Cross-modal metrics improve vision encoder evaluation for AI models
A Strong Baseline for Evaluating Vision Encoders in Multimodal Large Language Models
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Evaluating how well vision components work with language-based AI models is tricky, especially choosing the right way to measure performance. The authors found problems in past methods and made some simple fixes to create RAVEL, a new way to test these vision parts without extra training. This method uses matching images and text closely to judge how good the vision part is, and it works better than earlier ways. Their work shows that careful evaluation with cross-modal comparisons leads to better predictions of real-world performance.
What this means in practice
- •For ml engineers: Select more effective vision encoders for multimodal AI systems based on RAVEL’s improved evaluation metric.
- •For computer vision developers: Use RAVEL to benchmark vision encoder models before integrating them with language-based AI for better design decisions.
Authors
Yilin Yang, Jun-Tao Tang, Kengyi Wang, Siyuan Su, Gaoyong Luo, Mingda Chen
Abstract
Evaluating vision encoders requires metrics that reliably predict their downstream performance in multimodal large language models (MLLMs). Although recent studies have shown that cross-modal metrics can better capture such performance, unimodal metrics remain the dominant choice in practice. In this work, we revisit cross-modal evaluation of vision encoders through large-scale experiments. We identify important limitations in both the experimental design and methodological formulation of prior approaches. After addressing these limitations and introducing simple improvements, we propose RAVEL, a training-free method based on cross-modal nearest-neighbor retrieval. Despite its simplicity, RAVEL achieves state-of-the-art performance across our experiments, outperforming prior methods by a substantial margin. Our results demonstrate that simple cross-modal metrics, when evaluated under a careful and comprehensive setup, can provide a strong basis for evaluating vision encoders for MLLMs.