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Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals
Opt-CWM is a self-supervised method for motion estimation from videos, achieving state-of-the-art performance without labeled data by optimizing counterfactual probes from a pre-trained model.
https://arxiv.org/abs//2503.19953
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
Opt-CWM is a self-supervised method for motion estimation from videos, achieving state-of-the-art performance without labeled data by optimizing counterfactual probes from a pre-trained model.
https://arxiv.org/abs//2503.19953
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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