The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Fine-grained recognition without part annotations
Abstract
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.
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ImageNet Large Scale Visual Recognition Challenge
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Self-citationObject Detection with Discriminatively Trained Part-Based Models
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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Locality-constrained Linear Coding for image classification
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Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition
Zheng, H., Fu, J., Mei, T., & Luo, J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.557 · Cited by 1,045
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chen, C., Li, O., Tao, C., Barnett, A. J., Su, J., & Rudin, C. (2018) · arXiv · doi:10.48550/arxiv.1806.10574 · Cited by 702
Low-Rank Bilinear Pooling for Fine-Grained Classification
Kong, S., & Fowlkes, C. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.743 · Cited by 367
Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition
Lin, T.-Y., RoyChowdhury, A., & Maji, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723400 · Cited by 328
Metadata
- Authors
- Krause, J., Jin, H., Yang, J., & Fei-Fei, L.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 5546-5555
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1898560071
- Cited by
- 466
- References
- 5
- FWCI
- 25.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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