Unsupervised Attention Based Instance Discriminative Learning for Person Re-Identification

• URL: http://arxiv.org/abs/2011.01888v1
• Date: Tue, 3 Nov 2020 18:08:31 GMT
• Title: Unsupervised Attention Based Instance Discriminative Learning for Person Re-Identification
• Authors: Kshitij Nikhal and Benjamin S. Riggan
• Abstract summary: We propose an unsupervised framework for person re-identification which is trained in an end-to-end manner without pre-training. Our proposed framework leverages a new attention mechanism that combines group convolutions to enhance spatial attention at multiple scales. We perform extensive analysis using the Market1501 and DukeMTMC-reID datasets to demonstrate that our method consistently outperforms the state-of-the-art methods.
• Score: 5.233788055084763
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent advances in person re-identification have demonstrated enhanced discriminability, especially with supervised learning or transfer learning. However, since the data requirements---including the degree of data curations---are becoming increasingly complex and laborious, there is a critical need for unsupervised methods that are robust to large intra-class variations, such as changes in perspective, illumination, articulated motion, resolution, etc. Therefore, we propose an unsupervised framework for person re-identification which is trained in an end-to-end manner without any pre-training. Our proposed framework leverages a new attention mechanism that combines group convolutions to (1) enhance spatial attention at multiple scales and (2) reduce the number of trainable parameters by 59.6%. Additionally, our framework jointly optimizes the network with agglomerative clustering and instance learning to tackle hard samples. We perform extensive analysis using the Market1501 and DukeMTMC-reID datasets to demonstrate that our method consistently outperforms the state-of-the-art methods (with and without pre-trained weights).

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