WHEN SMART MACHINES ARE BIASED Olga Russakovsky Is Working to Change That
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What's the Point: Semantic Segmentation with Point Supervision
What's the Point: Semantic Segmentation with Point Supervision Amy Bearman1, Olga Russakovsky2, Vittorio Ferrari3, and Li Fei-Fei1 1 Stanford University fabearman,[email protected] 2 Carnegie Mellon University [email protected] 3 University of Edinburgh [email protected] Abstract. The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost. Detailed per-pixel annotations enable training accurate models but are very time- consuming to obtain; image-level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image-level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and object- ness potential yields an improvement of 12:9% mIOU over image-level supervision. Further, we demonstrate that models trained with point- level supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget. Keywords: semantic segmentation, weak supervision, data annotation 1 Introduction At the forefront of visual recognition is the question of how to effectively teach computers new concepts. Algorithms trained from carefully annotated data enjoy better performance than their weakly supervised counterparts (e.g., [1] vs. [2], [3] vs. [4], [5] vs. [6]), yet obtaining such data is very time-consuming [5, 7]. It is particularly difficult to collect training data for semantic segmentation, i.e., the task of assigning a class label to every pixel in the image. -
Feature Mining: a Novel Training Strategy for Convolutional Neural
Feature Mining: A Novel Training Strategy for Convolutional Neural Network Tianshu Xie1 Xuan Cheng1 Xiaomin Wang1 [email protected] [email protected] [email protected] Minghui Liu1 Jiali Deng1 Ming Liu1* [email protected] [email protected] [email protected] Abstract In this paper, we propose a novel training strategy for convo- lutional neural network(CNN) named Feature Mining, that aims to strengthen the network’s learning of the local feature. Through experiments, we find that semantic contained in different parts of the feature is different, while the network will inevitably lose the local information during feedforward propagation. In order to enhance the learning of local feature, Feature Mining divides the complete feature into two com- plementary parts and reuse these divided feature to make the network learn more local information, we call the two steps as feature segmentation and feature reusing. Feature Mining is a parameter-free method and has plug-and-play nature, and can be applied to any CNN models. Extensive experiments demonstrate the wide applicability, versatility, and compatibility of our method. 1 Introduction Figure 1. Illustration of feature segmentation used in our method. In each iteration, we use a random binary mask to Convolution neural network (CNN) has made significant divide the feature into two parts. progress on various computer vision tasks, 4.6, image classi- fication10 [ , 17, 23, 25], object detection [7, 9, 22], and seg- mentation [3, 19]. However, the large scale and tremendous parameters of CNN may incur overfitting and reduce gener- training strategy for CNN for strengthening the network’s alizations, that bring challenges to the network training. -
JULIA BUMKE Local Address: Permanent Address: [email protected] 15 Irving Street, Apt
JULIA BUMKE Local Address: Permanent Address: [email protected] 15 Irving Street, Apt. 2 362 Morris Ave. 201.486.7197 Somerville, MA 02144 Mountain Lakes, NJ 07046 ___________________________________________________________________________________________________________________________________________________________________________________________________________ EDUCATION Institute for Advanced Theater Training at Harvard University, Cambridge, MA | American Repertory Theater/Moscow Art Theater School. M.F.A. Candidate | Dramaturgy and Theater Studies, June 2015. Princeton University, Princeton, NJ. A.B., Magna cum laude | United States History, with Theater and American Studies concentrations, June 2013. • Academic Senior Thesis, From Upstarts to Institutions: How W. McNeil Lowry Transformed America’s Nonprofit Theaters. Sean Wilentz, Advisor. • Creative Independent Thesis, Program in Theater: Directed Stephen Sondheim and James Lapine’s Sunday in the Park With George, Berlind Theatre, McCarter Theater Center. Tim Vasen, Advisor. HONORS AND PUBLICATIONS • Francis LeMoyne Page Theater Award | Princeton University Program in Theater, Spring 2013. • Asher Hinds Prize for Excellence in American Studies | Princeton University Program in American Studies, Spring 2013. • Published What History Can Teach Us About Arts Philanthropy in the Age of Obama | HowlRound: Journal of the Theater Commons at Emerson College, January 2013. • Published Rock ’n’ Revolution: How the Prague Spring’s Cultural Liberalism Transformed Czech Human Rights | -
