Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation

We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.Comment: CVPR 201

Exploring the Relationship of Personality and Social Support Types among College Students

This study investigated the relationship between Big Five personality traits and social support types among college students. Data was collected from 189 university students from a research institution in the US. Findings indicated that learners who have Agreeableness and Extraversion personality traits seek for more broadly social support resources in college study and life. Additionally, learners who have Neuroticism traits are negatively associated with all social support resources. It is expected that this study would assist college faculty and student affairs professionals to better understand the differences of personality traits among college students and craft feasible strategies so as to encourage these students to seek sufficient social support resources on their personal development. Keywords: Big Five personality, college students, social support, higher education DOI: 10.7176/JEP/11-31-03 Publication date: November 30th 202

Co-interest Person Detection from Multiple Wearable Camera Videos

Wearable cameras, such as Google Glass and Go Pro, enable video data collection over larger areas and from different views. In this paper, we tackle a new problem of locating the co-interest person (CIP), i.e., the one who draws attention from most camera wearers, from temporally synchronized videos taken by multiple wearable cameras. Our basic idea is to exploit the motion patterns of people and use them to correlate the persons across different videos, instead of performing appearance-based matching as in traditional video co-segmentation/localization. This way, we can identify CIP even if a group of people with similar appearance are present in the view. More specifically, we detect a set of persons on each frame as the candidates of the CIP and then build a Conditional Random Field (CRF) model to select the one with consistent motion patterns in different videos and high spacial-temporal consistency in each video. We collect three sets of wearable-camera videos for testing the proposed algorithm. All the involved people have similar appearances in the collected videos and the experiments demonstrate the effectiveness of the proposed algorithm.Comment: ICCV 201

Exploring Robust Features for Improving Adversarial Robustness

While deep neural networks (DNNs) have revolutionized many fields, their fragility to carefully designed adversarial attacks impedes the usage of DNNs in safety-critical applications. In this paper, we strive to explore the robust features which are not affected by the adversarial perturbations, i.e., invariant to the clean image and its adversarial examples, to improve the model's adversarial robustness. Specifically, we propose a feature disentanglement model to segregate the robust features from non-robust features and domain specific features. The extensive experiments on four widely used datasets with different attacks demonstrate that robust features obtained from our model improve the model's adversarial robustness compared to the state-of-the-art approaches. Moreover, the trained domain discriminator is able to identify the domain specific features from the clean images and adversarial examples almost perfectly. This enables adversarial example detection without incurring additional computational costs. With that, we can also specify different classifiers for clean images and adversarial examples, thereby avoiding any drop in clean image accuracy.Comment: 12 pages, 8 figure

Exploring the Characteristics of Adults’ Online Learning Activities: a Case Study of EdX Online Institute

Online learning has become a prevailing trend among adult learners. Therefore, this study investigated the learning time preference and the relationship between the course completion and learning activities among adult learners based on data from one online learning platform. Results indicate that a periodical fluctuation of participating online course study exists among adult learners. Additionally, the activity of posting on the discussion board is a main learning activity factor that influences their online course completion. It is expected that this study would help online learning system designers, education administrators and instructors to better understand the characteristics of adult learners and their learning activities to provide better accessibility and flexibility in online learning environments for them