The TIME Machine: On The Power of Motion for Efficient Perception
The paper titled 'The TIME Machine: On The Power of Motion for Efficient Perception' proposes a novel approach to video representation learning. It introduces a method that utilizes motion as the central modality, addressing limitations of current video models. The authors demonstrate that their approach, using a new embedding called TIME, achieves competitive performance with significantly less training data.
- ▪The proposed method uses motion in videos to improve representation learning.
- ▪This approach reduces the scale of training data needed and bypasses language-dependent training.
- ▪The new embedding, TIME, is trained exclusively on synthetic motion data and performs on par with state-of-the-art models.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23045 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | GHdZb9NfL-oy |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computer Vision and Pattern Recognition arXiv:2605.23045 (cs) [Submitted on 21 May 2026] Title:The TIME Machine: On The Power of Motion for Efficient Perception Authors:Mantas Skackauskas, Xinyue Hao, Laura Sevilla-Lara View a PDF of the paper titled The TIME Machine: On The Power of Motion for Efficient Perception, by Mantas Skackauskas and 2 other authors View PDF HTML (experimental) Abstract:Video representation learning has seen tremendous progress in recent years. This has been driven by many factors, including the scale of training and the success of visual models trained contrastively with language.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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