Reproduce “General Object Foundation Model for Images and Videos at Scale” on OpenResearch
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About this paper
Junfeng Wu, Yi Jiang, Qihao Liu, Zehuan Yuan, Xiang Bai, Song Bai
GLEE introduces an object-level foundation model that unifies diverse visual perception tasks, encompassing detection, segmentation, tracking, and grounding. The model demonstrates state-of-the-art zero-shot generalization across various image and video benchmarks, outperforming existing methods like OVTrack by 36% on TAO for open-world video tracking, and offers a cohesive framework for object-centric understanding.
Key insights
- A unified object-level foundation model can achieve state-of-the-art performance across diverse image and video perception tasks, reducing the need for specialized models.
- Leveraging diverse data sources with varying levels of supervision and large-scale automatically labeled data is critical for developing robust zero-shot generalization capabilities in object-centric visual AI.
- Object embeddings derived from a unified framework can inherently support complex temporal tasks like multi-object tracking, especially when enhanced with contrastive learning for temporal consistency, without requiring explicit tracking heads.
Summary by alphaXiv.
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