J. Xu, C. Guo, Q. Liu, J. Qin, Y. Wang and L. Liu, "DHA: Supervised Deep Learning to Hash with an Adaptive Loss Function," 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019, pp. 3054-3062, doi: 10.1109/ICCVW.2019.00368
DHA : supervised deep learning to hash with an adaptive loss function
|Author:||Xu, Jiehao1; Guo, Chengyu2; Liu, Qingjie1;|
1State Key Laboratory of Virtual Reality Technology and System, Beihang University, China
2College of System Engineering, National University of Defense Technology, China
3Inception Institute of Artificial Intelligence
4Center for Machine Vision and Signal Analysis, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020101584132
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-10-15
Hashing, which refers to the binary embedding of high-dimensional data, has been an effective solution for fast nearest neighbor retrieval in large-scale databases due to its computational and storage efficiency. Recently, deep learning to hash has been attracting increasing attention since it has shown great potential in improving retrieval quality by leveraging the strengths of deep neural networks. In this paper, we consider the problem of supervised hashing and propose an effective model (i.e., DHA), which is able to generate compact and discriminative binary codes while preserving semantic similarities of original data with an adaptive loss function. The key idea is that we scale and shift the loss function to avoid the saturation of gradients during training, and simultaneously adjust the loss to adapt to different levels of similarities of data. We evaluate the proposed DHA on three widely-used benchmarks, i.e., NUS-WIDE, CIFAR-10, and MS COCO. The state-of-the-art image retrieval performance clearly shows the effectiveness of our method in learning discriminative hash codes for nearest neighbor retrieval.
IEEE International Conference on Computer Vision workshops
|Pages:||3054 - 3062|
17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
IEEE/CVF International Conference on Computer Vision Workshop
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This work was supported by the Foundation for Innovative Research Groups through the National Natural Science Foundation of China under Grant 61421003.
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