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Conference papers

ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification

Abstract : Unsupervised person re-identification (ReID) aims at learning discriminative identity features without annotations. Recently, self-supervised contrastive learning has gained increasing attention for its effectiveness in unsupervised representation learning. The main idea of instance contrastive learning is to match a same instance in different augmented views. However, the relationship between different instances has not been fully explored in previous contrastive methods, especially for instance-level contrastive loss. To address this issue, we propose Interinstance Contrastive Encoding (ICE) that leverages interinstance pairwise similarity scores to boost previous classlevel contrastive ReID methods. We first use pairwise similarity ranking as one-hot hard pseudo labels for hard instance contrast, which aims at reducing intra-class variance. Then, we use similarity scores as soft pseudo labels to enhance the consistency between augmented and original views, which makes our model more robust to augmentation perturbations. Experiments on several largescale person ReID datasets validate the effectiveness of our proposed unsupervised method ICE, which is competitive with even supervised methods. Code is made available at
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Contributor : Hao Chen Connect in order to contact the contributor
Submitted on : Monday, September 20, 2021 - 1:56:14 PM
Last modification on : Tuesday, December 7, 2021 - 4:10:58 PM
Long-term archiving on: : Tuesday, December 21, 2021 - 6:46:57 PM


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  • HAL Id : hal-03349266, version 1


Hao Chen, Benoit Lagadec, Francois Bremond. ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification. IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2021, Virtual, Canada. ⟨hal-03349266⟩



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