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Semantic-Clipping: Efficient Vision-Language Modeling with Semantic-Guidedd Visual Selection

Bangzheng Li, Fei Wang, Wenxuan Zhou, Nan Xu, Ben Zhou, Sheng Zhang, Hoifung Poon, Muhao Chen

TL;DR

Semantic-Clipping addresses the inefficiency of processing high-resolution images in vision-language models by introducing a text-guided, semantic-aware sub-image selection (SemClip). It selects task-relevant regions via a relevance function $\psi$ and feeds only those crops together with the overview image into a fixed VLM, avoiding retraining. Distantly supervised training of $\psi$ with a CLIP-style objective yields notable gains, including an average improvement of $+3.3\%$ across benchmarks and up to $+5.3\%$ on the detailed understanding task $V^*$. The results reveal a substantial theoretical upper bound for SemClip, suggesting larger gains are possible with more pretraining and refined relevance signals, and point to future work in leveraging textual cues during VLM training to unlock latent capabilities.

Abstract

Vision-Language Models (VLMs) leverage aligned visual encoders to transform images into visual tokens, allowing them to be processed similarly to text by the backbone large language model (LLM). This unified input paradigm enables VLMs to excel in vision-language tasks such as visual question answering (VQA). To improve fine-grained visual reasoning, recent advancements in vision-language modeling introduce image cropping techniques that feed all encoded sub-images into the model. However, this approach significantly increases the number of visual tokens, leading to inefficiency and potential distractions for the LLM. To address the generalization challenges of image representation in VLMs, we propose a lightweight, universal framework that seamlessly integrates with existing VLMs to enhance their ability to process finegrained details. Our method leverages textual semantics to identify key visual areas, improving VQA performance without requiring any retraining of the VLM. Additionally, it incorporates textual signals into the visual encoding process, enhancing both efficiency and effectiveness. The proposed method, SEMCLIP, strengthens the visual understanding of a 7B VLM, LLaVA-1.5 by 3.3% on average across 7 benchmarks, and particularly by 5.3% on the challenging detailed understanding benchmark V*.

Semantic-Clipping: Efficient Vision-Language Modeling with Semantic-Guidedd Visual Selection

TL;DR

Semantic-Clipping addresses the inefficiency of processing high-resolution images in vision-language models by introducing a text-guided, semantic-aware sub-image selection (SemClip). It selects task-relevant regions via a relevance function and feeds only those crops together with the overview image into a fixed VLM, avoiding retraining. Distantly supervised training of with a CLIP-style objective yields notable gains, including an average improvement of across benchmarks and up to on the detailed understanding task . The results reveal a substantial theoretical upper bound for SemClip, suggesting larger gains are possible with more pretraining and refined relevance signals, and point to future work in leveraging textual cues during VLM training to unlock latent capabilities.

Abstract

Vision-Language Models (VLMs) leverage aligned visual encoders to transform images into visual tokens, allowing them to be processed similarly to text by the backbone large language model (LLM). This unified input paradigm enables VLMs to excel in vision-language tasks such as visual question answering (VQA). To improve fine-grained visual reasoning, recent advancements in vision-language modeling introduce image cropping techniques that feed all encoded sub-images into the model. However, this approach significantly increases the number of visual tokens, leading to inefficiency and potential distractions for the LLM. To address the generalization challenges of image representation in VLMs, we propose a lightweight, universal framework that seamlessly integrates with existing VLMs to enhance their ability to process finegrained details. Our method leverages textual semantics to identify key visual areas, improving VQA performance without requiring any retraining of the VLM. Additionally, it incorporates textual signals into the visual encoding process, enhancing both efficiency and effectiveness. The proposed method, SEMCLIP, strengthens the visual understanding of a 7B VLM, LLaVA-1.5 by 3.3% on average across 7 benchmarks, and particularly by 5.3% on the challenging detailed understanding benchmark V*.

Paper Structure

This paper contains 18 sections, 6 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. SemClip
  3. Preliminaries
  4. Problem Definition
  5. Semantic Clipping
  6. Relevance Measurement
  7. Training $\psi$ with Distant Supervision
  8. Experiment
  9. Experiment Settings
  10. Distant supervision yields the best performance
  11. SemClip improves visual understanding
  12. Where is the upper bound of SemClip?
  13. How are useful sub images distributed?
  14. A trade-off between image token numbers and performance
  15. Is the sub-image itself enough?
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Paying attention to task-relevant regions within a scene is an intuitive approach to answering visual questions. Our objective is to identify the optimal task-relevance measurement $\psi$ that selects the most pertinent sub-region of an image, enhancing visual understanding.
  • Figure 2: SemClip is a plug-and-play method that enhances a VLM through semantic-guided visual selection. Task-relevant sub-areas of the image was encoded and appended to the visual tokens of the overview image(colored in blue and purple, respectively). These visual tokens together with text embeddings of the question (colored in orange) are processed by the backbone VLM to generate a response.