arXiv Computer Vision
Beyond Scene Shortcuts: Action-Centric Video-Text Alignment for Fine-Grained Action Understanding
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Making Foresight Actionable: Repurposing Representation Alignment in World Action Models
arXiv:2606. 12217v1 Announce Type: cross Abstract: World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions.
Hugging Face Trending Papers
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Making Foresight Actionable: Repurposing Representation Alignment in World Action Models
World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions. However, our empirical observations reveal a phenomenon: generating plausible visual futures does not always guarantee the extraction of accurate actions.
arXiv Computer Vision
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VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning
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arXiv Computer Vision
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Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization
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Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion
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4d ago
EgoActionBench: Benchmarking Dense Action Captioning for Egocentric Human Videos
EgoActionBench is a new benchmark for dense action captioning in egocentric videos, featuring 1,407 episodes and 41,132 fine‑grained action annotations that specify which hand (left, right, or both) performs each temporally localized atomic action. The benchmark introduces an evaluation protocol that assesses semantic fidelity, temporal overlap, action precision, and recall, thereby measuring what happens, when it happens, and which hand is involved. Twenty baseline models were evaluated, and two backbones (Qwen3‑VL‑4B and LLaVA‑OneVision‑2‑8B) were fine‑tuned and reinforced, achieving significant score improvements and outperforming all open‑source baselines.