arXiv (All)
open access
Semantic Modality Compensation for Unsupervised Visible-Infrared Person Re-identification under Unpaired Settings
Unsupervised visible-infrared person re-identification (USL-VI-ReID) learns person representations that can be compared across modalities without identity annotations. In the unpaired setting, however, identity correspondences between modalities are often incomplete, leaving many identities without
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
- What Does It Mean to Forget a Person? Individual-Level Unlearning in Vision-Language Models#1118193
- $C^3$ASD: Multi-Level Consistency-Driven Representation Learning for Robust Active Speaker Detection#1117496
- AIM: Anchor Identity Features, Then Match for Multimodal Large Language Model Unlearning#792325
- AIM: Anchor Identity Features, Then Match for Multimodal Large Language Model Unlearning#788325
- ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning#163968
Record · ID 1119367
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