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Evaluating Discriminability of Vision-Language Models

We study the discriminative ability of vision-language models (VLMs). This ability refers to processing information by distinguishing key details from unnecessary or redundant parts to achieve specific goals. It is vital for the practical use of VLMs in applications like visual chatbots. Whereas recent VLMs have shown decent performance on various multimodal capabilities, their discriminative ability has not been thoroughly explored to date. To this end, we construct DiscriBench to evaluate the discriminability of VLMs in various daily life activities. We carefully design the dataset to require distinguishing information in both vision and language modalities, and semi-manually craft questions in English and Japanese, making them solvable without relying on external knowledge or expertise. Experimental results demonstrate a large performance gap (14.0 to 69.3 points) between humans and existing VLMs in discriminability, where humans can solve the task with an accuracy of 90% or higher. By reducing the difficulty of discriminability, our ablation studies elucidate that vision encoders cannot distinguish visual details well, given generally similar but partially different images. Besides, we observe that VLMs show inconsistent inference between modalities. We will publish DiscriBench (1,200 samples) to foster research in this direction.