative models, as a powerful technique for generation, also gradually become a critical tool for recognition tasks. However, in skeleton-based action recognition, the features obtained from existing pre-trained generative methods contain redundant information unrelated to recognition, which con…

2024

MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion

ECCV 2024poster

"Self-supervised learning has proved effective for skeleton-based human action understanding. However, previous works either rely on contrastive learning that suffers false negative problems or are based on reconstruction that learns too much unessential low-level clues, leading to limited represent…

2024

Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition

IJCAI 2024poster

In real-world scenarios, human actions often fall into a long-tailed distribution. It makes the existing skeleton-based action recognition works, which are mostly designed based on balanced datasets, suffer from a sharp performance degradation. Recently, many efforts have been made to image/video lo…

2023

Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition

CVPR 2023highlight

The self-supervised pretraining paradigm has achieved great success in skeleton-based action recognition. However, these methods treat the motion and static parts equally, and lack an adaptive design for different parts, which has a negative impact on the accuracy of action recognition. To realize t…

Cited by 79SourcePDFScholar
2023

Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations

AAAI 2023technical

Contrastive learning has been proven beneficial for self-supervised skeleton-based action recognition. Most contrastive learning methods utilize carefully designed augmentations to generate different movement patterns of skeletons for the same semantics. However, it is still a pending issue to apply…