✨EdgeTAM: On-Device Track Anything Model

📝 Summary:
EdgeTAM optimizes SAM 2 for mobile devices by addressing memory attention bottlenecks with a novel 2D Spatial Perceiver. This lightweight Transformer encodes frame-level memories to reduce computational cost. A distillation pipeline improves performance, enabling high-quality video segmentation a...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.07256
• PDF: https://arxiv.org/pdf/2501.07256
• Github: https://github.com/facebookresearch/edgetam

🔹 Models citing this paper:
• https://huggingface.co/yonigozlan/EdgeTAM-hf
• https://huggingface.co/facebook/EdgeTAM

✨ Spaces citing this paper:
• https://huggingface.co/spaces/merve/EdgeTAM
• https://huggingface.co/spaces/yonigozlan/edgetam
• https://huggingface.co/spaces/facebook/EdgeTAM

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#EdgeAI #VideoSegmentation #ComputerVision #MobileAI #DeepLearning