Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"

Large-scale variations still pose a challenge in unconstrained face detection. To the best of our knowledge, no current face detection algorithm can detect a face as large as 800 x 800 pixels while simultaneously detecting another one as small as 8 x 8 pixels within a single image with equally high accuracy. We propose a two-stage cascaded face detection framework, Multi-Path Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a deep neural network with a classic learning strategy, to tackle this challenge. The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes faces at three different scales. It simultaneously utilizes three parallel outputs of the convolutional feature maps to predict multi-scale candidate face regions. The "atrous" convolution trick (convolution with up-sampled filters) and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN to further boost its performance. The second stage is a Boosted Forests classifier, which utilizes deep facial features pooled from inside the candidate face regions as well as deep contextual features pooled from a larger region surrounding the candidate face regions. This step is included to further remove hard negative samples. Experiments show that this approach achieves state-of-the-art face detection performance on the WIDER FACE dataset "hard" partition, outperforming the former best result by 9.6% for the Average Precision.Comment: 11 pages, 7 figures, to be presented at CRV 201

Relation Based Access Control in Campus Social Network System

AbstractAs one of the most popular network applications, online social network system has gained huge adoption in the past few years. Campus social network system is a special type of social network system which focuses on providing information communication, knowledge sharing, and online collaboration services to campus users in colleges and universities. In this paper, we discuss the design of relation based access control in campus social network system which is decided by the collective efforts system designers, system administrators, and especially users of the system. Generally speaking, relation based access control in campus social network system is defined in terms of users can establish relationships; and they can also assign relation based permissions on information and resources when they release them. It consists of user-centered access control and group-centered access control which deal with access control of information and resources released in users’ personal space and groups’ shared space respectively. Once a campus social network system is put online, access control in it is actually decided by the collective intelligence of its users. Specifically, it's built upon collective intelligence that is reflected through users’ identity, their social relationships and permissions that they set on their profile and created content. In a word, relation based access control in campus social network system adopts a collective intelligence model

Rigidity of 3D spherical caps via μ\mu-bubbles

By using Gromov's $\mu$-bubble technique, we show that the $3$-dimensional spherical caps are rigid under perturbations that do not reduce the metric, the scalar curvature, and the mean curvature along its boundary. Several generalizations of this result will be discussed.Comment: 20 pages, 1 figure, All comments are welcom

Towards Understanding How Self-training Tolerates Data Backdoor Poisoning

Recent studies on backdoor attacks in model training have shown that polluting a small portion of training data is sufficient to produce incorrect manipulated predictions on poisoned test-time data while maintaining high clean accuracy in downstream tasks. The stealthiness of backdoor attacks has imposed tremendous defense challenges in today's machine learning paradigm. In this paper, we explore the potential of self-training via additional unlabeled data for mitigating backdoor attacks. We begin by making a pilot study to show that vanilla self-training is not effective in backdoor mitigation. Spurred by that, we propose to defend the backdoor attacks by leveraging strong but proper data augmentations in the self-training pseudo-labeling stage. We find that the new self-training regime help in defending against backdoor attacks to a great extent. Its effectiveness is demonstrated through experiments for different backdoor triggers on CIFAR-10 and a combination of CIFAR-10 with an additional unlabeled 500K TinyImages dataset. Finally, we explore the direction of combining self-supervised representation learning with self-training for further improvement in backdoor defense.Comment: Accepted at SafeAI 2023: AAAI's Workshop on Artificial Intelligence Safet