ID-Selection: Importance-Diversity Based Visual Token Selection for Efficient LVLM Inference

• URL: http://arxiv.org/abs/2604.05601v1
• Date: Tue, 07 Apr 2026 08:52:28 GMT
• Title: ID-Selection: Importance-Diversity Based Visual Token Selection for Efficient LVLM Inference
• Abstract summary: ID-Selection is a simple yet effective token selection strategy for efficient LVLM inference.<n>It preserves informative tokens while reducing redundancy in a unified selection process.<n>Experiments across 5 LVLM backbones and 16 main benchmarks demonstrate that ID-Selection consistently achieves superior performance and efficiency.
• Score: 58.28899599773308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances have explored visual token pruning to accelerate the inference of large vision-language models (LVLMs). However, existing methods often struggle to balance token importance and diversity: importance-based methods tend to retain redundant tokens, whereas diversity-based methods may overlook informative ones. This trade-off becomes especially problematic under high reduction ratios, where preserving only a small subset of visual tokens is critical. To address this issue, we propose ID-Selection, a simple yet effective token selection strategy for efficient LVLM inference. The key idea is to couple importance estimation with diversity-aware iterative selection: each token is first assigned an importance score, after which high-scoring tokens are selected one by one while the scores of similar tokens are progressively suppressed. In this way, ID-Selection preserves informative tokens while reducing redundancy in a unified selection process. Extensive experiments across 5 LVLM backbones and 16 main benchmarks demonstrate that ID-Selection consistently achieves superior performance and efficiency, especially under extreme pruning ratios. For example, on LLaVA-1.5-7B, ID-Selection prunes 97.2% of visual tokens, retaining only 16 tokens, while reducing inference FLOPs by over 97% and preserving 91.8% of the original performance, all without additional training.

Related papers

• Capturing Token Tendencies for Training-Free Token Pruning in Multimodal Large Language Models [86.02826269126308]
  We propose Trend-aware Pruning, a novel framework that elevates pruning from a local snapshot decision to a temporal trajectory modeling problem.<n>This enables a dynamic rectification mechanism that selectively reactivates "late-blooming" tokens, those initially undervalued but exhibiting rising semantic importance, thereby preventing the loss of critical visual cues.
  arXiv  Detail & Related papers  (2026-07-30T15:07:00Z)
• TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference [54.41143483594129]
  Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of visual tokens.<n>Visual token pruning offers a natural solution, yet existing methods are imperfect.<n>We propose TOPS, a training-free and model-agnostic pruning module that can be applied to various MLLMs.
  arXiv  Detail & Related papers  (2026-06-25T15:29:37Z)
• Moving Beyond Diversity: Visual Token Pruning as Subspace Reconstruction for Efficient VLMs [7.371189496638082]
  We introduce SPARE, a subspace reconstruction method that reformulates token pruning as a column subset selection problem.<n>We show that SPARE consistently achieves state-of-the-art performance, with strong gains on tasks.
  arXiv  Detail & Related papers  (2026-06-17T04:45:10Z)
• Stepwise Token Selection for Efficient Multimodal Large Language Models [0.7233065479782755]
  We introduce a pointer-style selection mechanism that iteratively chooses informative tokens, conditioning each decision on previously selected ones, and dynamically determines when to stop.<n>Under aggressive pruning that removes 88.9% of visual tokens, our method preserves 94.6% of the original accuracy while achieving a 1.88x speed-up in prefill latency.
  arXiv  Detail & Related papers  (2026-06-14T23:50:08Z)
• One Layer's Trash is Another Layer's Treasure: Adaptive Layer-wise Visual Token Selection in LVLMs [18.48496973561215]
  Large Vision-Language Models (LVLMs) have achieved remarkable success across diverse multimodal tasks, yet their practical deployment remains constrained by the computational burden arising from lengthy visual tokens.<n>We propose Adaptive Layer-wise Visual Token Selection (ALVTS), a novel framework that breaks away from the conventional static token pruning paradigm.<n>With an 89% token compression ratio, ALVTS retains 96.7% of the original model's accuracy, achieving a superior efficiency-accuracy trade-off for LVLM inference.
  arXiv  Detail & Related papers  (2026-06-12T08:58:58Z)
• TokenSeek: Memory Efficient Fine Tuning via Instance-Aware Token Ditching [82.13572707265513]
  Fine tuning has been regarded as a de facto approach for adapting large language models to downstream tasks.<n>We propose TokenSeek, a universal plugin solution for various transformer-based models through instance-aware token seeking and ditching.
  arXiv  Detail & Related papers  (2026-01-27T15:58:36Z)
• D2Pruner: Debiased Importance and Structural Diversity for MLLM Token Pruning [49.16227597771663]
  D2Pruner is a framework that combines debiased importance with a structural pruning mechanism.<n>It reduces FLOPs by 74.2% while retaining 99.2% of its original performance.<n>It marks a significant advancement with up to 63. 53% improvement over existing methods.
  arXiv  Detail & Related papers  (2025-12-22T14:42:31Z)
• ssToken: Self-modulated and Semantic-aware Token Selection for LLM Fine-tuning [51.133569963553576]
  ssToken is a Self-modulated and Semantic-aware Token Selection approach.<n>We show that both self-modulated selection and semantic-aware selection alone outperform full-data fine-tuning.
  arXiv  Detail & Related papers  (2025-10-21T03:21:04Z)
• Don't Just Chase "Highlighted Tokens" in MLLMs: Revisiting Visual Holistic Context Retention [50.97683288777336]
  Multimodal Large Language Models (MLLMs) suffer from considerable computational overhead due to their reliance on massive visual tokens.<n>Recent studies have explored token pruning to alleviate this problem, which typically uses text-vision cross-attention.<n>We propose HoloV, a plug-and-play visual token pruning framework for efficient inference.
  arXiv  Detail & Related papers  (2025-10-03T11:33:40Z)
• When Less is Enough: Adaptive Token Reduction for Efficient Image Representation [2.2120851074630177]
  We introduce a new method for determining feature utility based on the idea that less valuable features can be reconstructed from more valuable ones.<n>We implement this concept by integrating an autoencoder with a Gumbel-Softmax selection mechanism.<n>Our results highlight a promising direction towards adaptive and efficient multimodal pruning.
  arXiv  Detail & Related papers  (2025-03-20T19:17:08Z)
• Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention [36.90363317158731]
  We propose an adaptive sparse token pruning framework with a minimal cost. Our method improves the throughput of DeiT-S by 50% and brings only 0.2% drop in top-1 accuracy.
  arXiv  Detail & Related papers  (2022-09-28T03:07:32Z)

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.