13th Workshop on Fine-grained Visual Categorization
Agentic Prompt Optimization with Visual Contrastive Reasoning for Fine-Grained Classification-
[pdf]
[supp]
[bibtex]@InProceedings{Goncalves_2026_CVPR, author = {Goncalves, Lucas and A Barton, Robert and Bansal, Vidit and Bouyarmane, Karim}, title = {Agentic Prompt Optimization with Visual Contrastive Reasoning for Fine-Grained Classification}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2026}, pages = {3101-3110} }
Efficient Fine-grained Image Retrieval with Vision Foundation Models for Industrial Objects-
[pdf]
[supp]
[bibtex]@InProceedings{Liu_2026_CVPR, author = {Liu, Yushi and Graf, Christian and Spies, Markus and Keuper, Margret}, title = {Efficient Fine-grained Image Retrieval with Vision Foundation Models for Industrial Objects}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2026}, pages = {3111-3120} }
Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Zaher_2026_CVPR, author = {Zaher, Kawtar and Buisson, Olivier and Joly, Alexis}, title = {Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2026}, pages = {3121-3130} }
Masked Autoencoders with Limited Data: Does It Work? A Fine-Grained Bioacoustics Case Study-
[pdf]
[arXiv]
[bibtex]@InProceedings{Liu_2026_CVPR, author = {Liu, Wuao and Chasmai, Mustafa and Maji, Subhransu and Van Horn, Grant}, title = {Masked Autoencoders with Limited Data: Does It Work? A Fine-Grained Bioacoustics Case Study}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2026}, pages = {3131-3140} }

