Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models
<|reference_start|>Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models: Fine-grained image classification, particularly in zero/few-shot scenarios, presents a significant challenge for vision-language models (VLMs), such as CLIP. These models often struggle with the nuanced task of distinguishing between semantically similar classes due to limitations in their pre-trained recipe, which lacks supervision signals for fine-grained categorization. This paper introduces CascadeVLM, an innovative framework that overcomes the constraints of previous CLIP-based methods by effectively leveraging the granular knowledge encapsulated within large vision-language models (LVLMs). Experiments across various fine-grained image datasets demonstrate that CascadeVLM significantly outperforms existing models, specifically on the Stanford Cars dataset, achieving an impressive 85.6% zero-shot accuracy. Performance gain analysis validates that LVLMs produce more accurate predictions for challenging images that CLIPs are uncertain about, bringing the overall accuracy boost. Our framework sheds light on a holistic integration of VLMs and LVLMs for effective and efficient fine-grained image classification.<|reference_end|>
@article{wei2024enhancing,
    title={Enhancing Fine-Grained Image Classifications via Cascaded Vision
  Language Models},
    author={Canshi Wei},
    journal={arXiv preprint arXiv:2405.11301},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11301},
    primaryClass={cs.CL cs.CV}
}