Retrieval Replace Reduction: An effective visual token reduction method via semantic match

• URL: http://arxiv.org/abs/2410.07278v1
• Date: Wed, 9 Oct 2024 07:13:22 GMT
• Title: Retrieval Replace Reduction: An effective visual token reduction method via semantic match
• Authors: Yingen Liu, Fan Wu, Ruihui Li, Zhuo Tang, Kenli Li,
• Abstract summary: We introduce textbfTRSM (textbfToken textbfReduction via textbfSemantic textbfMatch), which effectively reduces the number of visual tokens without compromising MLLM performance. Inspired by how humans process multimodal tasks, TRSM leverages semantic information from one modality to match relevant semantics in another, reducing the number of visual tokens. Based on experimental results, our approach compresses the visual tokens by 20%, achieving comparable performance across diverse visual question-answering and reasoning tasks.
• Score: 32.33892531885448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models (MLLMs) have demonstrated strong performance across various tasks without requiring training from scratch. However, they face significant computational and memory constraints, particularly when processing multimodal inputs that exceed context length, limiting their scalability. In this paper, we introduce a new approach, \textbf{TRSM} (\textbf{T}oken \textbf{R}eduction via \textbf{S}emantic \textbf{M}atch), which effectively reduces the number of visual tokens without compromising MLLM performance. Inspired by how humans process multimodal tasks, TRSM leverages semantic information from one modality to match relevant semantics in another, reducing the number of visual tokens.Specifically, to retain task relevant visual tokens, we use the text prompt as a query vector to retrieve the most similar vectors from the visual prompt and merge them with the text tokens. Based on experimental results, when applied to LLaVA-1.5\cite{liu2023}, our approach compresses the visual tokens by 20\%, achieving comparable performance across diverse visual question-answering and reasoning tasks.

Related papers

• Balancing Performance and Efficiency: A Multimodal Large Language Model Pruning Method based Image Text Interaction [6.467840081978855]
  multimodal large language models (MM-LLMs) have achieved great success in many multimodal tasks, but their high computational costs limit their further promotion and application. We studied the visual tokens of MM-LLMs and designed a dynamic pruning algorithm to address this issue. Our proposed method can achieve performance that competes with the original performance when using an average of 22% of the original token quantity.
  arXiv  Detail & Related papers  (2024-09-02T10:49:10Z)
• ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models [73.34709921061928]
  We propose a training-free method to inject visual referring into Multimodal Large Language Models (MLLMs) We observe the relationship between text prompt tokens and visual tokens in MLLMs, where attention layers model the connection between them. We optimize a learnable visual token based on an energy function, enhancing the strength of referential regions in the attention map.
  arXiv  Detail & Related papers  (2024-07-31T11:40:29Z)
• Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens [21.61634020256455]
  Transformer-based large language models (LLMs) suffer a performance degradation when modeling long-term contexts. We propose a simple yet effective method to enable LLMs to take a deep breath, encouraging them to summarize information contained within discrete text chunks.
  arXiv  Detail & Related papers  (2024-06-16T15:50:10Z)
• Towards Semantic Equivalence of Tokenization in Multimodal LLM [149.11720372278273]
  Vision tokenization is essential for semantic alignment between vision and language. This paper proposes a novel dynamic Semantic-Equivalent Vision Tokenizer (SeTok) SeTok groups visual features into semantic units via a dynamic clustering algorithm. The resulting vision tokens effectively preserve semantic integrity and capture both low-frequency and high-frequency visual features.
  arXiv  Detail & Related papers  (2024-06-07T17:55:43Z)
• Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference [59.91176945361035]
  We introduce Visual Tokens Withdrawal (VTW), a plug-and-play module to boost MLLMs for rapid inference. Our approach is inspired by two intriguing phenomena we have observed. Our VTW approach can cut computational overhead by over 40% across diverse multimodal tasks while maintaining performance.
  arXiv  Detail & Related papers  (2024-05-09T14:38:53Z)
• LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models [35.88374542519597]
  Large Multimodal Models (LMMs) have shown significant visual reasoning capabilities by connecting a visual encoder and a large language model. Recent LMMs incorporate more complex visual inputs, such as high-resolution images and videos, which further increases the number of visual tokens significantly. We propose PruMerge, a novel adaptive visual token reduction strategy that significantly reduces the number of visual tokens without compromising the performance of LMMs.
  arXiv  Detail & Related papers  (2024-03-22T17:59:52Z)
• Identifying and Analyzing Task-Encoding Tokens in Large Language Models [55.03191279766383]
  In this paper, we identify and analyze task-encoding tokens on whose representations the task performance depends. We show that template and stopword tokens are the most prone to be task-encoding. Our work sheds light on how large language models (LLMs) learn to perform a task from demonstrations, deepens our understanding of the varied roles different types of tokens play in LLMs, and provides insights for avoiding instability from improperly utilizing task-encoding tokens.
  arXiv  Detail & Related papers  (2024-01-20T20:55:21Z)
• Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens [76.40196364163663]
  We propose a learning-based vision-language pre-training approach, such as CLIP. We show that our method can learn more comprehensive representations and capture meaningful cross-modal correspondence.
  arXiv  Detail & Related papers  (2023-03-27T00:58:39Z)
• All in Tokens: Unifying Output Space of Visual Tasks via Soft Token [30.6086480249568]
  We show a single unified model that simultaneously handles two typical visual tasks of instance segmentation and depth estimation. We propose several new techniques that take into account the particularity of visual tasks. We achieve 0.279 RMSE on the specific task of NYUv2 depth estimation, setting a new record on this benchmark.
  arXiv  Detail & Related papers  (2023-01-05T18:55:20Z)
• Visually-augmented pretrained language models for NLP tasks without images [77.74849855049523]
  Existing solutions often rely on explicit images for visual knowledge augmentation. We propose a novel textbfVisually-textbfAugmented fine-tuning approach. Our approach can consistently improve the performance of BERT, RoBERTa, BART, and T5 at different scales.
  arXiv  Detail & Related papers  (2022-12-15T16:13:25Z)

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.