arXiv Computer Vision
Oct 9

ERASE: Eliminating Redundant Visual Tokens via Adaptive Two-Stage Token Pruning

ERASE is an adaptive two-stage token pruning framework designed to reduce computational overhead in Vision‑Language Models by eliminating redundant visual tokens. Stage 1 removes image‑level redundancy using lightweight raw‑image statistics, while Stage 2 progressively prunes instruction‑irrelevant tokens across decoder layers. Experiments on Qwen2.5‑VL‑7B show that ERASE retains 95.70 % of the original accuracy while keeping only 25 % of the tokens.

By Yuna Lee, Kyoungho Min, Yulhwa Kim
Hugging Face Trending Papers
Aug 3

CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models

In video understanding, vision-language models (VLMs) must ingest massive numbers of visual tokens, causing the computational and memory cost of the prefill stage to rise sharply. Such visual sequences are highly redundant along the spatio-temporal dimension, yet a high compression ratio is often accompanied by the loss of critical details.

arXiv AI
2d ago

Just Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning

The paper introduces Varest, a method for image-token compression that reduces computational load while preserving semantic integrity. Varest uses variable-resolution tokenization guided by semantic fungibility, merging tokens within coherent regions coarsely and maintaining finer distinctions at object boundaries and text. After early decoder processing, learned relevance registers prune visual tokens, enabling a single checkpoint to perform well across multiple budgets and backbones, achieving 86.10‑92.33% of full-token performance with only 5.56% of the original token count.

By Tianhao Fu, Austin Wang, Charles Chen, Roby Aldave-Garza, Victor Xie, Xiyue Jiao, Yucheng Chen, Martin Tin