Submission Data for 2017 CORE Conference Re-Ranking Process Computer Vision and Pattern Recognition
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![Arxiv:2108.09823V1 [Cs.AI] 22 Aug 2021 1 Introduction](https://docslib.b-cdn.net/cover/6994/arxiv-2108-09823v1-cs-ai-22-aug-2021-1-introduction-246994.webp)
Arxiv:2108.09823V1 [Cs.AI] 22 Aug 2021 1 Introduction
Embodied AI-Driven Operation of Smart Cities: A Concise Review Farzan Shenavarmasouleh1, Farid Ghareh Mohammadi1, M. Hadi Amini2, and Hamid R. Arabnia1 1:Department of Computer Science, Franklin College of arts and sciences, University of Georgia, Athens, GA, USA 2: School of Computing & Information Sciences, College of Engineering & Computing, Florida International University, Miami, FL, USA Emails: [email protected], [email protected], amini@cs.fiu.edu, [email protected] August 24, 2021 Abstract A smart city can be seen as a framework, comprised of Information and Communication Technologies (ICT). An intelligent network of connected devices that collect data with their sensors and transmit them using wireless and cloud technologies in order to communicate with other assets in the ecosystem plays a pivotal role in this framework. Maximizing the quality of life of citizens, making better use of available resources, cutting costs, and improving sustainability are the ultimate goals that a smart city is after. Hence, data collected from these connected devices will continuously get thoroughly analyzed to gain better insights into the services that are being offered across the city; with this goal in mind that they can be used to make the whole system more efficient. Robots and physical machines are inseparable parts of a smart city. Embodied AI is the field of study that takes a deeper look into these and explores how they can fit into real-world environments. It focuses on learning through interaction with the surrounding environment, as opposed to Internet AI which tries to learn from static datasets. Embodied AI aims to train an agent that can See (Computer Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement Learning), and Reason (General Intelligence), all at the same time. -
Zero-Shot Compositional Concept Learning
Zero-Shot Compositional Concept Learning Guangyue Xu Parisa Kordjamshidi Joyce Y. Chai Michigan State University Michigan State University University of Michigan [email protected] [email protected] [email protected] Abstract Train Phase: Concept of Sliced Concept of Apple In this paper, we study the problem of rec- Sliced Tomato Sliced Bread Sliced Cake Diced Apple Ripe Apple Peeled Apple Localize, Learn and Compose Regional ognizing compositional attribute-object con- Visual Features cepts within the zero-shot learning (ZSL) framework. We propose an episode-based cross-attention (EpiCA) network which com- Test Phase: bines merits of cross-attention mechanism and Sliced Apple Compose the Learnt Regional Visual Diced Pizza Features episode-based training strategy to recognize … novel compositional concepts. Firstly, EpiCA bases on cross-attention to correlate concept- Figure 1: Given the concepts of sliced and apple in the visual information and utilizes the gated pool- training phase, our target is to recognize the novel com- ing layer to build contextualized representa- positional concept slice apple which doesn’t appear in tions for both images and concepts. The up- the training set by decomposing, grounding and com- dated representations are used for a more in- posing concept-related visual features. depth multi-modal relevance calculation for concept recognition. Secondly, a two-phase episode training strategy, especially the trans- with other objects or attributes, the combination ductive phase, is adopted to utilize unlabeled of this attribute-object pair is not observed in the test examples to alleviate the low-resource training set. learning problem. Experiments on two widely- used zero-shot compositional learning (ZSCL) This is a challenging problem, because objects benchmarks have demonstrated the effective- with different attributes often have a significant di- ness of the model compared with recent ap- versity in their visual features. -
I Hope This Is Helpful": Understanding Crowdworkers’ Challenges and Motivations for an Image Description Task
