Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.Comment: Accepted by AAAI202

T-PickSeer: Visual Analysis of Taxi Pick-up Point Selection Behavior

Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hot-spot regions of pick-up points, which can make it easier for drivers to pick up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory results in real-world applications because of the changing travel demands and the lack of interpretability. In this paper, we introduce a visual analytics system, T-PickSeer, for taxi company analysts to better explore and understand the pick-up point selection behavior of passengers. We explore massive taxi GPS data and employ an overview-to-detail approach to enable effective analysis of pick-up point selection. Our system provides coordinated views to compare different regularities and characteristics in different regions. Also, our system assists in identifying potential pick-up points and checking the performance of each pick-up point. Three case studies based on a real-world dataset and interviews with experts have demonstrated the effectiveness of our system.Comment: 10 pages, 10 figures; The 10th China Visualization and Visual Analytics Conferenc

STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation

The impressive development of facial manipulation techniques has raised severe public concerns. Identity-aware methods, especially suitable for protecting celebrities, are seen as one of promising face forgery detection approaches with additional reference video. However, without in-depth observation of fake video’s characteristics, most existing identity-aware algorithms are just naive imitation of face verification model and fail to exploit discriminative information. In this article, we argue that it is necessary to take both spatial and temporal perspectives into consideration for adequate inconsistency clues and propose a novel forgery detector named SpatioTemporal IDentity network (STIDNet). To effectively capture heterogeneous spatiotemporal information in a unified formulation, our STIDNet is following a knowledge distillation architecture that the student identity extractor receives supervision from a spatial information encoder (SIE) and a temporal information encoder (TIE) through multiteacher training. Specifically, a regional sensitive identity modelling paradigm is proposed in SIE by introducing facial blending augmentation but with uniform identity label, thus encourage model to focus on spatial discriminative region like outer face. Meanwhile, considering the strong temporal correlation between audio and talking face video, our TIE is devised in a cross-modal pattern that the audio information is introduced to supervise model exploiting temporal personalized movements. Benefit from knowledge transfer from SIE and TIE, STIDNet is able to capture individual’s essential spatiotemporal identity attributes and sensitive to even subtle identity deviation caused by manipulation. Extensive experiments indicate the superiority of our STIDNet compared with previous works. Moreover, we also demonstrate STIDNet is more suitable for real-world implementation in terms of model complexity and reference set size

An optically pumped atomic clock based on a continuous slow cesium beam

Herein, we report the scheme of an optically pumped atomic clock based on a cold cesium atomic beam source. We propose the laser system and physical mechanism of this atomic clock, wherein the atomic beam travels in an upper parabolic trajectory, thereby eliminating the light shift effect. In the experiments, when the length of the free evolution region was 167 mm, the line width of the Ramsey fringe was 37 Hz. When the expected signal-to-noise ratio of the Ramsey fringe that can be achieved is 36,000, the expected short-term frequency stability is about 3.6 × 10–14/√τ, which is significantly higher than that of a conventional optically pumped cesium clock of similar volume

3D Printing Metal Lattice Structure Using Fused Deposition Modeling Process

Metal additive manufacturing (AM) or 3D printing parts are widely used in aerospace, medical and many other industrial fields. With the introduction of metal filament, fused deposition modeling (FDM) technology provides a new approach of 3D printing metal parts, especially for lightweight or lattice structures. This study conducts preliminary research on FDM 3D printing metal lattice structures, with the purpose of validating the process applicability. The maximum overhang angle for FDM green part is studied to extend the borders of “45o rule” for 3D printing parts without adding supports. The influences of printing parameters on the debinding process are analyzed, and insights are provided on designing metal FDM 3D printing lattice parts with specific unit cells. Finally, the shrinkage rate of metal FDM printed parts is summarized and analyzed. In addition, the production cost of metal 3D printing lattice is of great interest for industry. Hence, this study compares the economics and some part defects of the metal FDM process and selective laser melting process (a powder-bed-fusion based AM process), through fabricating selected lattice structures. The advantages and disadvantages of the two methods are summarized, and guidance is proposed on choosing the appropriate 3D printing processes for production

Evaluating the impact of radiofrequency spectroscopy on reducing reoperations after breast conserving surgery: A meta‐analysis

Abstract Background The benefits of breast conserving surgery for breast cancer patients are well established. To achieve adequate margins of excision, intraoperative management of breast margins is a critical factor through reducing reoperation for inadequate positive margin excision and associated morbidity and cost. Radiofrequency spectroscopy is a technology that could significantly reduce positive margins when used intraoperatively as an adjunct to other margin management methods. Methods A meta‐analysis was completed with 10 publications comparing use of radiofrequency spectroscopy technology (MarginProbe) with standard margin assessment procedures. Three randomized controlled studies and seven retrospective studies comparing MarginProbe to historical controls were included. The primary endpoint was reduction of re‐excision rates. Statistical significance level was set at the two‐sided 5% level corresponding to two‐sided 95% confidence intervals (CIs) of the pooled relative risk estimates. Results A total of 2335 patients from 10 publications were included in this meta‐analysis. The overall relative reduction in re‐excision rate was 0.49 (95% CI: 0.38–0.64, p < 0.001). Statistical methods were used to examine publication bias. Conclusion Despite the limited randomized controlled trials available comparing radiofrequency spectroscopy to standard operation procedures, the data from the 10 studies demonstrate a statistically significant reduction in re‐excision rate of 49% for MarginProbe usage, currently the only technology indicated for intraoperative identification of breast cancer tissue at the lumpectomy specimen margin