Wenxuan Ge — accepted AI-conference papers

2026

Condensed Test-Time Adaptation of VLMs for Action Recognition

Wenxuan Ge, Hongyu Qu, Rui Yan, Guo-Sen Xie, Yazhou Yao, Xiangbo Shu +1

CVPR 2026

Test-time adaptation for video understanding, which enables vision-language models (VLMs) to generalize to downstream tasks such as action recognition, has demonstrated substantial value in real-world applications. Existing memory-based methods typically build a visual cache from high-confidence tes

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2025

Reliable and Diverse Hierarchical Adapter for Zero-shot Video Classification

Wenxuan Ge, Peng Huang, Rui Yan, Hongyu Qu, Guosen Xie, Xiangbo Shu

IJCAI 2025

Adapting pre-trained vision-language models to downstream tasks has emerged as a novel paradigm for zero-shot learning. Existing test-time adaptation (TTA) methods such as TPT attempt to fine-tune visual or textual representations to accommodate downstream tasks but still require expensive optimizat

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