PEVLM: Parallel Encoding for Vision-Language Models

• URL: http://arxiv.org/abs/2506.19651v3
• Date: Tue, 29 Jul 2025 07:51:37 GMT
• Title: PEVLM: Parallel Encoding for Vision-Language Models
• Authors: Letian Kang, Shixian Luo, Yiqiang Li, Yuxin Yin, Shenxuan Zhou, Xiaoyang Yu, Jin Yang, Yong Wu,
• Abstract summary: We introduce textbfPEVLM, a fine-tuning-free parallel encoding method designed to enhance the prefilling efficiency of Vision-Language Models.<n>PEVLM partitions the input video into context blocks with a shared sink block, while preserving sequential position embeddings to align the attention weight distribution with that of Full-Attention.<n>Experiments demonstrate that PEVLM consistently outperforms existing parallel encoding approaches, achieving up to textbf7.47x speedup in attention computation and reducing end-to-end latency by textbf40%.
• Score: 4.777805570120456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) have demonstrated strong capabilities in multimodal understanding and generation tasks. However, their application to long video understanding remains hindered by the quadratic complexity of standard attention mechanisms. In this work, we introduce \textbf{PEVLM}, a fine-tuning-free parallel encoding method designed to enhance the prefilling efficiency of VLMs in long video scenarios. PEVLM partitions the input video into context blocks with a shared sink block, while preserving sequential position embeddings to align the attention weight distribution with that of Full-Attention. This design reduces attention complexity from $O((T \times N)^2)$ to $O(T \times N)$ where $T$ is the number of frames and $N$ the number of tokens per frame, without sacrificing accuracy. Extensive experiments across multiple state-of-the-art models and benchmarks demonstrate that PEVLM consistently outperforms existing parallel encoding approaches, achieving up to \textbf{7.47x} speedup in attention computation and reducing end-to-end latency by \textbf{40\%}. Remarkably, PEVLM not only maintains high accuracy, but in some settings even surpasses Full-Attention performance. Under strict latency constraints, it achieves substantial gains, improving accuracy from \textbf{23.26\%} to \textbf{61.03\%}. These results underscore the effectiveness of PEVLM for low-latency, long-context video understanding, making it a promising solution for real-world applications.

