EV-CLIP: Efficient Visual Prompt Adaptation for CLIP in Few-shot Action Recognition under Visual Challenges
CLIP has demonstrated strong generalization in visual domains through natural language supervision, even for video action recognition. However, most existing approaches that adapt CLIP for action recognition have primarily focused on temporal modeling, often overlooking spatial perception. In real-world scenarios, visual challenges such as low-light environments or egocentric viewpoints can severely impair spatial understanding, an essential precursor for effective temporal reasoning. To address this limitation, we propose Efficient Visual Prompting for CLIP (EV-CLIP), an efficient adaptation framework designed for few-shot video action recognition across diverse scenes and viewpoints. EV...
| Authors | Hyo Jin Jon, Longbin Jin, Eun Yi Kim |
|---|---|
| Primary category | cs.CV |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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