1 citations · 1 across the 11 of their papers we have counts for
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What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective
Jiazhen Huang, Xiao Chen, Zhiming Liu +3
Vision-Language Models (VLMs) such as CLIP have become a standard backbone for open-vocabulary recognition, yet their zero-shot predictions remain vulnerable to distribution shifts…
Thinking in Dynamics: How Multimodal Large Language Models Perceive, Track, and Reason Dynamics in Physical 4D World
Yuzhi Huang, Kairun Wen, Rongxin Gao +14
Humans inhabit a physical 4D world where geometric structure and semantic content evolve over time, constituting a dynamic 4D reality (spatial with temporal dimension). While curre…
Neural Collapse in Test-Time Adaptation
Xiao Chen, Zhongjing Du, Jiazhen Huang +4
Test-Time Adaptation (TTA) enhances model robustness to out-of-distribution (OOD) data by updating the model online during inference, yet existing methods lack theoretical insights…
Accelerating Parallel Diffusion Model Serving with Residual Compression
Jiajun Luo, Yicheng Xiao, Jianru Xu +5
Diffusion models produce realistic images and videos but require substantial computational resources, necessitating multi-accelerator parallelism for real-time deployment. However,…
Test-Time Distillation for Continual Model Adaptation
Xiao Chen, Jiazhen Huang, Zhiming Liu +4
Deep neural networks often suffer performance degradation upon deployment due to distribution shifts. Continual Test-Time Adaptation (CTTA) aims to address this issue in an unsuper…
COSMIC: Clique-Oriented Semantic Multi-space Integration for Robust CLIP Test-Time Adaptation
Fanding Huang, Jingyan Jiang, Qinting Jiang +3
Recent vision-language models (VLMs) face significant challenges in test-time adaptation to novel domains. While cache-based methods show promise by leveraging historical informati…