mlsanityData to 14 Oct 2026

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

arXiv 2504.20322 ↗First posted 29 Apr 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 29 Apr 2025.

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19 of 27 references.

References · 19

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  12. 2018Learning to Navigate for Fine-grained Classificationcited by 103
7 more
  1. 2019Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognitioncited by 95
  2. 2020Learning Attentive Pairwise Interaction for Fine-Grained Classificationcited by 84
  3. 2019Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Upcited by 66
  4. 2019Learning Deep Bilinear Transformation for Fine-grained Image Representationcited by 54
  5. 2017Fine-graind Image Classification via Combining Vision and Languagecited by 51
  6. 2018Hierarchical Bilinear Pooling for Fine-Grained Visual Recognitioncited by 47
  7. 2025RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddingscited by 19

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