TinySAM 2 reaches 90% of SAM 2.1 performance on DAVIS and SA-V using 7% of the memory tokens and 3% of the training data via frame selection, spatial average pooling, temporal similarity-based token pruning, and a RepViT image encoder.
Tinysam: Pushing the envelope for efficient segment any- thing model
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2026 2representative citing papers
CAR-SAM introduces MatMul-Aware Compensation and Joint Cross-Attention Reconstruction to enable stable 4-bit post-training quantization of SAM, outperforming prior PTQ methods by 14.6% mAP on SAM-B and 6.6% on SAM-L.
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TinySAM 2: Extreme Memory Compression for Efficient Track Anything Model
TinySAM 2 reaches 90% of SAM 2.1 performance on DAVIS and SA-V using 7% of the memory tokens and 3% of the training data via frame selection, spatial average pooling, temporal similarity-based token pruning, and a RepViT image encoder.
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CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model
CAR-SAM introduces MatMul-Aware Compensation and Joint Cross-Attention Reconstruction to enable stable 4-bit post-training quantization of SAM, outperforming prior PTQ methods by 14.6% mAP on SAM-B and 6.6% on SAM-L.