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

2025-03-14

AI Summary

To address the efficiency degradation and semantic interference in vision-language models (VLMs) caused by blind expansion of visual tokens during fine-grained visual reasoning, this paper proposes a semantics-guided visual cropping framework. Without requiring model retraining, it introduces text semantics explicitly into the visual encoding process via a lightweight, text-driven visual token selection mechanism—enabling plug-and-play enhancement. Key technical components include semantic alignment-based cropping, cross-modal attention guidance, and sparse visual token selection, with efficient adaptation to LLaVA-1.5. Evaluated on seven benchmarks, the method achieves an average +3.3% performance gain for 7B-scale VLMs and a +5.3% improvement on the fine-grained understanding benchmark V*, while reducing visual token count by ~40% and significantly lowering inference latency.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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*.

Problem

• Improves fine-grained visual reasoning in VLMs
• Reduces inefficiency from excessive visual tokens
• Enhances VQA performance without VLM retraining

Innovation

• Semantic-guided visual selection enhances VLM efficiency.
• Textual semantics improve visual area identification.
• SEMCLIP boosts VLM performance without retraining.