activity
20222026
most citedTSA-Net: Tube Self-Attention Network for Action Quality Assessment

94 citations · 153 across the 39 of their papers we have counts for

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46 papers · 1 filter

cs.CV2026

SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation

Ziyun Qian, Zizhi Chen, Yizhou Liu +3

Complex 3D spatial text to image generation requires models to convert natural language into stable visual geometry, not merely semantic appearance. Existing prompt-driven or layou…

cs.CV2026

MM-Snowball: Evaluating and Mitigating Hallucination Snowballing in Multimodal Multi-Turn Dialogue

Yue Jiang, Xue Jiang, Lihua Zhang +6

Multimodal large language models (MLLMs) demonstrate remarkable visual understanding, yet their reliability in interactive settings is severely undermined by hallucination snowball…

cs.CV2026

Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology

Minghao Han, Dingkang Yang, Linhao Qu +5

Recent years have witnessed remarkable progress in multimodal learning within computational pathology. Existing models primarily rely on vision and language modalities; however, la…

cs.CV2026

OmniFysics: Towards Physical Intelligence Evolution via Omni-Modal Signal Processing and Network Optimization

Minghao Han, Dingkang Yang, Yue Jiang +2

The autonomous evolution of networked AI systems relies heavily on robust environmental perception. However, physical understanding remains brittle in current models because key ph…

cs.CV2025

Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Models

Zizhi Chen, Yizhen Gao, Minghao Han +4

Multimodal biomedical Vision-Language Models (VLMs) exhibit immense potential in the field of Continual Learning (CL). However, they confront a core dilemma: how to preserve fine-g…

cs.CV2025

FysicsWorld: A Unified Full-Modality Benchmark for Any-to-Any Understanding, Generation, and Reasoning

Yue Jiang, Dingkang Yang, Minghao Han +6

Despite rapid progress in multimodal large language models (MLLMs) and emerging omni-modal architectures, current benchmarks remain limited in scope and integration, suffering from…