Title: Anomaly Detection : A Survey
Speaker: Gang Zhi
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and succinct understanding of the techniques belonging
to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
Supervisor: Handong Liu
Title: EfficientDet: Scalable and Efficient Object Detection
Speaker: Yuchuan An
Model efficiency has become increasingly important in computer vision. In this paper, we systematically study var- ious neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional fea- ture pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these op- timizations, we have developed a new family of object de- tectors, called EfficientDet, which consistently achieve an order-of-magnitude better efficiency than prior art across a wide spectrum of resource constraints. In particular, with- out bells and whistles, our EfficientDet-D7 achieves state- of-the-art 51.0 mAP on COCO dataset with 52M param- eters and 326B FLOPS1, being 4x smaller and using 9.3x fewer FLOPS yet still more accurate (+0.3% mAP) than the best previous detector.
Supervisor: Yongfang Dai
Time：16:00 December 3, 2020
Address：MingLi Buliding C1102
Chair: Guogen Tang