Accelerate Learning of Deep Hashing With Gradient Attention

Long-Kai Huang, Jianda Chen, Sinno Jialin Pan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 5271-5280


Recent years have witnessed the success of learning to hash in fast large-scale image retrieval. As deep learning has shown its superior performance on many computer vision applications, recent designs of learning-based hashing models have been moving from shallow ones to deep architectures. However, based on our analysis, we find that gradient descent based algorithms used in deep hashing models would potentially cause hash codes of a pair of training instances to be updated towards the directions of each other simultaneously during optimization. In the worst case, the paired hash codes switch their directions after update, and consequently, their corresponding distance in the Hamming space remain unchanged. This makes the overall learning process highly inefficient. To address this issue, we propose a new deep hashing model integrated with a novel gradient attention mechanism. Extensive experimental results on three benchmark datasets show that our proposed algorithm is able to accelerate the learning process and obtain competitive retrieval performance compared with state-of-the-art deep hashing models.

Related Material

author = {Huang, Long-Kai and Chen, Jianda and Pan, Sinno Jialin},
title = {Accelerate Learning of Deep Hashing With Gradient Attention},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}