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VESSA: Video-based objEct-centric Self-Supervised Adaptation for Visual Foundation Models

arXiv — cs.LG•Monday, October 27, 2025 at 4:00:00 AM
NeutralArtificial Intelligence
The recent paper on VESSA introduces a novel approach to enhance visual foundation models through video-based self-supervised adaptation. This method addresses the challenges faced by these models in scenarios with distribution shifts and limited labeled data, which are common in real-world applications. By leveraging self-supervised learning techniques, the research aims to improve model performance in diverse visual tasks, making it a significant contribution to the field of computer vision.
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