arXiv (All)
open access
HOIBlender: Blending Lightweight Detection with Vision-Language Priors for Efficient Human-Object Interaction Detection
Human-object interaction (HOI) detection requires grounding an interacting human-object pair and recognizing the verb that links them, often under severe long-tail supervision. Recent methods improve accuracy with stronger detectors and vision-language priors, but many still stack heavy transformer
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
- PIEDet: Prototype-Driven Intrinsically Explainable Object Detection#609914
- Track, Articulate, Act: Generating Articulation from Casual Human Videos#1013325
- Event-Driven Refresh and Recurrence Memory to Reduce Stale Grounding in Referring Video Object Segmentation#1143359
- EvoScene-VLA: Evolving Scene Beliefs Inside the Action Decoder for Chunked Robot Control#609945
- EvoScene-VLA: Evolving Scene Beliefs Inside the Action Decoder for Chunked Robot Control#608883
Record · ID 1049172
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