Papers by Canshi Wei
Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models (2024.findings-emnlp)
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| Challenge: | Fine-grained image classification is a challenge for vision-language models (VLMs) such as CLIP, which struggle to distinguish between semantically similar classes due to insufficient supervision for fine-grain tasks. |
| Approach: | They propose a framework that harnesses the complementary strengths of both CLIP-like and LVLMs to tackle these challenges. |
| Outcome: | The proposed framework outperforms existing models on multiple fine-grained datasets, particularly the Stanford Cars dataset. |