105 "I Hope This Is Helpful": Understanding Crowdworkers’ Challenges and Motivations for an Image Description Task RACHEL N. SIMONS, Texas Woman’s University DANNA GURARI, The University of Texas at Austin KENNETH R. FLEISCHMANN, The University of Texas at Austin AI image captioning challenges encourage broad participation in designing algorithms that automatically create captions for a variety of images and users. To create large datasets necessary for these challenges, researchers typically employ a shared crowdsourcing task design for image captioning. This paper discusses findings from our thematic analysis of 1,064 comments left by Amazon Mechanical Turk workers usingthis task design to create captions for images taken by people who are blind. Workers discussed difficulties in understanding how to complete this task, provided suggestions of how to improve the task, gave explanations or clarifications about their work, and described why they found this particular task rewarding or interesting. Our analysis provides insights both into this particular genre of task as well as broader considerations for how to employ crowdsourcing to generate large datasets for developing AI algorithms. CCS Concepts: • Information systems → Crowdsourcing; • Computing methodologies → Computer vision; Computer vision tasks; Image representations; Machine learning; • Human- centered computing → Accessibility. Additional Key Words and Phrases: Crowdsourcing; Computer Vision; Artificial Intelligence; Image Captioning; Accessibility; Amazon Mechanical Turk -
Teaching Visual Recognition Systems
Teaching visual recognition systems Kristen Grauman Department of Computer Science University of Texas at Austin Work with Sudheendra Vijayanarasimhan, Prateek Jain, Devi Parikh, Adriana Kovashka, and Jeff Donahue Visual categories Beyond instances, need to recognize and detect classes of visually and semantically related… Objects Scenes Activities Kristen Grauman, UT-Austin Learning-based methods Last ~10 years: impressive strides by learning appearance models (usually discriminative). Novel image Annotator Training images Car Non-car Kristen Grauman, UT-Austin Exuberance for image data (and their category labels) 14M images 1K+ labeled object categories [Deng et al. 2009-2012] ImageNet 80M images 53K noisily labeled object categories [Torralba et al. 2008] 80M Tiny Images 131K images 902 labeled scene categories 4K labeled object categories SUN Database [Xiao et al. 2010] Kristen Grauman, UT-Austin And yet… • More data ↔ more accurate visual models? • Which images should be labeled? X. Zhu, C. Vondrick, D. Ramanan and C. Fowlkes. Do We Need More Training Data or Better Models for Object Detection? BMVC 2012. Kristen Grauman, UT-Austin And yet… • More data ↔ more accurate visual models? X. Zhu, C. Vondrick, D. Ramanan and C. Fowlkes. Do We Need More Training Data or Better Models for Object Detection? BMVC 2012. Kristen Grauman, UT-Austin And yet… • More data ↔ more accurate visual models? • Which images should be labeled? • Are labels enough to teach visual concepts? “This image has a cow in it.” Human annotator [tiny image montage -
M2P3: Multimodal Multi-Pedestrian Path Prediction by Self-Driving Cars with Egocentric Vision
M2P3: Multimodal Multi-Pedestrian Path Prediction by Self-Driving Cars With Egocentric Vision Atanas Poibrenski Matthias Klusch iMotion Germany GmbH German Research Center for Artificial Intelligence (DFKI) German Research Center for Artificial Intelligence (DFKI) Saarbrücken, Germany Saarbrücken, Germany [email protected] [email protected] Igor Vozniak Christian Müller German Research Center for Artificial Intelligence (DFKI) German Research Center for Artificial Intelligence (DFKI) Saarbrücken, Germany Saarbrücken, Germany [email protected] [email protected] ABSTRACT effective and efficient multi-pedestrian path prediction intraffic Accurate prediction of the future position of pedestrians in traffic scenes by AVs. In fact, there is a plethora of solution approaches scenarios is required for safe navigation of an autonomous vehicle for this problem [65] to be employed in advanced driver assistance but remains a challenge. This concerns, in particular, the effective systems of AVs. Currently, these