Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection

Object detectors usually achieve promising results with the supervision of complete instance annotations. However, their performance is far from satisfactory with sparse instance annotations. Most existing methods for sparsely annotated object detection either re-weight the loss of hard negative samples or convert the unlabeled instances into ignored regions to reduce the interference of false negatives. We argue that these strategies are insufficient since they can at most alleviate the negative effect caused by missing annotations. In this paper, we propose a simple but effective mechanism, called Co-mining, for sparsely annotated object detection. In our Co-mining, two branches of a Siamese network predict the pseudo-label sets for each other. To enhance multi-view learning and better mine unlabeled instances, the original image and corresponding augmented image are used as the inputs of two branches of the Siamese network, respectively. Co-mining can serve as a general training mechanism applied to most of modern object detectors. Experiments are performed on MS COCO dataset with three different sparsely annotated settings using two typical frameworks: anchor-based detector RetinaNet and anchor-free detector FCOS. Experimental results show that our Co-mining with RetinaNet achieves 1.4%~2.1% improvements compared with different baselines and surpasses existing methods under the same sparsely annotated setting. Code is available at https://github.com/megvii-research/Co-mining.Comment: Accepted to AAAI 2021. Code is available at https://github.com/megvii-research/Co-minin

Improvements on "Multi-Party Quantum Summation without a Third Party based on dd-Dimensional Bell States"

In 2021, Wu et al. presented a multi-party quantum summation scheme exploiting the entanglement properties of d-dimensional Bell states (Wu et al. in Quantum Inf Process 20:200, 2021). In particular, the authors proposed a three-party quantum summation protocol and then extended their work to a multi-party case. It is claimed that their protocol is secure against outside and participants' attacks. However, this work points out that Wu's protocol has a loophole, i.e., two or more dishonest participants who meet a specific location relationship can conspire to obtain the private inputs of some honest participants without being detected. Accordingly, improvements are proposed to address these issues

Customer Retention in Chinese Financial Retail Sector Quantitative Research in Shanghai

The aim of the paper is to find out who the most profitable customers for domestic financial institutions to retain, and what those profitable customers preference when choosing simple/complex financial products. The database of 104 questionnaires collected in shanghai is used in the analysis. The result shows that the younger people (aged 35 years old or below) are those most profitable customers with great potential purchasing power on financial products. The result also confirms the differences of choosing criteria between simple financial products and complex financial products

The APC Algorithm of Solving Large-Scale Linear Systems: A Generalized Analysis

A new algorithm called accelerated projection-based consensus (APC) has recently emerged as a promising approach to solve large-scale systems of linear equations in a distributed fashion. The algorithm adopts the federated architecture, and attracts increasing research interest; however, it's performance analysis is still incomplete, e.g., the error performance under noisy condition has not yet been investigated. In this paper, we focus on providing a generalized analysis by the use of the linear system theory, such that the error performance of the APC algorithm for solving linear systems in presence of additive noise can be clarified. We specifically provide a closed-form expression of the error of solution attained by the APC algorithm. Numerical results demonstrate the error performance of the APC algorithm, validating the presented analysis.Comment: 6 pages, 3 figure