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Research questionHow can frozen dense-prediction models remain robust under distribution shift without retraining or changing their prediction heads?Distribution shifts can degrade dense visual predictions even when a pretrained model remains unchanged. Inference-time refinement must improve shifted-image features without disrupting the patch relationships that support spatial structure.
AI
Computer Vision
Image & Video Processing
Inference Optimization
Machine Learning
Latest papersRecent research connected to this question, newest first.GramLoop: Training-Free Gram-Gated Replay for Robust Dense PredictionThe evidence concerns DINOv3 dense-prediction models using training-free replay and cosine-Gram consistency gating. Results cover object detection and semantic segmentation across corruptions, perturbations, and natural shifts on five shifted benchmarks, with clean ADE20K performance preserved.research paper · Sep 3, 2026
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