HHCL-ReID
Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification (by bupt-ai-cz)
IAST-ECCV2020
IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm (by bupt-ai-cz)
HHCL-ReID | IAST-ECCV2020 | |
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1 | 1 | |
133 | 84 | |
- | - | |
0.0 | 1.8 | |
almost 2 years ago | over 2 years ago | |
Python | Python | |
- | - |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
HHCL-ReID
Posts with mentions or reviews of HHCL-ReID.
We have used some of these posts to build our list of alternatives
and similar projects.
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Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification
Unsupervised person re-identification (Re-ID) is a promising and very challenging research problem in computer vision. Learning robust and discriminative features with unlabeled data is of central importance to Re-ID. Recently, more attention has been paid to unsupervised Re-ID algorithms based on clustered pseudo-label. However, the previous approaches did not fully exploit information of hard samples, simply using cluster centroid or all instances for contrastive learning. In this paper, we propose a Hard-sample Guided Hybrid Contrast Learning (HHCL) approach combining cluster-level loss with instance-level loss for unsupervised person Re-ID. Our approach applies cluster centroid contrastive loss to ensure that the network is updated in a more stable way. Meanwhile, introduction of a hard instance contrastive loss further mines the discriminative information. Extensive experiments on two popular large-scale Re-ID benchmarks demonstrate that our HHCL outperforms previous state-of-the-art methods and significantly improves the performance of unsupervised person Re-ID. The code of our work is available soon at https://github.com/bupt-ai-cz/HHCL-ReID.
IAST-ECCV2020
Posts with mentions or reviews of IAST-ECCV2020.
We have used some of these posts to build our list of alternatives
and similar projects.
What are some alternatives?
When comparing HHCL-ReID and IAST-ECCV2020 you can also consider the following projects:
Real-time-Semantic-Segmentation
HRNet-Semantic-Segmentation - The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919