Lost in Embeddings: Information Loss in Vision-Language Models

2025-09-15

AI Summary

In vision-language models (VLMs), visual encoder outputs are projected into the language embedding space via a connector module, often inducing substantial information loss—yet the underlying mechanisms and quantitative impact remain poorly understood. Method: We propose a dual-perspective diagnostic framework: (i) quantifying semantic fidelity via k-nearest-neighbor (k-NN) structural distortion in the joint embedding space, and (ii) localizing image-patch-level information loss through embedding reconstruction inversion. Leveraging mainstream pre-trained VLM architectures, we combine representation space analysis with reconstruction-based inversion techniques. Results: Our analysis reveals that connectors distort 40–60% of local neighborhood relationships; critically, high-loss regions exhibit strong spatial correlation with performance degradation in visual question answering. This work provides the first interpretable, spatially localized, and quantitatively rigorous characterization of connector-induced information loss—establishing a novel diagnostic paradigm and empirical foundation for modality alignment optimization.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Vision--language models (VLMs) often process visual inputs through a pretrained vision encoder, followed by a projection into the language model's embedding space via a connector component. While crucial for modality fusion, the potential information loss induced by this projection step and its direct impact on model capabilities remain understudied. We introduce two complementary approaches to examine and quantify this loss by analyzing the latent representation space. First, we evaluate semantic information preservation by analyzing changes in k-nearest neighbor relationships between image representations, before and after projection. Second, we directly measure information loss by reconstructing visual embeddings from the projected representation, localizing loss at an image patch level. Experiments reveal that connectors substantially distort the local geometry of visual representations, with k-nearest neighbors diverging by 40--60% post-projection, correlating with degradation in retrieval performance. The patch-level embedding reconstruction provides interpretable insights for model behavior on visually grounded question-answering tasks, finding that areas of high information loss reliably predict instances where models struggle.

Problem

• Quantifying information loss in vision-language model projections
• Analyzing semantic distortion in visual representation geometry
• Localizing patch-level information loss affecting model performance

Innovation

• Analyzing k-nearest neighbor semantic changes
• Reconstructing visual embeddings from projections
• Localizing information loss at patch level