The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep domain adaptation for describing people based on fine-grained clothing attributes
Abstract
We address the problem of describing people based on fine-grained clothing attributes. This is an important problem for many practical applications, such as identifying target suspects or finding missing people based on detailed clothing descriptions in surveillance videos or consumer photos. We approach this problem by first mining clothing images with fine-grained attribute labels from online shopping stores. A large-scale dataset is built with about one million images and fine-detailed attribute sub-categories, such as various shades of color (e.g., watermelon red, rosy red, purplish red), clothing types (e.g., down jacket, denim jacket), and patterns (e.g., thin horizontal stripes, houndstooth). As these images are taken in ideal pose/lighting/background conditions, it is unreliable to directly use them as training data for attribute prediction in the domain of unconstrained images captured, for example, by mobile phones or surveillance cameras. In order to bridge this gap, we propose a novel double-path deep domain adaptation network to model the data from the two domains jointly. Several alignment cost layers placed inbetween the two columns ensure the consistency of the two domain features and the feasibility to predict unseen attribute categories in one of the domains. Finally, to achieve a working system with automatic human body alignment, we trained an enhanced RCNN-based detector to localize human bodies in images. Our extensive experimental evaluation demonstrates the effectiveness of the proposed approach for describing people based on fine-grained clothing attributes.
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Cites
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“The majority of existing approaches for domain adaptation or transfer learning with deep architectures rely on re-training the last few layers of the network using samples from the target domain, or instead performing fine-tuning of all layers using backpropagation at a lower learning rate [36, 38, 20].”
BackgroundWhat is the best multi-stage architecture for object recognition?
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Self-citationPANDA: Pose Aligned Networks for Deep Attribute Modeling
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Survey on deep learning with class imbalance
Johnson, J. M., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0192-5 · Cited by 3,032
Unified Deep Supervised Domain Adaptation and Generalization
Motiian, S., Piccirilli, M., Adjeroh, D. A., & Doretto, G. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.609 · Cited by 803
SpotTune: Transfer Learning Through Adaptive Fine-Tuning
Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., & Feris, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00494 · Cited by 506
Self-citationBeyond Sharing Weights for Deep Domain Adaptation
Rozantsev, A., Salzmann, M., & Fua, P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2814042 · Cited by 462
Lu, Y., Kumar, A., Zhai, S., Cheng, Y., Javidi, T., & Feris, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.126 · Cited by 371
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Metadata
- Authors
- Chen, Q., Huang, J., Feris, R., Brown, L. M., Dong, J., & Yan, S.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 5315-5324
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1946323491
- Cited by
- 241
- References
- 9
- FWCI
- 21.59
- Type
- conference paper
- Language
- en
- Open access
- closed
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