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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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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…

cs.CV2024

Visual Object Tracking across Diverse Data Modalities: A Review

Mengmeng Wang, Teli Ma, Shuo Xin +5

Visual Object Tracking (VOT) is an attractive and significant research area in computer vision, which aims to recognize and track specific targets in video sequences where the targ…

cs.CV2024

Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing

Haonan Lin, Mengmeng Wang, Jiahao Wang +7

Text-guided diffusion models have significantly advanced image editing, enabling high-quality and diverse modifications driven by text prompts. However, effective editing requires…

cs.CV2024

Timestep-Aware Correction for Quantized Diffusion Models

Yuzhe Yao, Feng Tian, Jun Chen +4

Diffusion models have marked a significant breakthrough in the synthesis of semantically coherent images. However, their extensive noise estimation networks and the iterative gener…

cs.CV2024

TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On

Jiazheng Xing, Chao Xu, Yijie Qian +5

Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. However, the cl…

cs.CV2024

DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation

Haonan Lin, Mengmeng Wang, Yan Chen +6

While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images, novel challenges arise with a nuanced task of "identity fine editi…

cs.CV2024★ 2 cited

SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking

Xiaojun Hou, Jiazheng Xing, Yijie Qian +8

Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers, which was i…

cs.CV2023★ 3 cited

MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition

Jiazheng Xing, Chao Xu, Mengmeng Wang +5

Applying large-scale vision-language pre-trained models like CLIP to few-shot action recognition (FSAR) can significantly enhance both performance and efficiency. While several stu…