MINER: Multi-crop INference-time Enhancement for Rare-Object Retrieval with Frozen Dual Encoders

arXiv:2609.27142 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/aalquwayfili/MINER

Terminology

• Benchmark — 16× in this paper · explained in 2 episodes
• Embeddings — 13× in this paper · explained in 2 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Ablation — 4× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Rare Objects in Cluttered Scenes — 3× in this paper
• CVF International Conference on Computer Vision — 2× in this paper
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• European Conference on Computer Vision — 2× in this paper
• Explainability — 2× in this paper · explained in 3 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• In International Conference on Learning Representations — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• SAM 3: Segment Anything with Concepts
• One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness
• DINOv3
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

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