systems enable an AV to detect if and efficient multimodal prediction of most likely trajectories of a pedestrian is actually in the direction of travel, warn the control tracked pedestrians from egocentric view of self-driving car. In this driver and even stop automatically. Other approaches would allow paper, we present a novel solution, named M2P3, which combines a ADAS to predict whether the pedestrian is going to step on the conditional variational autoencoder with recurrent neural network street, or not [46]. encoder-decoder architecture in order to predict a set of possible The multimodality of multi-pedestrian path prediction in ego-view future locations of each pedestrian in a traffic scene. The M2P3 is a challenge and hard to handle by many deep learning (DL) mod- system uses a sequence of RGB images delivered through an internal els for many-to-one mappings. -
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift Remi Tachet des Combes∗ Han Zhao∗ Microsoft Research Montreal D. E. Shaw & Co. Montreal, QC, Canada New York, NY, USA [email protected] [email protected] Yu-Xiang Wang Geoff Gordon UC Santa Barbara Microsoft Research Montreal Santa Barbara, CA, USA Montreal, QC, Canada [email protected] [email protected] Abstract Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting, by learning domain-invariant representations. However, recent work has shown limitations of this approach when label distributions differ between the source and target domains. In this paper, we propose a new assumption, generalized label shift (GLS), to improve robustness against mismatched label distributions. GLS states that, conditioned on the label, there exists a representation of the input that is invariant between the source and target domains. Under GLS, we provide theoretical guarantees on the transfer performance of any classifier. We also devise necessary and sufficient conditions for GLS to hold, by using an estimation of the relative class weights between domains and an appropriate reweighting of samples. Our weight estimation method could be straightforwardly and generically applied in existing domain adaptation (DA) algorithms that learn domain-invariant representations, with small computational overhead. In particular, we modify three DA algorithms, JAN, DANN and CDAN, and evaluate their performance on standard and artificial DA tasks. Our algorithms outperform the base versions, with vast improvements for large label distribution mismatches. Our code is available at https://tinyurl.com/y585xt6j. 1 Introduction arXiv:2003.04475v3 [cs.LG] 11 Dec 2020 In spite of impressive successes, most deep learning models [24] rely on huge amounts of labelled data and their features have proven brittle to distribution shifts [43, 59]. -
Kernel Methods for Unsupervised Domain Adaptation
Kernel Methods for Unsupervised Domain Adaptation by Boqing Gong A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) August 2015 Copyright 2015 Boqing Gong Acknowledgements This thesis concludes a wonderful four-year journey at USC. I would like to take the chance to express my sincere gratitude to my amazing mentors and friends during my Ph.D. training. First and foremost I would like to thank my adviser, Prof. Fei Sha, without whom there would be no single page of this thesis. Fei is smart, knowledgeable, and inspiring. Being truly fortunate, I got an enormous amount of guidance and support from him, financially, academically, and emotionally. He consistently and persuasively conveyed the spirit of adventure in research and academia of which I appreciate very much and from which my interests in trying out the faculty life start. On one hand, Fei is tough and sets a high standard on my research at “home”— the TEDS lab he leads. On the other hand, Fei is enthusiastically supportive when I reach out to conferences and the job market. These combined make a wonderful mix. I cherish every mind-blowing discussion with him, which sometimes lasted for hours. I would like to thank our long-term collaborator, Prof. Kristen Grauman, whom I see as my other academic adviser. Like Fei, she has set such a great model for me to follow on the road of becoming a good researcher. She is a deep thinker, a fantastic writer, and a hardworking professor. -