Arxiv:2004.07999V4 [Cs.CV] 23 Jul 2021
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets Angelina Wang · Alexander Liu · Ryan Zhang · Anat Kleiman · Leslie Kim · Dora Zhao · Iroha Shirai · Arvind Narayanan · Olga Russakovsky Abstract Machine learning models are known to per- petuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models are deployed. Our work tackles this issue and enables the preemptive analysis of large-scale datasets. REVISE (REvealing VIsual bi- aSEs) is a tool that assists in the investigation of a visual dataset, surfacing potential biases along three Fig. 1: Our tool takes in as input a visual dataset and its dimensions: (1) object-based, (2) person-based, and (3) annotations, and outputs metrics, seeking to produce geography-based. Object-based biases relate to the size, insights and possible actions. context, or diversity of the depicted objects. Person- based metrics focus on analyzing the portrayal of peo- ple within the dataset. Geography-based analyses con- sider the representation of different geographic loca- the visual world, representing a particular distribution tions. These three dimensions are deeply intertwined of visual data. Since then, researchers have noted the in how they interact to bias a dataset, and REVISE under-representation of object classes (Buda et al., 2017; sheds light on this; the responsibility then lies with Liu et al., 2009; Oksuz et al., 2019; Ouyang et al., 2016; the user to consider the cultural and historical con- Salakhutdinov et al., 2011; J. Yang et al., 2014), ob- text, and to determine which of the revealed biases ject contexts (Choi et al., 2012; Rosenfeld et al., 2018), may be problematic. -
AI for All by Mr. S. Arjun
AI for All Mr. S. Arjun 2nd Year, B.Tech CSE (Hons.), Lovely Professional University, Punjab [email protected] What is AI Many problems in the world that once seemed impossible for a computer to tackle without human intervention are solved today with Artificial Intelligence. We are witnessing the second major wave of AI, disrupting a plethora of unrelated fields such as health, ethics, politics, and economy. These intelligent systems prove that machines too can learn from experience, adapt, and make meaningful decisions. While the first wave was driven by rule-based systems where experts in performing a task handcrafted a set of rules for machines to follow, thus emulating intelligence, the second wave is driven by huge amounts of data, coupled with algorithms that enable machines to recognize patterns and learn from experience. Impact Though at a very early stage, AI has already made a profound impact on our lives. However, the nature of the impact it has made on different establishments is as unique as itself. On Enterprises Enterprises are seen to make the best out of the second wave of AI, primarily owing to the abundance of data they already collect every second. Deep Learning - a subset of Machine Learning, that allows recognizing and learning from complex patterns in data - feeds on huge magnitudes of data in the order of millions of samples. Large enterprises have the benefit of being capable of collecting such data in-house for their own systems, products, and services. Today, numerous such organizations that operate at scale are starting to use AI to automate workflows, streamline their operations, and optimize production. -
The Fastest of Them
00paw0424_coverfinalNOBOX_00paw0707_Cov74 4/11/13 10:16 AM Page 1 Science and art Princeton come together Anne-Marie Slaughter ’80 Alumni to become emerita The war in Iraq: What Weekly was accomplished? THE FASTEST OF THEM ALL DAN FEYER ’99, KING OF CROSSWORDS April 24, 2013 • paw.princeton.edu PAW_1746_AD_dc_v1.4.qxp:Layout 1 4/2/13 8:07 AM Page 1 Welcome to 1746 Welcome to a long tradition of visionary Now, the 1746 Society carries that people who have made Princeton one of the promise forward to 2013 and beyond with top universities in the world. planned gifts, supporting the University’s In 1746, Princeton’s founders saw the future through trusts, bequests, and other bright promise of a college in New Jersey. long-range generosity. We welcome our newest 1746 Society members.And we invite you to join us. Christopher K. Ahearn Marie Horwich S64 Richard R. Plumridge ’67 Stephen E. Smaha ’73 Layman E. Allen ’51 William E. Horwich ’64 Peter Randall ’44 William W. Stowe ’68 Charles E. Aubrey ’60 Mrs. H. Alden Johnson Jr. W53 Emily B. Rapp ’84 Sara E. Turner ’94 John E. Bartlett ’03 Anne Whitfield Kenny Martyn R. Redgrave ’74 John W. van Dyke ’65 Brooke M. Barton ’75 Mrs. C. Frank Kireker Jr. W39 Benjamin E. Rice *11 Yung Wong ’61 David J. Bennett *82 Charles W. Lockyer Jr. *71 Allen D. Rushton ’51 James K. H. Young ’50 James M. Brachman ’55 John T. Maltsberger ’55 Francis D. Ruyak ’73 Anonymous (1) Bruce E. Burnham ’60 Andree M. Marks Jay M. -
Crowdsourcing in Computer Vision
