VLA-InfoEntropy: A Training-Free Vision-Attention Information Entropy Approach for Vision-Language-Action Models Inference Acceleration and Success

• URL: http://arxiv.org/abs/2604.05323v1
• Date: Tue, 07 Apr 2026 01:52:42 GMT
• Title: VLA-InfoEntropy: A Training-Free Vision-Attention Information Entropy Approach for Vision-Language-Action Models Inference Acceleration and Success
• Abstract summary: Vision-Language-Action (VLA) models integrate visual perception, language understanding, and action decision-making for cross-modal semantic alignment.<n>To address this issue, we use image entropy to quantify the grayscale distribution characteristics of each visual token.<n>We show that our method reduces inference parameters, accelerates inference speed, and outperforms existing approaches.
• Score: 33.810242609054924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models integrate visual perception, language understanding, and action decision-making for cross-modal semantic alignment, exhibiting broad application potential. However, the joint processing of high-dimensional visual features, complex linguistic inputs, and continuous action sequences incurs significant computational overhead and low inference efficiency, thereby hindering real-time deployment and reliability. To address this issue, we use image entropy to quantify the grayscale distribution characteristics of each visual token and introduce attention entropy to capture the distribution of attention scores over task-related text. Visual entropy identifies texture-rich or structurally informative regions, while attention entropy pinpoints semantically relevant tokens. Combined with timestep information, these metrics enable a dynamic transition strategy that shifts the model's focus from global visual features to attention-guided local informative regions. Thus, the resulting VLA-InfoEntropy method integrates spatial, semantic, and temporal cues to reduce redundancy while preserving critical content. Extensive experiments show that our method reduces inference parameters, accelerates inference speed, and outperforms existing approaches.

Related papers

• Attention Dynamics in Diffusion Models: A Visual Analytics Framework for Human-AI Collaboration [1.7188280334580195]
  We present a visual analytics framework for exploring attention dynamics in diffusion models.<n>Our approach enables structured analysis of attention behavior across generation steps.<n>Case studies show how linked temporal and spatial views facilitate the observation and discussion of generative processes.
  arXiv  Detail & Related papers  (2026-06-28T23:11:22Z)
• ViQ: Text-Aligned Visual Quantized Representations at Any Resolution [91.46185855575789]
  We present ViQ, a Visual Quantized Representations framework, which is designed to balance semantics and details in discrete representations.<n>Our approach structures quantization learning into two stages: text-aligned pre-training and feature discretization.<n>Experiments on multimodal tasks demonstrate that ViQ achieves competitive performance compared to state-of-the-art multimodal vision encoders.
  arXiv  Detail & Related papers  (2026-06-25T17:29:27Z)
• MSACT: Multistage Spatial Alignment for Stable Low-Latency Fine Manipulation [4.439585594082787]
  Real-world fine manipulation, particularly in bimanual manipulation, requires low-latency control and stable visual localization.<n>Existing approaches make different trade-offs: action-chunking policies such as ACT enable low-latency execution and data efficiency but rely on dense visual features without explicit spatial consistency.<n>We introduce a multistage spatial attention module that extracts stable 2D attention points and jointly predicts future attention sequences with a temporal alignment loss.
  arXiv  Detail & Related papers  (2026-05-01T07:35:15Z)
• From Attenuation to Attention: Variational Information Flow Manipulation for Fine-Grained Visual Perception [12.4823697158657]
  Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in general visual understanding.<n>They frequently falter in fine-grained perception tasks that require identifying tiny objects or discerning subtle visual relationships.<n>We attribute this limitation to Visual Attenuation: a phenomenon where sparse fine-grained visual signals are prematurely suppressed or diluted by dominant textual tokens during network propagation.<n>Existing input-centric solutions fail to fundamentally reverse this intrinsic mechanism of information loss.<n>We propose the Variational Information Flow (VIF) framework to address this challenge.
  arXiv  Detail & Related papers  (2026-04-14T09:32:13Z)
• VISion On Request: Enhanced VLLM efficiency with sparse, dynamically selected, vision-language interactions [51.41587958253802]
  We introduce VISion On Request (VISOR), a method that reduces inference cost without discarding visual information.<n>VISOR improves efficiency by sparsifying the interaction between image and text tokens.<n>Experiments show that VISOR drastically reduces computational cost while matching or exceeding state-of-the-art results.
  arXiv  Detail & Related papers  (2026-03-24T17:58:17Z)
• Online Segment Any 3D Thing as Instance Tracking [60.20416622842975]
  We reconceptualize online 3D segmentation as an instance tracking problem (AutoSeg3D)<n>We introduce spatial consistency learning to mitigate the fragmentation problem inherent in Vision Foundation Models.<n>Our method establishes a new state-of-the-art, surpassing ESAM by 2.8 AP on ScanNet200.
  arXiv  Detail & Related papers  (2025-12-08T14:48:51Z)
• Generalized Decoupled Learning for Enhancing Open-Vocabulary Dense Perception [71.26728044621458]
  DeCLIP is a novel framework that enhances CLIP by decoupling the self-attention module to obtain content'' and context'' features respectively.<n>It consistently achieves state-of-the-art performance across a broad spectrum of tasks, including 2D detection and segmentation, 3D instance segmentation, video instance segmentation, and 6D object pose estimation.
  arXiv  Detail & Related papers  (2025-08-15T06:43:51Z)
• Make It Efficient: Dynamic Sparse Attention for Autoregressive Image Generation [8.624395048491275]
  We propose a novel training-free context optimization method called Adaptive Dynamic Sparse Attention (ADSA)<n>ADSA identifies historical tokens crucial for maintaining local texture consistency and those essential for ensuring global semantic coherence, thereby efficiently streamlining attention.<n>We also introduce a dynamic KV-cache update mechanism tailored for ADSA, reducing GPU memory consumption during inference by approximately $50%$.
  arXiv  Detail & Related papers  (2025-06-23T01:27:06Z)
• Mitigating Hallucination in Large Vision-Language Models through Aligning Attention Distribution to Information Flow [9.561772135477883]
  LVLMs (Large Vision-Language Models) follow the same architecture, with visual information gradually integrated into semantic representations.<n>We observe that the model's attention distribution does not exhibit sufficient emphasis on semantic representations.<n>This misalignment undermines the model's visual understanding ability and contributes to hallucinations.
  arXiv  Detail & Related papers  (2025-05-20T12:10:13Z)
• Top-Down Compression: Revisit Efficient Vision Token Projection for Visual Instruction Tuning [70.57180215148125]
  Visual instruction tuning aims to enable large language models to comprehend the visual world.<n>Existing methods often grapple with the intractable trade-off between accuracy and efficiency.<n>We present LLaVA-Meteor, a novel approach that strategically compresses visual tokens without compromising core information.
  arXiv  Detail & Related papers  (2025-05-17T10:22:29Z)
• Variational Structured Attention Networks for Deep Visual Representation Learning [49.80498066480928]
  We propose a unified deep framework to jointly learn both spatial attention maps and channel attention in a principled manner. Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework. We implement the inference rules within the neural network, thus allowing for end-to-end learning of the probabilistic and the CNN front-end parameters.
  arXiv  Detail & Related papers  (2021-03-05T07:37:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.