A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

Contours of Inclusion: Inclusive Arts Teaching and Learning

The purpose of this publication is to share models and case examples of the process of inclusive arts curriculum design and evaluation. The first section explains the conceptual and curriculum frameworks that were used in the analysis and generation of the featured case studies (i.e. Understanding by Design, Differentiated Instruction, and Universal Design for Learning). Data for the cases studies was collected from three urban sites (i.e. Los Angeles, San Francisco, and Boston) and included participant observations, student and teacher interviews, curriculum documentation, digital documentation of student learning, and transcripts from discussion forum and teleconference discussions from a professional learning community.The initial case studies by Glass and Barnum use the curricular frameworks to analyze and understand what inclusive practices look like in two case studies of arts-in-education programs that included students with disabilities. The second set of precedent case studies by Kronenberg and Blair, and Jenkins and Agois Hurel uses the frameworks to explain their process of including students by providing flexible arts learning options to support student learning of content standards. Both sets of case studies illuminate curricular design decisions and instructional strategies that supported the active engagement and learning of students with disabilities in educational settings shared with their peers. The second set of cases also illustrate the reflective process of using frameworks like Universal Design for Learning (UDL) to guide curricular design, responsive instructional differentiation, and the use of the arts as a rich, meaningful, and engaging option to support learning. Appended are curriculum design and evaluation tools. (Individual chapters contain references.