From Static to Dynamic: Exploring Self-supervised Image-to-Video Representation Transfer Learning
Recent studies have made notable progress in video representation learning by transferring image-pretrained models to video tasks, typically with complex temporal modules and video fine-tuning. However, fine-tuning heavy modules may compromise inter-video semantic separability, i.e., the essential ability to distinguish objects across videos. While reducing the tunable parameters hinders their intra-video temporal consistency, which is required for stable representations of the same object within a video. This dilemma indicates a potential trade-off between the intra-video temporal consistency and inter-video semantic separability during image-to-video transfer. To this end, we propose th...
| Authors | Yang Liu, Qianqian Xu, Peisong Wen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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