arXiv Computer Vision
Kronecker-Structured Video Completion with Cross-Fitted Self-Supervised Residual Learning
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arXiv Computer Vision
Oct 1
TripleFlow: Training-Free Video Object Removal by Bridging Residual Editing and Native Generation
arXiv:2609.39157v1 Announce Type: new Abstract: Video object removal presents a uniquely difficult editing challenge. Because a removal prompt specifies only what to erase rather than what to generat...
arXiv Machine Learning
Aug 26
Joint-Embedding Prediction of Masked Point Tubes for Self-Supervised Learning on 4D Point Cloud Videos
arXiv:2608.24093v1 Announce Type: cross Abstract: Self-supervised representation learning for 4D point cloud videos is challenging because annotations are costly and reconstruction-based pretraining...
arXiv AI
Jul 28
The Visual Bottleneck: Sparse-Frame Adaptation of MLLMs for Joint Spatial-Temporal Video Grounding
arXiv:2607. 24570v1 Announce Type: cross Abstract: Large-scale video platforms process millions of uploads hourly, requiring moderation systems that can localize when and where policy violations occur within each video.
arXiv AI
Oct 26
Learning from Next-Frame Prediction: Towards Unified and Scalable Visual World Modeling
arXiv:2610.30724v2 Announce Type: replace-cross Abstract: Video provides a natural source of supervision: what happens next serves as a learning signal for visual world modeling. Yet masked video pre...
arXiv Computer Vision
Oct 1
Image Classifiers are Efficient Self-Supervised Video Representation Learners
arXiv:2609.40347v1 Announce Type: new Abstract: We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of r...
arXiv Computer Vision
Oct 23
Learning from Next-Frame Prediction: Towards Unified and Scalable Visual Representation Learning
The paper introduces VANE, a self‑supervised visual pretraining framework that learns from next‑frame prediction in embedding space. It uses a causal encoder to summarize past observations, a state predictor to infer the representation of the entire next frame, and a lightweight generative decoder to reconstruct fine‑grained spatial detail. By applying the same objective to both videos and still images through a local‑to‑global crop sequence, VANE achieves state‑of‑the‑art performance on anticipation, recognition, and dense perception benchmarks, notably outperforming V‑JEPA on EPIC‑KITCHENS‑100 action anticipation.