Related papers

• VLMQ: Efficient Post-Training Quantization for Large Vision-Language Models via Hessian Augmentation [8.891793681316992]
  Post-training quantization (PTQ) has emerged as an effective approach for compressing large models and accelerating their inference without retraining.<n>While PTQ has been extensively studied in the context of large language models (LLMs), its applicability to vision-language models (VLMs) remains underexplored.<n>We propose a novel importance-aware PTQ framework tailored for VLMs, dubbed VLMQ.
  arXiv  Detail & Related papers  (2025-08-05T11:57:03Z)
• Less is More: Token-Efficient Video-QA via Adaptive Frame-Pruning and Semantic Graph Integration [21.69452489173625]
  "Less is more" phenomenon where excessive frames can paradoxically degrade performance due to context dilution.<n>"Visual echoes" yield significant temporal redundancy, which we term 'visual echoes'<n>"AFP" employs an adaptive hierarchical clustering algorithm on a fused ResNet-50 and CLIP feature space to identify and merge these echoes into single representatives.<n>Our full approach demonstrates a drastic reduction in required frames by up to 86.9% and total input tokens by up to 83.2%.
  arXiv  Detail & Related papers  (2025-08-05T11:31:55Z)
• Event-Priori-Based Vision-Language Model for Efficient Visual Understanding [13.540340702321911]
  Event-Priori-Based Vision-Language Model (EP-VLM) improves VLM inference efficiency.<n>EP-VLM uses motion priors derived from dynamic event vision to enhance VLM efficiency.
  arXiv  Detail & Related papers  (2025-06-09T10:45:35Z)
• LiteVLM: A Low-Latency Vision-Language Model Inference Pipeline for Resource-Constrained Environments [3.5132364723753797]
  This paper introduces an efficient Vision-Language Model (VLM) pipeline optimized for deployment on embedded devices.<n>The pipeline significantly reduces the computational overhead by jointly leveraging patch selection to filter irrelevant camera views.
  arXiv  Detail & Related papers  (2025-06-09T04:30:25Z)
• Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding [51.711605076319216]
  Diffusion-based large language models (Diffusion LLMs) have shown promise for non-autoregressive text generation with parallel decoding capabilities.<n>We introduce a novel block-wise approximate KV Cache mechanism tailored for bidirectional diffusion models, enabling cache reuse with negligible performance drop.<n>We propose a confidence-aware parallel decoding strategy that selectively decodes tokens exceeding a confidence threshold, mitigating dependency violations and maintaining generation quality.
  arXiv  Detail & Related papers  (2025-05-28T17:39:15Z)
• AdaTP: Attention-Debiased Token Pruning for Video Large Language Models [73.70241811066907]
  Video Large Language Models (Video LLMs) have achieved remarkable results in video understanding tasks.<n>They often suffer from heavy computational overhead due to the large number of visual tokens generated from multiple video frames.<n>AdaTP integrates two dedicated debiasing modules into the pipeline, targeting global attention bias and local attention bias.
  arXiv  Detail & Related papers  (2025-05-26T15:08:37Z)
• Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs [25.13186579764434]
  We introduce Sparse-to-Dense (StD), a novel decoding strategy that integrates two distinct modules.<n>StD is a tuning-free, plug-and-play solution that achieves up to a 1.94$times$ walltime speedup in video processing.
  arXiv  Detail & Related papers  (2025-05-25T14:09:28Z)
• DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs [124.52164183968145]
  We present DyMU, an efficient, training-free framework that reduces the computational burden of vision-language models (VLMs)<n>Our approach comprises two key components. First, Dynamic Token Merging (DToMe) reduces the number of visual token embeddings by merging similar tokens based on image complexity.<n>Second, Virtual Token Unmerging (VTU) simulates the expected token sequence for large language models (LLMs) by efficiently reconstructing the attention dynamics of a full sequence.
  arXiv  Detail & Related papers  (2025-04-23T18:38:18Z)
• MMInference: Accelerating Pre-filling for Long-Context VLMs via Modality-Aware Permutation Sparse Attention [61.025422435235456]
  MMInference is a dynamic sparse attention method that accelerates the prefilling stage for long-context multi-modal inputs.<n>We show that MMInference accelerates the pre-filling stage by up to 8.3x at 1M tokens while maintaining accuracy.
  arXiv  Detail & Related papers  (2025-04-22T17:59:51Z)
• Neural Discrete Token Representation Learning for Extreme Token Reduction in Video Large Language Models [50.214593234229255]
  We introduce the novel task of Extreme Short Token Reduction, which aims to represent entire videos using a minimal set of discrete tokens.<n>On the Extreme Short Token Reduction task, our VQToken compresses sequences to just 0.07 percent of their original length while incurring only a 0.66 percent drop in accuracy on the NextQA-MC benchmark.
  arXiv  Detail & Related papers  (2025-03-21T09:46:31Z)
• Token-Efficient Long Video Understanding for Multimodal LLMs [101.70681093383365]
  STORM is a novel architecture incorporating a dedicated temporal encoder between the image encoder and the Video-LLMs.<n>We show that STORM achieves state-of-the-art results across various long video understanding benchmarks.
  arXiv  Detail & Related papers  (2025-03-06T06:17:38Z)
• A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs [65.00970402080351]
  A promising approach to accelerating large vision-language models (VLMs) is using partial information, such as attention maps from specific layers, to assess token importance and prune less essential tokens.<n>Our study reveals three key insights: (i) Partial attention information is insufficient for accurately identifying critical visual tokens, resulting in suboptimal performance, especially at low token retention ratios; (ii) Global attention information, such as the attention map aggregated across all layers, more effectively preserves essential tokens and maintains comparable performance under aggressive pruning; and (iii) The global attention map aggregated from a small VLM closely resembles that of a large VLM,
  arXiv  Detail & Related papers  (2024-12-04T13:56:44Z)
• ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification [29.163757099307553]
  The efficiency of large vision-language models (LVLMs) is constrained by the computational bottleneck of the attention mechanism during the prefill phase.<n>We present ZipVL, an efficient inference framework designed for LVLMs through a dynamic ratio allocation strategy of important tokens.
  arXiv  Detail & Related papers  (2024-10-11T07:24:21Z)
• A Training-free Sub-quadratic Cost Transformer Model Serving Framework With Hierarchically Pruned Attention [43.211427581302715]
  We propose Hierarchically Pruned Attention (HiP) to increase context length in large language models.<n>HiP reduces the time complexity of the attention mechanism to $O(T log T)$ and the space complexity to $O(T)$, where $T$ is the sequence length.<n>We show that HiP significantly reduces both prefill and decoding latencies, as well as memory usage, while maintaining high-quality generation with minimal degradation.
  arXiv  Detail & Related papers  (2024-06-14T08:32:45Z)
• An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models [65.37846460916042]
  We find out that the attention computation over visual tokens is of extreme inefficiency in the deep layers of popular LVLMs. We introduce FastV, a versatile plug-and-play method designed to optimize computational efficiency.
  arXiv  Detail & Related papers  (2024-03-11T14:35:32Z)

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.