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arXiv Machine Learning By Genpei Zhang

OccJEPA: Occlusion as Masking for Amodal Video Segmentation

Read the original on arXiv Machine Learning →

OccJEPA treats occlusion as masking for amodal video segmentation by dropping tokens outside an object's visible mask and using a pre‑trained V‑JEPA 2.1 predictor to fill in the missing features, followed by a lightweight head that decodes these into complete masks. The resulting deterministic model is 33.5× faster than diffusion‑based state‑of‑the‑art methods and achieves higher accuracy on SAIL‑VOS, TAO‑Amodal (including a held‑out test split), and Kubric. Experiments show that the performance gains stem from the predictor’s pre‑trained weights rather than its size, with models ranging from 87 M to 1.85 B parameters.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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