arXiv (All)
open access
TACO: Training-free Sound Prompted Segmentation via Semantically Constrained Audio-visual CO-factorization
Large-scale pre-trained audio and image models demonstrate an unprecedented degree of generalization, making them suitable for a wide range of applications. Here, we tackle the specific task of sound-prompted segmentation, aiming to segment image regions corresponding to objects heard in an audio si
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
- Microsoft COCO: Common Objects in Context#6287
- Binaural Audio-Visual Instance Segmentation#1116511
- EvoAudio: Recursive Self-Improvement for Audio Understanding#1063600
- AVSD-Scenes: A Dataset for Audio-Visual Description of Urban Scenes#1157132
- RoboGesture: Real-Time Semantic-aligned Co-Speech Gestures Generation for Humanoid Interaction#792941
Record · ID 1129047
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