Foundations and Trends R in Computer Graphics and Vision Vol. 10, No. 3 (2014) 177–243 c 2016 A. Kovashka, O. Russakovsky, L. Fei-Fei and K. Grauman DOI: 10.1561/0600000073 Crowdsourcing in Computer Vision Adriana Kovashka Olga Russakovsky University of Pittsburgh Carnegie Mellon University [email protected] [email protected] Li Fei-Fei Kristen Grauman Stanford University University of Texas at Austin [email protected] [email protected] Contents 1 Introduction 178 2 What annotations to collect 181 2.1 Visual building blocks . 182 2.2 Actions and interactions . 191 2.3 Visual story-telling . 198 2.4 Annotating data at different levels . 204 3 How to collect annotations 205 3.1 Interfaces for crowdsourcing and task managers . 205 3.2 Labeling task design . 207 3.3 Evaluating and ensuring quality . 211 4 Which data to annotate 215 4.1 Active learning . 215 4.2 Interactive annotation . 221 5 Conclusions 226 References 228 ii Abstract Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing plat- forms offer an inexpensive method to capture human knowledge and un- derstanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. -
The Pearson Global Forum
The Pearson Global Forum FORUM REPORT October 4–5, 2018 Authored by Bridget Burns, Jordan Ernstsen, Rebecca Ernstsen, Elaine Li, Lauren Manning, Evan Trowbridge Welcome The Pearson Global Forum The most devastating conflicts raging across the globe are not wars between nations, but violent breakdowns of social order. When the institutions that bind people together and govern how they interact with one another are illegitimate, conflict emerges creating significant instability. This instability is a by-product of the autocratic On behalf of The Pearson Institute for the Study and Resolution of Global Conflicts, I’d like to thank those who regimes that plague such societies and which fail to invest in education, infrastructure and the health and welfare made possible the inaugural Pearson Global Forum. The objective of this paramount gathering was to bring of their citizens. Resources are withheld or wasted. Poverty takes root. Grievances mount. together scholars, leaders, and practitioners to discuss and debate pressing issues of global conflict, social order, and how to build and sustain peace. Such a situation can lead to social break downs, conflict and violence, the creation of economic crises and drive The Pearson Institute for the Study and Resolution of Global Conflicts was established through a grant from unprecedented global displacement. From Colombia to Nigeria to Afghanistan and the Democratic Republic the Thomas L. Pearson and Pearson Family Members Foundation and is dedicated to contributing to a world of Congo, it is the illegitimacy of the social order—the norms by which we define ourselves and our roles in more at peace through research, education, and engagement. -
Cornernet-Lite- Efficient Keypoint Based Object Detection
H. LAW, Y. TANG, O. RUSSAKOVSKY, J. DENG: CORNERNET-LITE 1 CornerNet-Lite: Efficient Keypoint-Based Object Detection Hei Law Department of Computer Science [email protected] Princeton University Yun Tang Princeton, NJ USA [email protected] Olga Russakovsky [email protected] Jia Deng [email protected] Abstract Keypoint-based methods are a relatively new paradigm in object detection, eliminat- ing the need for anchor boxes and offering a simplified detection framework. Keypoint- based CornerNet achieves state of the art accuracy among single-stage detectors. How- ever, this accuracy comes at high processing cost. In this work, we tackle the problem of efficient keypoint-based object detection and introduce CornerNet-Lite. CornerNet-Lite is a combination of two efficient variants of CornerNet: CornerNet-Saccade, which uses an attention mechanism to eliminate the need for exhaustively processing all pixels of the image, and CornerNet-Squeeze, which introduces a new compact backbone architecture. Together these two variants address the two critical use cases in efficient object detection: improving efficiency without sacrificing accuracy, and improving accuracy at real-time efficiency. CornerNet-Saccade is suitable for offline processing, improving the efficiency arXiv:1904.08900v2 [cs.CV] 16 Sep 2020 of CornerNet by 6.0x and the AP by 1.0% on COCO. CornerNet-Squeeze is suitable for real-time detection, improving both the efficiency and accuracy of the popular real-time detector YOLOv3 (34.4% AP at 30ms for CornerNet-Squeeze compared to 33.0% AP at 39ms for YOLOv3 on COCO). Together these contributions for the first time reveal the potential of keypoint-based detection to be useful for applications requiring processing efficiency. -
Managing Racist Pasts the Black Justice League’S Demand for Inclusion and Its Challenge to the Promise of Diversity at Princeton University
