mlsanityData to 14 Oct 2026

Context-Enhanced Memory-Refined Transformer for Online Action Detection

arXiv 2503.18359 ↗First posted 24 Mar 2025cs.CVcited by 13

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Abstract (arXiv)

Online Action Detection (OAD) detects actions in streaming videos using past observations. State-of-the-art OAD approaches model past observations and their interactions with an anticipated future. The past is encoded using short- and long-term memories to capture immediate and long-range dependencies, while anticipation compensates for missing future context. We identify a training-inference discrepancy in existing OAD methods that hinders learning effectiveness. The training uses varying lengths of short-term memory, while inference relies on a full-length short-term memory. As a remedy, we propose a Context-enhanced Memory-Refined Transformer (CMeRT). CMeRT introduces a context-enhanced encoder to improve frame representations using additional near-past context. It also features a memory-refined decoder to leverage near-future generation to enhance performance. CMeRT achieves state-of-the-art in online detection and anticipation on THUMOS'14, CrossTask, and EPIC-Kitchens-100.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 49 of its 55 references that are in the map. First posted on arXiv 24 Mar 2025.

Builds on

49 of 55 references.

References · 49

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37 more
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Citing papers per year: 2025: 4; 2026: 9.

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