Building Maps Using Vision for Safe Local Mobile Robot Navigation
Copyright by Aniket Murarka 2009 The Dissertation Committee for Aniket Murarka certifies that this is the approved version of the following dissertation: Building Safety Maps using Vision for Safe Local Mobile Robot Navigation Committee: Benjamin J. Kuipers, Supervisor Kristen Grauman Risto Miikkulainen Brian Stankiewicz Peter Stone Building Safety Maps using Vision for Safe Local Mobile Robot Navigation by Aniket Murarka, M.S. Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy The University of Texas at Austin August 2009 To my family Acknowledgments First and foremost I would like to thank my advisor, Ben Kuipers. I often wonder if I would have been able to cultivate my perspective on science – to take a broader view of the problems I am working on, to look for the right questions to ask, and to understand the value of my and other work – if it had not been for Ben. I greatly appreciate his patience in guiding me while at the same time giving me the freedom to pursue my line of thought and then gently leading me back to the right answer if I drifted too far. I would like to thank my committee members, Peter Stone, Risto Miikkulainen, Kristen Grauman, and Brian Stankiewicz for their well thought out questions and sugges- tions during my proposal and defense. In addition, my individual interactions with them have helped me as a researcher. Peter’s class on multi-agent systems showed me how one could build a complex system from scratch. -
Dark, Beyond Deep: a Paradigm Shift to Cognitive AI with Humanlike Common Sense
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense Yixin Zhua,∗, Tao Gaoa, Lifeng Fana, Siyuan Huanga, Mark Edmondsa, Hangxin Liua, Feng Gaoa, Chi Zhanga, Siyuan Qia, Ying Nian Wua, Joshua B. Tenenbaumb, Song-Chun Zhua aCenter for Vision, Cognition, Learning, and Autonomy (VCLA), UCLA bCenter for Brains, Minds, and Machines (CBMM), MIT Abstract Recent progress in deep learning is essentially based on a “big data for small tasks” paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a “small data for big tasks” paradigm, wherein a single artificial intelligence (AI) system is challenged to develop “common sense,” enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of “why” and “how,” beyond the dominant “what” and “where” framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the “dark matter” of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. -
Curriculum Vitae
Sagnik Majumder ♂ - 25 years (+1)-5129034773 [email protected] Webpage GitHub Education May 2025 University of Texas at Austin (UT)-Austin, Texas, United States Doctor of Philosophy and Master of Science in Computer Science; GPA: 3.96/4.0 July 2018 Birla Institute of Technology and Science (BITS)-Pilani, Pilani, Rajasthan, India Bachelor of Engineering (Hons.) in Electronics and Instrumentation Thesis: “Neural Architecture Meta-learning via Reinforcement” | Advisor: Prof. V. Ramesh GPA: 9.55/10, Distinction and ranked 2nd out of 100 students Internships and Research Jan 2020 - present Research assistant at UT Austin Vision Lab Advisor - Prof. Kristen Grauman • Introduced the new task of active audio-visual source separation and proposed a novel deep RL based method to solve it • Built an RL based hierarchical audio-visual navigation system that combined a novel end-to-end waypoint prediction model and an geometric motion plan- ner, and also leveraged a novel acoustic map design Aug 20219 - Dec 2019 Student in Graduate Natural Language Processing course at UT Austin Advisor - Prof. Greg Durrett • Built a novel state-of-the-art adversarial defense for question answering that uses a model-agnostic answer reranking mechanism by computing named en- tity overlap between questions and candidate answers Jan 2018 - May 2019 Research assistant at Goethe University Advisor - Prof. Visvanathan Ramesh • Built a continual learning framework by integrating a variational autoencoder based deep generative replay model and a statistical outlier rejection tech- nique (OpenSet) that outperformed the state-of-the-art • Curated a novel concrete defect dataset; meta-learned task specific neural architectures that outperformed strong baselines and transfer-learned models May 2017 - May 2019 Research intern at Frankfurt Institute for Advanced Studies Advisor - Prof. -
