HHCL-ReID VS Writing-Styles-Classification-Using-Stylometric-Analysis

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.

Or look into stylistic features and add them to xgboost classifier (https://github.com/ZILiAT-NASK/StyloMetrix - I used those combined with BERT last hidden state for fake news classification and got the best results so far - here repo if you wish to get some inspirations: https://github.com/MarBry111/Fake-News-Detection-for-Social-Media-Posts-in-Polish-Language , https://github.com/Hassaan-Elahi/Writing-Styles-Classification-Using-Stylometric-Analysis here some more inspirations for stylistic features) to enhance the results.