TT-VidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining
Comparisons in video self-supervised learning often evaluate complete training recipes rather than isolating the method itself: architecture, objective, data exposure, schedule, scale, and decoder capacity can all vary at once. This makes it hard to identify which choices yield motion-prioritized representations, whose gains concentrate on frame-to-frame change while retaining useful appearance. We address this with a matched $4 \times 6 = 24$ architecture-objective study at roughly 170M ~ 190M encoder scale on $\sim$1.7M OpenVid and Moments-in-Time v2 clips for 8 epochs, and propose TT-VidT. TT-VidT combines a DINOv3-initialized ViT-B/16 per-frame spatial path with a compact Temporal Tra...
| Authors | Shih-Ying Yeh, Daniel Z. Kaplan, Xuehai Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 September 2026 |
| Last updated at source | 29 September 2026 |
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