Self-Supervised Learning of Structured Dynamics from Videos
Understanding motion in video is a fundamental challenge for visual learning, as frame-to-frame change entangles two sources of dynamics: camera motion and object motion. This decomposition has remained underexplored in representation learning, partly because these factors are tightly coupled in natural videos and difficult to supervise separately. Yet recovering it is important for learning robust motion representations that separate meaningful object dynamics from camera-induced variation. We study whether such structured motion representations can be recovered from frozen features of a pret
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- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Self-Supervised Learning of Structured Dynamics from Videos” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Lukas Knobel →
“Self-Supervised Learning of Structured Dynamics from Videos”
- LinkedLinked via arxiv author · 85%Andrew Zisserman →
“Self-Supervised Learning of Structured Dynamics from Videos”
- LinkedLinked via arxiv author · 85%Yuki M. Asano →
“Self-Supervised Learning of Structured Dynamics from Videos”