![Emergence of Exploratory Look-Around Behaviors Through Active Observation Completion Arxiv:1906.11407V1 [Cs.CV] 27 Jun 2019](https://docslib.b-cdn.net/cover/8900/emergence-of-exploratory-look-around-behaviors-through-active-observation-completion-arxiv-1906-11407v1-cs-cv-27-jun-2019-1948900.webp)
Emergence of Exploratory Look-Around Behaviors Through Active Observation Completion Arxiv:1906.11407V1 [Cs.CV] 27 Jun 2019
Emergence of Exploratory Look-Around Behaviors through Active Observation Completion ∗ Santhosh K. Ramakrishnan,1;3yz , Dinesh Jayaraman,2y , Kristen Grauman1;3 1Department of Computer Science, University of Texas at Austin, Texas 2Department of Electrical Engineering and Computer Science, University of California, Berkeley 3Facebook AI Research, Austin, Texas zTo whom correspondence should be addressed; E-mail: [email protected] yEqual contribution Standard computer vision systems assume access to intelligently captured in- puts (e.g., photos from a human photographer), yet autonomously capturing good observations is a major challenge in itself. We address the problem of learning to look around: how can an agent learn to acquire informative vi- sual observations? We propose a reinforcement learning solution, where the agent is rewarded for reducing its uncertainty about the unobserved portions of its environment. Specifically, the agent is trained to select a short sequence arXiv:1906.11407v1 [cs.CV] 27 Jun 2019 of glimpses after which it must infer the appearance of its full environment. To address the challenge of sparse rewards, we further introduce sidekick pol- icy learning, which exploits the asymmetry in observability between training and test time. The proposed methods learn observation policies that not only ∗This manuscript has been accepted for publication in Science Robotics. This version has not undergone final editing. Please refer to the complete version of record at https://robotics.sciencemag.org/ content/4/30/eaaw6326. The manuscript may not be reproduced or used in any manner that does not fall within the fair use provisions of the Copyright Act without the prior, written permission of AAAS. -
Professor Jitendra Malik Arthur J. Chick Professor, Department of EECS University of California at Berkeley, CA 94720 Email: [email protected], Phone: (510) 642-7597
Professor Jitendra Malik Arthur J. Chick Professor, Department of EECS University of California at Berkeley, CA 94720 Email: [email protected], Phone: (510) 642-7597 Field of Specialization Computer Vision, Computational Modeling of Human Vision, Machine Learning Education B.S. in Electrical Engineering, Indian Institute of Technology, Kanpur, 1980. Ph.D. in Computer Science, Stanford University, December 1985. Experience Research Scientist Director & Site Lead, Facebook Artificial Intelligence Research, Menlo Park, 2018 & 2019 Chair, Department of EECS, UC Berkeley, 2016-2017 Visiting Research Scientist, Google, 2015-2016 Member, Committee on Budget and Interdepartmental Relations, 2013-2014 Associate Dean for New Academic Initiatives, College of Engineering, 2010-2012 Professor, Bioengineering, UC Berkeley, since January 2009. Chair, Department of EECS, UC Berkeley, 2004{2006. Chair, Computer Science Division, EECS, UC Berkeley, 2002{2004. Scientific Director, Yahoo! Research Berkeley, Jan-June 2007 (visiting) Professor, EECS, UC Berkeley, since July 1996. Associate Professor, EECS, UC Berkeley, July 1991-June 1996. Assistant Professor, EECS, UC Berkeley, Jan 1986-June 1991. Member, Groups in Vision Science and Cognitive Science, UC Berkeley. Honors and Awards Computer Pioneer Award, IEEE Computer Society, 2019 IJCAI Award for Research Excellence, 2018 Best paper prize, IEEE CVPR 2018 Helmholtz Prize for a Contribution That Has Stood the Test of Time, ICCV, 2003 ACM-AAAI Allen Newell Award, 2016. National Academy of Sciences, 2015. Helmholtz Prize (1) for a Contribution That Has Stood the Test of Time, ICCV, 2001 Helmholtz Prize (2) for a Contribution That Has Stood the Test of Time, ICCV, 2001 Best student paper award, IEEE CVPR 2016 (adviser) King-Sun Fu Prize of the International Association of Pattern Recognition, 2014.