arXiv (OAI Expanded)
open access
Augmenting Large Audio-Language Models with Frame-Level Grounding for Fine-Grained Temporal Perception
Large Audio-Language Models (LALMs) have substantially advanced general audio understanding, yet they remain limited in fine-grained temporal perception, particularly in precise event localization. Existing approaches primarily post-train LALMs to predict event boundaries as timestamp tokens. Howeve
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
- Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt#14079
- MusTBench: Benchmarking and Advancing Temporal Grounding in Music LLMs#821455
- TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models#799245
- TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models#797822
- Salt++: Context-Aligned Post-Training for Few-Step Streaming Multimodal Generation#1132196
Record · ID 1109260
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