What's in the Image? A Deep-Dive into the Vision of Vision Language Models

• URL: http://arxiv.org/abs/2411.17491v1
• Date: Tue, 26 Nov 2024 14:59:06 GMT
• Title: What's in the Image? A Deep-Dive into the Vision of Vision Language Models
• Authors: Omri Kaduri, Shai Bagon, Tali Dekel,
• Abstract summary: Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in comprehending complex visual content. In this paper, we conduct a thorough empirical analysis, focusing on attention modules across layers. We reveal several key insights about how these models process visual data.
• Score: 20.669971132114195
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• Abstract: Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in comprehending complex visual content. However, the mechanisms underlying how VLMs process visual information remain largely unexplored. In this paper, we conduct a thorough empirical analysis, focusing on attention modules across layers. We reveal several key insights about how these models process visual data: (i) the internal representation of the query tokens (e.g., representations of "describe the image"), is utilized by VLMs to store global image information; we demonstrate that these models generate surprisingly descriptive responses solely from these tokens, without direct access to image tokens. (ii) Cross-modal information flow is predominantly influenced by the middle layers (approximately 25% of all layers), while early and late layers contribute only marginally.(iii) Fine-grained visual attributes and object details are directly extracted from image tokens in a spatially localized manner, i.e., the generated tokens associated with a specific object or attribute attend strongly to their corresponding regions in the image. We propose novel quantitative evaluation to validate our observations, leveraging real-world complex visual scenes. Finally, we demonstrate the potential of our findings in facilitating efficient visual processing in state-of-the-art VLMs.

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