arXiv AI
AdaVSkip: Adaptive Visual Token Skipping Across Layers For Efficient MLLMs Inference
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
Oct 19
CatchUp: Depth-Aware Re-Admission of Visual Tokens for Efficient Large Vision-Language Models
CatchUp is a training‑free framework that improves efficiency in large vision‑language models by depth‑aware re‑admission of visual tokens. It repeatedly evaluates query‑conditioned relevance, processes only selected tokens per decoder interval, and preserves deferred tokens for later re‑admission, using reference‑to‑current transition signals to approximate missed representation evolution. Experiments on three referring‑expression grounding datasets and six multimodal benchmarks show that CatchUp retains over 92% of full‑model performance with 88.9% fewer active tokens and reduces end‑to‑end latency by 8.0% at a 64‑token budget.
arXiv AI
Jun 11
Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models
arXiv:2606. 12412v1 Announce Type: cross Abstract: Vision-language models (VLMs) project images into hundreds to thousands of visual tokens, making decoder inference expensive in both attention computation and KV-cache memory.
arXiv AI
Oct 28
LearnPrunerV2: Learnable Progressive Token Pruning for Vision-Language Models
LearnPrunerV2 is a fully learnable token‑pruning framework for Vision‑Language Models that replaces heuristic pruning with lightweight modules in every backbone layer. These modules estimate token importance by combining visual saliency and query relevance via a shadow token, revealing a consistent staircase redundancy pattern across layers. The method introduces Budget‑Deterministic Pruning to set fixed per‑layer budgets, achieving state‑of‑the‑art efficiency while preserving 99.8% of performance even after pruning 77.8% of visual tokens on LLaVA‑v1.5.
Hugging Face Trending Papers
Oct 27
LearnPrunerV2: Learnable Progressive Token Pruning for Vision-Language Models
LearnPrunerV2 introduces a fully learnable token pruning framework for Vision‑Language Models, embedding lightweight modules in every backbone layer to estimate token importance by combining visual saliency and query relevance via a shadow token. It identifies a consistent staircase redundancy pattern across layers and employs Budget‑Deterministic Pruning to set fixed per‑layer budgets, yielding a hardware‑friendly pruning schedule. Experiments show that LearnPrunerV2 outperforms prior methods, preserving 99.8% of performance while removing 77.8% of visual tokens on LLaVA‑v1.5, thus achieving a superior accuracy‑efficiency trade‑off.
arXiv AI
Jul 1
Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inference
arXiv:2606. 31903v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) increasingly process long visual-token sequences, increasing the overall inference computation.
arXiv AI
Jun 9
Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation
arXiv:2606. 09131v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) commonly inherit the deep, symmetric Transformer backbone designed for unimodal text modeling, and apply the same computation uniformly to image and language tokens.