A semi-supervised framework distills vision foundation models into compact instance segmentation experts that outperform their teachers by up to 11.9 AP on Cityscapes and 8.6 AP on ADE20K while being 11 times smaller.
Unbiased teacher for semi-supervised object detection
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MS-DePro achieves state-of-the-art performance on multi-source domain adaptation benchmarks for object detection by using depth-guided region proposals and multi-modal alignment of learnable text embeddings.
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Training a Student Expert via Semi-Supervised Foundation Model Distillation
A semi-supervised framework distills vision foundation models into compact instance segmentation experts that outperform their teachers by up to 11.9 AP on Cityscapes and 8.6 AP on ADE20K while being 11 times smaller.
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Multi-Modal Guided Multi-Source Domain Adaptation for Object Detection
MS-DePro achieves state-of-the-art performance on multi-source domain adaptation benchmarks for object detection by using depth-guided region proposals and multi-modal alignment of learnable text embeddings.