Fine-Grained Recognition Without Part Annotations

Jonathan Krause, Hailin Jin, Jianchao Yang, Li Fei-Fei; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5546-5555


Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories. Current state-of-the-art techniques rely heavily upon the use of keypoint or part annotations, but scaling up to hundreds or thousands of domains renders this annotation cost-prohibitive for all but the most important categories. In this work we propose a method for fine-grained recognition that uses no part annotations. Our method is based on generating parts using co-segmentation and alignment, which we combine in a discriminative mixture. Experimental results show its efficacy, demonstrating state-of-the-art results even when compared to methods that use part annotations during training.

Related Material

author = {Krause, Jonathan and Jin, Hailin and Yang, Jianchao and Fei-Fei, Li},
title = {Fine-Grained Recognition Without Part Annotations},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}