AdaTP: Attention-Debiased Token Pruning for Video Large Language Models

• URL: http://arxiv.org/abs/2505.20100v1
• Date: Mon, 26 May 2025 15:08:37 GMT
• Title: AdaTP: Attention-Debiased Token Pruning for Video Large Language Models
• Abstract summary: 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.
• Score: 73.70241811066907
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video Large Language Models (Video LLMs) have achieved remarkable results in video understanding tasks. However, they often suffer from heavy computational overhead due to the large number of visual tokens generated from multiple video frames. Existing visual token compression methods often rely on attention scores from language models as guidance. However, these scores exhibit inherent biases: global bias reflects a tendency to focus on the two ends of the visual token sequence, while local bias leads to an over-concentration on the same spatial positions across different frames. To address the issue of attention bias, we propose $\textbf{A}$ttention-$\textbf{D}$ebi$\textbf{a}$sed $\textbf{T}$oken $\textbf{P}$runing for Video Large Language Models ($\textbf{AdaTP}$), a novel token pruning pipeline for Video LLMs. AdaTP integrates two dedicated debiasing modules into the pipeline, targeting global attention bias and local attention bias, respectively. Without the need for additional training, our method significantly reduces the computational overhead of Video LLMs while retaining the performance of vanilla models. Extensive evaluation shows that AdaTP achieves state-of-the-art performance in various commonly used video understanding benchmarks. In particular, on LLaVA-OneVision-7B, AdaTP maintains performance without degradation while using only up to $27.3\%$ FLOPs compared to the vanilla model. Our code will be released soon.

