arXiv (All)
open access
RAO-Nav: Probing Omni-Language Models for Zero-shot Semantic Audio-Visual Navigation
We explore whether Omni-Language Models (OLMs) can be directly applied to zero-shot Semantic Audio-Visual Navigation (SAVN). Recent work demonstrates that even state-of-the-art specialized models still struggle to achieve generalist multimodal navigation, despite extensive task-specific training. In
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
- NaviScale: Generating Large-Scale Semantic Map Datasets for Object Navigation#1063429
- VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs#925861
- Efficient Unified Multimodal Understanding (EUMU): Winning Solution for the MUMU Track at the 8th LSVOS Challenge#1014045
- Human-Centric Image Captioning with Subject-Centered Spatial Understanding#985668
- Valley3: Scaling Omni Foundation Models for E-commerce#1084198
Record · ID 1116555
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