arXiv (All)
open access
What Drives Hierarchy-Aware Image Retrieval? Taxonomy Alignment, Objective Choice, and Geometry
Foundation vision models provide strong generic representations, yet high class-level retrieval accuracy does not necessarily imply that an embedding respects a target semantic taxonomy. We study strict explicit-taxonomy image retrieval on frozen DINOv2 features and ask: when hierarchical retrieval
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Human-Grounded Calibration for Long-Text Image-Text Congruence in Vision-Language Models#1109621
- State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models#792946
- State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models#788942
- InfoTaxa: Information-Calibrated Label-Free Clustering for Fine-Grained Visual Taxonomy#1123600
- Text-Video Retrieval via Multi-Dimensional Saliency Assessment and Granularity-Aware Query Decomposition#1143916
Record · ID 1061275
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive