CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning

Face recognition technology is widely used in the financial field, and various types of liveness attack behaviors need to be addressed. Existing liveness detection algorithms are trained on specific training datasets and tested on testing datasets, but their performance and robustness in transferring to unseen datasets are relatively poor. To tackle this issue, we propose a face liveness detection method based on image-text pairs and contrastive learning, dividing liveness attack problems in the financial field into eight categories and using text information to describe the images of these eight types of attacks. The text encoder and image encoder are used to extract feature vector representations for the classification description text and face images, respectively. By maximizing the similarity of positive samples and minimizing the similarity of negative samples, the model learns shared representations between images and texts. The proposed method is capable of effectively detecting specific liveness attack behaviors in certain scenarios, such as those occurring in dark environments or involving the tampering of ID card photos. Additionally, it is also effective in detecting traditional liveness attack methods, such as printing photo attacks and screen remake attacks. The zero-shot capabilities of face liveness detection on five public datasets, including NUAA, CASIA-FASD, Replay-Attack, OULU-NPU and MSU-MFSD also reaches the level of commercial algorithms. The detection capability of proposed algorithm was verified on 5 types of testing datasets, and the results show that the method outperformed commercial algorithms, and the detection rates reached 100% on multiple datasets. Demonstrating the effectiveness and robustness of introducing image-text pairs and contrastive learning into liveness detection tasks as proposed in this paper

A Survey on Transformers in Reinforcement Learning

Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings. Recently, a similar surge of using Transformers has appeared in the domain of reinforcement learning (RL), but it is faced with unique design choices and challenges brought by the nature of RL. However, the evolution of Transformers in RL has not yet been well unraveled. In this paper, we seek to systematically review motivations and progress on using Transformers in RL, provide a taxonomy on existing works, discuss each sub-field, and summarize future prospects

Preparation and solution properties of a novel cationic hydrophobically modified polyacrylamide for enhanced oil recovery

Financial support from National Natural Science Foundation of China (51504050, 51774062), Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJ1601305) and Research Foundation of Chongqing University of Science & Technology (CK2016B07, CK2016Z20).Peer reviewedPostprin

Construction of PAN-based activated carbon nanofibers for hydrogen storage under ambient pressure

Adsorption agents are an important class of solid hydrogen storage materials. Attributed to their high specific surface area and adjustable nanopore structure, activated carbon nanofibers have attracted extensive attention in the application of solid hydrogen storage. The research in this field mostly focuses on applications with a hydrogen pressure condition of 30 to 300 bar, while there have been few systematic studies on the hydrogen storage performance of these materials under ambient pressure. In this study, polyacrylonitrile-based activated carbon nanofibers were constructed by electrospinning technology and ultrasonic-assisted activation technology for the application of atmospheric hydrogen storage. Their nanopore structure was revealed to be mainly composed of micropores, and the relative contents of micropore volume and ultramicropore volume were 77.92% to 88.3% and 22.34% to 24.68%, respectively. Attributed to the synergy of rich microporous structure and surface chemical structure, the atmospheric hydrogen storage density of activated carbon nanofibers could reach 2.64 wt% at 77 K and 1 bar. After the optimization analysis of adsorption isotherm models, the Multisite-Langmuir model was found as more suitable for accurately describing the atmospheric hydrogen adsorption process of activated carbon nanofibers.Cited as: Yu, J., Lin, T., Li, J., Zhang, W., Bao, W., Zhu, B. Construction of PAN-based activated carbon nanofibers for hydrogen storage under ambient pressure. Capillarity, 2023, 6(3): 49-56. https://doi.org/10.46690/capi.2023.03.0