mlsanityData to 7 Oct 2026

Beyond Resolution: Object-to-Image Ratio Mismatch in Instance Retrieval

arXiv 2610.11489 ↗First posted 8 Oct 2026cs.CV cs.IRcited by 0

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Abstract (arXiv)

Visual instance retrieval often fails when the same object appears at different apparent sizes in the query and gallery. We show that the dominant cause is usually not resolution loss but object-to-image (O2I) ratio mismatch: the object occupies different fractions of the two images. On a controlled benchmark of 3,021 Objaverse objects rendered at five camera distances, more than 80% of the cross-distance degradation is attributable to O2I mismatch rather than resolution for 9 of 12 pretrained backbones; multi-scale architectures cut the resolution-only effect to single digits yet remain equally susceptible. The failure is also asymmetric: tight queries retrieve more reliably against wide gallery images than the reverse. Guided by this analysis, query-side scale augmentation and an OWLv2 crop reranker reach state of the art on ILIAS 100M (29.2 mAP@1000 before reranking, 42.0 after) without training or modifying the precomputed gallery index, and a LoRA fine-tune matches the query-side gains at a single forward pass, showing that O2I robustness is learnable.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 38 references that are in the map. First posted on arXiv 8 Oct 2026.

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4 of 38 references.

References · 4

  1. 2025DINOv3cited by 1,297
  2. 2025SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Featurescited by 1,127
  3. 2019Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Researchcited by 98
  4. 2019Needles in Haystacks: On Classifying Tiny Objects in Large Imagescited by 13

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