Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models

• URL: http://arxiv.org/abs/2405.11301v1
• Date: Sat, 18 May 2024 14:12:04 GMT
• Title: Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models
• Authors: Canshi Wei,
• Abstract summary: This paper introduces CascadeVLM, an innovative framework that overcomes the constraints of previous CLIP-based methods. Experiments across various fine-grained image datasets demonstrate that CascadeVLM significantly outperforms existing models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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