Understanding the Fine-Grained Knowledge Capabilities of Vision-Language Models

• URL: http://arxiv.org/abs/2602.17871v1
• Date: Thu, 19 Feb 2026 22:07:29 GMT
• Title: Understanding the Fine-Grained Knowledge Capabilities of Vision-Language Models
• Authors: Dhruba Ghosh, Yuhui Zhang, Ludwig Schmidt,
• Abstract summary: Vision-language models (VLMs) have made substantial progress across a wide range of visual question answering benchmarks, spanning visual reasoning, document understanding, and multimodal dialogue.<n>Recent works show that these models trail behind in traditional image classification benchmarks, which test fine-grained visual knowledge.<n>We test a large number of recent VLMs on fine-grained classification benchmarks and identify potential factors in the disconnect between fine-grained knowledge and other vision benchmarks.
• Score: 42.79282247484499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) have made substantial progress across a wide range of visual question answering benchmarks, spanning visual reasoning, document understanding, and multimodal dialogue. These improvements are evident in a wide range of VLMs built on a variety of base models, alignment architectures, and training data. However, recent works show that these models trail behind in traditional image classification benchmarks, which test fine-grained visual knowledge. We test a large number of recent VLMs on fine-grained classification benchmarks and identify potential factors in the disconnect between fine-grained knowledge and other vision benchmarks. Through a series of ablation experiments, we find that using a better LLM improves all benchmark scores equally, while a better vision encoder disproportionately improves fine-grained classification performance. Furthermore, we find that the pretraining stage is also vital to fine-grained performance, particularly when the language model weights are unfrozen during pretraining. These insights pave the way for enhancing fine-grained visual understanding and vision-centric capabilities in VLMs.

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