arXiv (All)
open access
AVOC: Enhancing Hour-Level Audio-Video Understanding in Omni-Modal LLMs via Retrieval-Inspired Token Compression
Multimodal Large Language Models have achieved remarkable progress in short-form audio-video understanding, yet long-form audio-video comprehension remains challenged by limited context windows and severe information redundancy. To address these bottlenecks, we propose AVOC, a framework for long-for
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
- Rethinking Visual Token Compression for Video Large Language Models: A Simple Yet Strong Baseline#1129983
- OmniSelect: Dynamic Modality-Aware Token Compression for Efficient Omni-modal Large Language Models#1354907
- Modularized Dynamic-Granularity Video LLM for Multi-Event Long Video Understanding#381795
- AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs#1297952
- AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs#1291851
Record · ID 1202121
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