CelebA-Spoof: Large-Scale Face Anti-spoofing Dataset with Rich Annotations

Abstract

Traffic prediction plays a significant role in Intelligent Transportation Systems (ITS). Although many datasets have been introduced to support the study of traffic prediction, most of them only provide time-series traffic data. However, urban transportation systems are always susceptible to various factors, including unusual weather and traffic accidents. Therefore, relying solely on historical data for traffic prediction greatly limits the accuracy of the prediction. In this paper, we introduce Beijing Text-Traffic (BjTT), a large-scale multimodal dataset for traffic prediction. BjTT comprises over 32,000 time-series traffic records, capturing velocity and congestion levels on more than 1,200 roads within the 5th ring area of Beijing. Meanwhile, each piece of traffic data is coupled with a text describing the traffic system (including time, location, and events). We detail the data collection and processing procedures and present a statistical analysis of the BjTT dataset. Furthermore, we conduct comprehensive experiments on the dataset with state-of-the-art traffic prediction methods and text-guided generative models, which reveal the unique characteristics of the BjTT. The dataset is available at https://github.com/ChyaZhang/BjTT.

Real masks and spoof faces: On the masked face presentation attack detection

“…Given the significance of good-quality databases, several face PAD databases have been released, such as NUAA [10] , CASIA-FAS [11] , Replay-Attack [12] , MSU-MFSD [13] , OULU-NPU [7] , and SiW [8] , all consisting of 2D print/replay attacks. In addition, SiW-M [9] and CelebA-Spoof [14] databases provide multiple types of attacks such as makeup, 3D mask, or paper cut. Moreover, some multimodal databases are publicly available: 3DMAD [15] , Mssproof [16] , CASIA-SURF [17] , and CSMAD [18] .…”

“…For example, the CeleA-Spoof database comprises images from various environments and illuminations with rich annotations to reflect real scenes. However, these databases also have weaknesses: 1) the multimodal databases have high hardware requirements and cannot be widely used in daily life; 2) some databases such as CASIA-MFS [11] and MSU-MFS [13] cannot satisfy the current needs because of the lower quality of the outdated acquisition sensors; 3) Oulu-NPU [7] , SiW [8] , SiW-M [9] , and CelebA-Spoof [14] are relatively up-to-date, but they do not consider PAs with real face masks to fit the current COVID-19 pandemic. Hence, we collect the CRMA database to fill the gaps in these databases in the context of the ongoing COVID-19 pandemic; furthermore, we ensure the the database is generalizable and compatible with real scenarios.…”

Deep Multi-Resolution Mutual Learning for Image Inpainting

“…• CelebA-HQ dataset is a human face dataset that is a high-quality version from the CelebA dataset [47]. There are 30k face images in CelebA-HQ dataset.…”

Efficient Face Spoofing Detection With Flash

“…Another interesting approaches include formulating PAD as object detection [63], PAD as material classification [64], using the shape from shading algorithm as a preprocessing [65], and neural architecture search [66]. Recently, Zhang et al created a large-scale face anti-spoofing dataset with annotations [67].…”