SparseSAM achieves 2x faster inference and 2.8x memory reduction in SAM with only 0.004 mIoU loss at 0.4 density via Stripe-Sort Attention and Residual-Consistency MLP.
EfficientSAM: leveraged masked image pretraining for efficient segment anything
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Grounded SAM integrates Grounding DINO and SAM to support text-prompted open-world detection and segmentation, achieving 48.7 mean AP on SegInW zero-shot with the base detector and huge segmenter.
Distilled SAM 3 and DINOv3 models deliver near-teacher accuracy in pig tracking (92.29% MOTA, 96.15% IDF1) and behavior classification while achieving 7.77x parameter reduction and fitting on Jetson Orin NX with headroom.
GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.
citing papers explorer
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SparseSAM: Structured Sparsification of Activations in Segment Anything Models
SparseSAM achieves 2x faster inference and 2.8x memory reduction in SAM with only 0.004 mIoU loss at 0.4 density via Stripe-Sort Attention and Residual-Consistency MLP.
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Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
Grounded SAM integrates Grounding DINO and SAM to support text-prompted open-world detection and segmentation, achieving 48.7 mean AP on SegInW zero-shot with the base detector and huge segmenter.
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Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics
Distilled SAM 3 and DINOv3 models deliver near-teacher accuracy in pig tracking (92.29% MOTA, 96.15% IDF1) and behavior classification while achieving 7.77x parameter reduction and fitting on Jetson Orin NX with headroom.
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Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement
GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.