Queries Knew More Than We Thought: Uncovering Latent Knowledge in Segmentation Models

arXiv:2609.20283 · cs.CV · Submitted 2026-07-30 · Read on arXiv

cs.CV

Submitted: 2026-07-30

Updated: 2026-07-30

Terminology

• Calibration — 27× in this paper · explained in 7 episodes
• Adaptation — 13× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Core — 6× in this paper · explained in 2 episodes
• Frozen model — 4× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 4× in this paper
• Foundation Model — 3× in this paper · explained in 3 episodes
• Abstention — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• In European Conference on Computer Vision — 2× in this paper
• In IEEE International Conference on Computer Vision — 2× in this paper
• Institutional Review Board — 2× in this paper
• LoRA Adaptation — 2× in this paper · explained in 2 episodes
• Mask Generation — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Calibration Failure — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Empirical Upper Bound, Error Diagnosis and Invariance Analysis of Modern Object Detectors
• Align-DETR: Enhancing End-to-end Object Detection with Aligned Loss
• SAM 3: Segment Anything with Concepts
• PaQ-DETR: Learning Pattern and Quality-Aware Dynamic Queries for Object Detection
• Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability
• Revealing the Semantic Selection Gap in DINOv3 through Training-Free Few-Shot Segmentation

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