arXiv (All)
open access
IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models
Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens. Existing token pruning methods mitigate this issue through empirical approaches while overlooking the internal mechanism o
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
- SinkPruner: Sink-Free Visual Token Pruning for Multimodal Large Language Models#820996
- Multi-Image Visual Token Pruning in Large Visual Language Models#820643
- EntropyPrune: Matrix Entropy Guided Visual Token Pruning for Multimodal Large Language Models#974331
- Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval#974486
- S$^2$Prune: Spatially Structured Visual Token Pruning for Multimodal Large Language Models#821181
Record · ID 820406
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