HHCL-ReID
Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification (by bupt-ai-cz)
GEFF
Official implementation of the paper GEFF: Improving Any Clothes-Changing Person ReID Model using Gallery Enrichment with Face Features. (by bar371)
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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.
GEFF
Posts with mentions or reviews of GEFF.
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 GEFF you can also consider the following projects:
curated-list-of-awesome-3D-Morphable-Model-software-and-data - The idea of this list is to collect shared data and algorithms around 3D Morphable Models. You are invited to contribute to this list by adding a pull request. The original list arised from the Dagstuhl seminar on 3D Morphable Models https://www.dagstuhl.de/19102 in March 2019.