Related papers

• Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation [3.6330067010404705]
  Large Vision-Language Models (LVLMs) extend large language models with visual understanding, but remain vulnerable to hallucination.<n>Recent studies link this issue to language bias-the tendency of LVLMs to over-rely on text while neglecting visual inputs.<n>We provide a systematic study of language bias and identify its root in modality misalignment during training.
  arXiv  Detail & Related papers  (2026-05-24T12:23:13Z)
• PIO-FVLM: Rethinking Training-Free Visual Token Reduction for VLM Acceleration from an Inference-Objective Perspective [59.24570811503256]
  We propose PIO-FVLM to reduce redundant visual tokens in vision-models (VLMs) to accelerate inference.<n>The proposed PIO-FVLM is training-free and compatible with FlashAttention, friendly to practical application and deployment.<n>On LLaVA-Next-7B, PIO-FVLM retains just 11.1% of visual tokens but maintains 97.2% of the original performance.
  arXiv  Detail & Related papers  (2026-02-04T15:33:10Z)
• Seeing the Forest and the Trees: Query-Aware Tokenizer for Long-Video Multimodal Language Models [24.875526594002434]
  We present QTSplus, a visual token selection module for long video understanding scenarios.<n>It is integrated into Qwen2.5-VL and compresses the vision stream by up to textbf89% and reduces end-to-end latency by textbf28% on long videos.<n>Results show that QTSplus is an effective, general mechanism for scaling MLLMs to realworld long-video scenarios.
  arXiv  Detail & Related papers  (2025-11-14T22:41:27Z)
• Pyramid Token Pruning for High-Resolution Large Vision-Language Models via Region, Token, and Instruction-Guided Importance [60.028070589466445]
  Pyramid Token Pruning (PTP) is a training-free strategy that hierarchically integrates bottom-up visual saliency at both region and token levels with top-down instruction-guided relevance.<n>We show that PTP substantially reduces computational cost, memory usage, and inference latency, with negligible performance degradation.
  arXiv  Detail & Related papers  (2025-09-19T07:28:17Z)
• Variation-aware Vision Token Dropping for Faster Large Vision-Language Models [24.952668143243542]
  Large vision-language models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding tasks.<n> Token compression offers a direct solution by reducing the number of tokens to be processed, thereby improving computational efficiency.<n>We propose Variation-aware Vision Token Dropping (textiti.e., textbfV$2$Drop), which progressively removes visual tokens with minimal variation during LVLM inference.
  arXiv  Detail & Related papers  (2025-09-01T15:28:44Z)
• Video-LevelGauge: Investigating Contextual Positional Bias in Large Video Language Models [51.67019924750931]
  Video-LevelGauge is a benchmark designed to assess positional bias in large video language models (LVLMs)<n>We employ standardized probes and customized contextual setups, allowing flexible control over context length, probe position, and contextual types.<n>Our benchmark comprises 438 manually curated videos spanning multiple types, yielding 1,177 high-quality multiple-choice questions and 120 open-ended questions.
  arXiv  Detail & Related papers  (2025-08-27T07:58:16Z)
• PEVLM: Parallel Encoding for Vision-Language Models [4.777805570120456]
  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%.
  arXiv  Detail & Related papers  (2025-06-24T14:14:52Z)
• REEF: Relevance-Aware and Efficient LLM Adapter for Video Understanding [2.309018557701645]
  Recent methods often compress memory banks to handle untemporal videos for video-level understanding.<n>To this, we designed video to compress un videos on a large scale using visual tokens.
  arXiv  Detail & Related papers  (2025-04-07T20:36:34Z)
• FastVID: Dynamic Density Pruning for Fast Video Large Language Models [38.267065642416554]
  We propose Density Pruning for Fast Video LLMs termed FastVID.<n>FastVID partitions videos into temporally ordered segments to preserve temporal structure.<n>Our method significantly reduces computational overhead while maintaining temporal and visual integrity.
  arXiv  Detail & Related papers  (2025-03-14T08:33:08Z)
• QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video Comprehension [86.0749609778104]
  We propose QuoTA, an ante-hoc training-free modular that extends existing large video-language models.<n>QuoTA strategically allocates frame-level importance scores based on query relevance.<n>We decouple the query through Chain-of-Thoughts reasoning to facilitate more precise LVLM-based frame importance scoring.
  arXiv  Detail & Related papers  (2025-03-11T17:59:57Z)
• Rethinking Homogeneity of Vision and Text Tokens in Large Vision-and-Language Models [29.611769371733672]
  We propose De Attention (D-Attn), a novel method that processes visual and textual embeddings differently.<n>D-Attn diagonalizes visual-to-visual self-attention, reducing computation from $mathcalO(|V|2)$ to $mathcalO(|V|)$ for $|V|$ visual embeddings without compromising performance.
  arXiv  Detail & Related papers  (2025-02-04T00:46:11Z)
• ST$^3$: Accelerating Multimodal Large Language Model by Spatial-Temporal Visual Token Trimming [14.937905258757635]
  $textbfST3$ is a framework designed to accelerate MLLM inference without retraining.<n>$textbfST3$ can be seamlessly integrated into existing pre-trained MLLMs.
  arXiv  Detail & Related papers  (2024-12-28T10:17:29Z)
• Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance [67.26434607115392]
  Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. LVLMs suffer from hallucinations caused by language bias, leading to diminished focus on images and ineffective visual comprehension. We propose LACING to address the language bias of LVLMs with muLtimodal duAl-attention meChanIsm (MDA) aNd soft-image Guidance (IFG)
  arXiv  Detail & Related papers  (2024-11-21T16:33:30Z)
• Inference Optimal VLMs Need Fewer Visual Tokens and More Parameters [54.01228554126122]
  Vision Language Models (VLMs) have demonstrated strong capabilities across various visual understanding and reasoning tasks.<n>To reduce inference costs, one can either downsize the Large Language Models (LLMs) or reduce the number of input tokens needed to represent the image.<n>We take the first steps toward designing token compression algorithms tailored for high-compression settings.
  arXiv  Detail & Related papers  (2024-11-05T18:54:21Z)
• Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models [15.53216696218776]
  We explore the internal mechanisms of how bias emerges in large language models (LLMs) when provided with ambiguous comparative prompts. We propose $textttATLAS$, a technique to localize bias to specific layers of the LLM by analyzing attention scores and then reduce bias by scaling attention in these biased layers.
  arXiv  Detail & Related papers  (2024-10-29T20:15:56Z)
• VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation [66.00245701441547]
  We introduce a novel approach to reduce vision compute by leveraging redundant vision tokens "skipping layers" rather than decreasing the number of vision tokens. Our method, VideoLLM-MoD, is inspired by mixture-of-depths LLMs and addresses the challenge of numerous vision tokens in long-term or streaming video.
  arXiv  Detail & Related papers  (2024-08-29T17:21:58Z)
• Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks [76.35271072704384]
  Deep learning models perform poorly when applied to videos with rare scenes or objects. We tackle this problem from two different angles: algorithm and dataset. We show that the debiased representation can generalize better when transferred to other datasets and tasks.
  arXiv  Detail & Related papers  (2022-09-20T00:30:35Z)
• Video Understanding as Machine Translation [53.59298393079866]
  We tackle a wide variety of downstream video understanding tasks by means of a single unified framework. We report performance gains over the state-of-the-art on several downstream tasks including video classification (EPIC-Kitchens), question answering (TVQA), captioning (TVC, YouCook2, and MSR-VTT)
  arXiv  Detail & Related papers  (2020-06-12T14:07:04Z)

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.