Unsupervised domain adaptive (UDA) person Re-Identification aims to improve the model’s generalization capability from labeled source domain to unlabeled target domain. To this end, a strong and robust method is required to extract discriminative features of pedestrians. Recently, transformer-based method achieves great performance on person Re-Identification (ReID). However, due to the domain gap between ImageNet and ReID datasets, it requires a large pre-training dataset to boost performance on Vision Transformer (ViT). To this end, we first investigate self-supervised learning methods with ViTs pretrained on LUPerson datasets, and find it significantly outperforms ImageNet supervised pre-training models on ReID tasks. A Catastrophic Forgetting Score (CFS) is also used to select a subset of LUPerson, which reduces the training time and improves performance. We then proposed a channel-wise self-attention module to reduce the computing cost on the class token. A dual prototype contrastive learning is proposed to fully exploit the hard feature on memory bank under unsupervised domain adaptation. Finally, we achieve state-of-the-art performance on Market-1501 and MSMT17. Our model achieves 91.5%/69.6% mAP accuracy on Market-1501/MSMT17 for supervised ReID, and 90.7%/57.4% mAP for MS2MA/MA2MS UDA ReID.

The hash method or product quantization based on deep learning has achieved great success in image retrieval. But most deep hash methods are designed for supervised scenes. They only use semantic similarity information and ignore the underlying data structure. Moreover, a large amount of manual label information is expensive and time-consuming, which is not in line with the actual application scenario. In order to tackle this problem, we propose a novel quantization-based semi-supervised image retrieval network: Central Product Quantization Network (CPQN). We design a novel central similarity strategy to preserve the semantic similarity and underlying data structure in labeled data, and generalize it to unlabeled data through consistent regularization to tap the potential of unlabeled data. We also propose a novel semi-supervised loss algorithm to achieve effective hashing by reducing quantization noise and minimizing the empirical error of labeled data and the embedding error of unlabeled data. Experiments on public benchmark dataset clearly show that our proposed method is superior to the most advanced hash method.

Due to their damages to Internet security, malware (such as virus, worms, trojans, spyware, backdoors, and rootkits) detection has caught the attention not only of anti-malware industry but also of researchers for decades. Resting on the analysis of file contents extracted from the file samples, like Application Programming Interface (API) calls, instruction sequences, and binary strings, data mining methods such as Naive Bayes and Support Vector Machines have been used for malware detection. However, besides file contents, relations among file samples, such as a "Downloader" is always associated with many Trojans, can provide invaluable information about the properties of file samples. In this paper, we study how file relations can be used to improve malware detection results and develop a file verdict system (named "Valkyrie") building on a semi-parametric classifier model to combine file content and file relations together for malware detection. To the best of our knowledge, this is the first work of using both file content and file relations for malware detection. A comprehensive experimental study on a large collection of PE files obtained from the clients of anti-malware products of Comodo Security Solutions Incorporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our Valkyrie system outperform other popular anti-malware software tools such as Kaspersky AntiVirus and McAfee VirusScan, as well as other alternative data mining based detection systems.