We present PhysCaP, a Physics-Informed Code-as-Policy agent for active perception in robotic manipulation. While vision-language-action policies excel at imitating demonstrations, they rely on passive observation and fail to infer latent physical properties critical for manipulation. PhysCaP augments code-as-policy fra...
Chen-Yu Lin, Jing-Wen Chen, Hsueh-En Chang et al.· 1 citation
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...
Shih-Ying Yeh, Daniel Z. Kaplan, Xue-Hai Wang et al.· 0 citations
In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with global temporal sampling, which violates causality and prevents deployment in live surveillance streams. Conversely, general streaming video...
Chia-Hui Chen, Shih-Ying Yeh, Fu-En Yang et al.· 0 citations
Online 3D reconstruction models perform poorly on long videos. This happens because regressing poses relative to a fixed first-frame anchor forces extrapolation far beyond the training distribution. Small drifts accumulate and amplify into significant geometric collapse. However, we observe that per-frame depth remains...
Chin-Yang Lin, Yang-Che Sun, Cheng Sun et al.· 0 citations
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