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

arXiv:2610.11489 · cs.CV, cs.IR · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.IR

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Benchmark — 31× in this paper · explained in 2 episodes
• Robustness — 16× in this paper · explained in 5 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Cosine Similarity — 7× in this paper · explained in 3 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Grounding — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 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
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Masked Scale Augmentation — 1× in this paper
• Quantization — 1× in this paper · explained in 12 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
• Needles in Haystacks: On Classifying Tiny Objects in Large Images
• DINOv3
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

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