arXiv (All)
open access
Preserve-and-Compose Training for Composed Image Retrieval
Composed image retrieval (CIR) aims to retrieve images that satisfy a user-specified modification while preserving relevant visual content from a reference image. Collecting target images for this purpose is costly, motivating zero-shot CIR methods that instead use target captions as supervision. Ho
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
- Who Remains, What Changes: Identity Anchored Composed Gait Retrieval#819472
- Who Remains, What Changes: Identity Anchored Composed Gait Retrieval#809476
- VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations#1155929
- NoisEasier: Test-Time Noise Optimization for Text-to-Video Generation#799415
- Adaptive Cortically Constrained EEG-Vision Alignment for Zero-Shot Brain-to-Image Retrieval#1050295
Record · ID 1105760
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