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20232026
most citedMA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition

3 citations · 5 across the 10 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Beyond Time Shifts: Adapting Omni-LLM as a Reference-Free Evaluator for Generative Audio-Visual Models

Yijie Qian, Juncheng Wang, Chao Xu +6

As audio-visual generative models evolve into world simulators, cross-modal synchronization stands as a critical proxy for assessing the consistency of world dynamics and causality…

cs.CV2026

Progression as Latent Drift: Generative Forecasting of Slow-Evolving Pathologies

Yuxiang Feng, Juncheng Wang, Chao Xu +7

Forecasting the future anatomy of slow-evolving neurodegenerative diseases could enable earlier, more targeted intervention and improve clinical trial design, but it remains challe…

cs.CV2026

Contact Matrix: Enhancing Dance Motion Synthesis with Precise Interaction Modeling

Xuhai Chen, Zhi Cen, Huaijin Pi +3

Generating realistic reactive motions, in which one person reacts to the fixed motions of others, is challenging due to strict interaction constraints and a limited feasible soluti…

cs.CV2026

Knowledge is Power: Advancing Few-shot Action Recognition with Multimodal Semantics from MLLMs

Jiazheng Xing, Chao Xu, Hangjie Yuan +4

Multimodal Large Language Models (MLLMs) have propelled the field of few-shot action recognition (FSAR). However, preliminary explorations in this area primarily focus on generatin…

cs.CV2026

LumosX: Relate Any Identities with Their Attributes for Personalized Video Generation

Jiazheng Xing, Fei Du, Hangjie Yuan +7

Recent advances in diffusion models have significantly improved text-to-video generation, enabling personalized content creation with fine-grained control over both foreground and…

cs.CV2026

SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training

Mengmeng Wang, Dengyang Jiang, Liuzhuozheng Li +6

Denoising-based diffusion transformers, despite their strong generation performance, suffer from inefficient training convergence. Existing methods addressing this issue, such as R…