Fine-Grained Classification: Connecting Metadata via Cross-Contrastive Pre-Training

Sumit Mamtani; Yash Thesia

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arXiv · 2504.20322

Fine-Grained Classification: Connecting Metadata via Cross-Contrastive Pre-Training

Sumit Mamtani·Yash Thesia

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Abstract

Fine-grained visual classification aims to recognize objects belonging to many subordinate categories of a supercategory, where appearance alone often fails to distinguish highly similar classes. We propose a unified framework that integrates image, text, and metadata via cross-contrastive pre-training. We first align the three modality encoders in a shared embedding space and then fine-tune the image and metadata encoders for classification. On NABirds, our approach improves over the baseline by 7.83% and achieves 84.44% top-1 accuracy, outperforming strong multimodal methods.

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Sumit Mamtani, Yash Thesia. 2025-04-29. Fine-Grained Classification: Connecting Metadata via Cross-Contrastive Pre-Training. https://arxiv.org/abs/2504.20322

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