Managing Racist Pasts the Black Justice League’s Demand for Inclusion and Its Challenge to the Promise of Diversity at Princeton University Tomoyo Joshi Submitted to the Institute of Gender, Sexuality & Feminist Studies of Oberlin College In partial fulfillment of the requirements for the degree of Bachelor of Arts Table of Contents TABLE OF CONTENTS ............................................................................................................................ II ACKNOWLEDGEMENTS ........................................................................................................................ III INTRODUCTION .................................................................................................................................... 4 METHODS ................................................................................................................................................... 5 WHY IS THIS PROJECT FEMINIST? ..................................................................................................................... 7 LITERATURE REVIEW .................................................................................................................................... 10 PART 1: THE DISCOURSE OF DIVERSITY IN PRINCETON UNIVERSITY’S “MANY VOICES, ONE FUTURE” WEBSITE ............................................................................................................................................. 12 DIVERSITY AS COMMODITY: MINORITY DIFFERENCE IS INDIVIDUALIZED AND CONSUMED ....................................... -
Strengthening.Human Rights Monitoring Missions An
STRENGTHENING.HUMAN RIGHTS MONITORING MISSIONS AN OPTIONS PAPER PREPARED FOR THE OFFICE OF TRANSITION INITIATIVES BUREAU FOR HUMANITARIAN RESPONSE UNITED STATES AGENCY FOR INTERNATIONAL DEVELOPMENT Stephen Golub December 1995 This paper was prepared for the USAID Office of Transition Initiatives and the consulting fir.m Thunder & Associates, Inc., under USAID Contract No. AEP-5451 I-OO-2050-00. Acknowledgements The author gratefully acknowledges the role of USAID's Office of Transition Initiatives in commissioning this study on human rights monitoring missions, as well as the OTI's timely concern with an issue that could vitally affect many societies and large populations in coming years. I especially benefitted from the very valuable support and feedback provided by OTI's Deputy Director Stephen Morrison and by Johanna Mendelson, also of OTI. In addition, I appreciate the various roles played by OTI Director Rick Barton, Larry Garber of USAID's Bureau for Policy and Program Coordination, Chris Dicken of OTI, Nancy McClintock of Thunder & Associates and Heather McHugh of USAID's Center for Development Information and Evaluation in offering various forms of advice and assistance. Of course, all errors of fact and opinion are solely my own. Contents Executive Summary ....... 1 I. Background . 1 II. Main Findings . 2 III. Main Recommendations. 3 IV. Other Recommendations. 5 V. Recommended Course for Immediate and Subsequent Action. 5 I. Introduction. .. 6 A. Focus of the Assignment . 6 B. Methodology .. .... 7 II. An Overview of Human Rights Monitoring Missions 9 A. Common Characteristics.... 9 B. A Basic Catalogue of Missions 9 C. The Nature of Missions.. 11 III. -
Prism Vol 5 No 3.Pdf
PRISM VOL. 5, NO. 3 2015 A JOURNAL OF THE CENTER FOR COMPLEX OPERATIONS PRISM About VOL. 5, NO. 3 2015 PRISM is published by the Center for Complex Operations. PRISM is a security studies journal chartered to inform members of U.S. Federal agencies, allies, and other partners on complex EDITOR and integrated national security operations; reconstruction and state-building; relevant policy Michael Miklaucic and strategy; lessons learned; and developments in training and education to transform America’s security and development EDITORIAL ASSISTANTS Connor Christenson Talley Lattimore Jeffrey Listerman Communications Giorgio Rajao Constructive comments and contributions are important to us. Direct Hiram Reynolds communications to: COPY EDITORS Editor, PRISM Dale Erickson 260 Fifth Avenue (Building 64, Room 3605) Rebecca Harper Fort Lesley J. McNair Christoff Luehrs Washington, DC 20319 Nathan White Telephone: (202) 685-3442 DESIGN DIRecTOR FAX: Carib Mendez (202) 685-3581 Email: [email protected] ADVISORY BOARD Dr. Gordon Adams Dr. Pauline H. Baker Ambassador Rick Barton Contributions Professor Alain Bauer PRISM welcomes submission of scholarly, independent research from security policymakers Dr. Joseph J. Collins (ex officio) and shapers, security analysts, academic specialists, and civilians from the United States and Ambassador James F. Dobbins abroad. Submit articles for consideration to the address above or by email to [email protected] Ambassador John E. Herbst (ex officio) with “Attention Submissions Editor” in the subject line. Dr. David Kilcullen Ambassador Jacques Paul Klein Dr. Roger B. Myerson This is the authoritative, official U.S. Department of Defense edition of PRISM. Dr. Moisés Naím Any copyrighted portions of this journal may not be reproduced or extracted MG William L.