Understanding the Fine-Grained Knowledge Capabilities of Vision-Language Models
arXiv:2602.17871v1 Announce Type: cross 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 fin
arXiv:2602.17871v1 Announce Type: cross 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.
Executive Summary
This article sheds light on the disparity between the performance of vision-language models (VLMs) in fine-grained visual knowledge tasks and other visual question answering benchmarks. Through a series of experiments, the authors identify the importance of a better vision encoder and the pretraining stage in enhancing fine-grained visual understanding. The findings of this study have implications for the development of more effective VLMs, particularly in the context of fine-grained classification and image understanding. The discovery that a better language model improves all benchmark scores equally, while a better vision encoder disproportionately improves fine-grained classification performance, provides valuable insights for researchers and practitioners in the field. This study contributes to the ongoing efforts to improve the capabilities of VLMs, potentially leading to breakthroughs in applications such as image recognition, object detection, and visual search.
Key Points
- ▸ VLMs perform poorly in fine-grained visual knowledge tasks compared to other visual question answering benchmarks
- ▸ A better vision encoder disproportionately improves fine-grained classification performance
- ▸ The pretraining stage is vital to fine-grained performance, particularly when the language model weights are unfrozen
Merits
Strength
The study provides valuable insights into the factors contributing to the disconnect between fine-grained knowledge and other vision benchmarks, enabling researchers to develop more effective VLMs.
Demerits
Limitation
The study focuses on a specific type of VLMs, and its findings may not be generalizable to other types of models or applications.
Expert Commentary
This study provides a significant contribution to the field of VLMs, shedding light on the factors contributing to the disconnect between fine-grained knowledge and other vision benchmarks. The findings of this study have implications for the development of more effective VLMs, particularly in the context of fine-grained classification and image understanding. The discovery that a better vision encoder disproportionately improves fine-grained classification performance highlights the importance of developing more effective vision encoders. Furthermore, the study's focus on the pretraining stage and the importance of unfreezing the language model weights during pretraining provides valuable insights for researchers and practitioners in the field. This study contributes to the ongoing efforts to improve the capabilities of VLMs, potentially leading to breakthroughs in applications such as image recognition, object detection, and visual search.
Recommendations
- ✓ Future studies should focus on developing more effective vision encoders and exploring the use of different pretraining stages and architectures.
- ✓ Researchers should investigate the applicability of VLMs with fine-grained visual understanding capabilities in various industries and applications.
