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Events for the 4th week of July

  • CS Colloquium: Josiane Zerubia (INRIA, France) - Marked Point Processes for Object Detection and Tracking in High Resolution Images: Applications to Remote Sensing and Biology

    Tue, Jul 18, 2017 @ 10:30 AM - 11:30 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Josiane Zerubia, INRIA, France

    Talk Title: Marked Point Processes for Object Detection and Tracking in High Resolution Images: Applications to Remote Sensing and Biology

    Series: CS Colloquium

    Abstract: In this talk, we combine the methods from probability theory and stochastic geometry to put forward new solutions to the multiple object detection and tracking problem in high resolution remotely sensed image sequences. First, we present a spatial marked point process model to detect a pre-defined class of objects based on their visual and geometric characteristics. Then, we extend this model to the temporal domain and create a framework based on spatio-temporal marked point process models to jointly detect and track multiple objects in image sequences. We propose the use of simple parametric shapes to describe the appearance of these objects. We build new, dedicated energy based models consisting of several terms that take into account both the image evidence and physical constraints such as object dynamics, track persistence and mutual exclusion. We construct a suitable optimization scheme that allows us to find strong local minima of the proposed highly non-convex energy.

    As the simulation of such models comes with a high computational cost, we turn our attention to the recent filter implementations for multiple objects tracking, which are known to be less computationally expensive. We propose a hybrid sampler by combining the Kalman filter with the standard Reversible Jump MCMC. High performance computing techniques are also used to increase the computational efficiency of our method. We provide an analysis of the proposed framework. This analysis yields a very good detection and tracking performance at the price of an increased complexity of the models. Tests have been conducted both on high resolution satellite and microscopy image sequences.

    Keywords:
    Multiple object tracking, object detection, marked point process, Kalman filter, satellite image sequences, microscopy data sequences, high resolution.

    Biography: Josiane Zerubia has been a permanent research scientist at INRIA since 1989 and director of research since July 1995. She was head of the PASTIS remote sensing laboratory (INRIA Sophia-Antipolis) from mid-1995 to 1997 and of the Ariana research group (INRIA/CNRS/University of Nice), which worked on inverse problems in remote sensing and biological imaging, from 1998 to 2011. From 2012 to 2016, she was head of Ayin research group (INRIA-SAM) dedicated to models of spatio-temporal structure for high resolution image processing with a focus on remote sensing and skincare imaging.

    She has been professor at SUPAERO (ISAE) in Toulouse since 1999. Before that, she was with the Signal and Image Processing Institute of the University of Southern California (USC) in Los-Angeles as a postdoc. She also worked as a researcher for the LASSY (University of Nice/CNRS) from 1984 to 1988 and in the Research Laboratory of Hewlett Packard in France and in Palo-Alto (CA) from 1982 to 1984. She received the MSc degree from the Department of Electrical Engineering at ENSIEG, Grenoble, France in 1981, the Doctor of Engineering degree, her PhD and her 'Habilitation', in 1986, 1988, and 1994 respectively, all from the University of Nice Sophia-Antipolis, France.

    She is a Fellow of the IEEE (2003- ) and IEEE SP Society Distinguished Lecturer (2016-2017). She was a member of the IEEE IMDSP TC (SP Society) from 1997 till 2003, of the IEEE BISP TC (SP Society) from 2004 till 2012 and of the IVMSP TC (SP Society) from 2008 till 2013. She was associate editor of IEEE Trans. on IP from 1998 to 2002, area editor of IEEE Trans. on IP from 2003 to 2006, guest co-editor of a special issue of IEEE Trans. on PAMI in 2003, member of the editorial board of IJCV from 2004 till March 2013 and member-at-large of the Board of Governors of the IEEE SP Society from 2002 to 2004. She has also been a member of the editorial board of the French Society for Photogrammetry and Remote Sensing (SFPT) since 1998, of the Foundation and Trends in Signal Processing since 2007 and member-at-large of the Board of Governors of the SFPT since September 2014. She has been associate editor of the on-line resource Earthzine (IEEE CEO and GEOSS) since 2006.

    She was co-chair of two workshops on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'01, Sophia Antipolis, France, and EMMCVPR'03, Lisbon, Portugal), co-chair of a workshop on Image Processing and Related Mathematical Fields (IPRM'02, Moscow, Russia), technical program chair of a workshop on Photogrammetry and Remote Sensing for Urban Areas (Marne La Vallee, France, 2003), co-chair of the special sessions at IEEE ICASSP 2006 (Toulouse, France) and IEEE ISBI 2008 (Paris, France), publicity chair of IEEE ICIP 2011 (Brussels, Belgium), tutorial co-chair of IEEE ICIP 2014 (Paris, France), general co-chair of the workshop EarthVision at IEEE CVPR 2015 (Boston, USA) and a member of the organizing committee and plenary talk co-chair of IEEE-EURASIP EUSIPCO 2015 (Nice, France). She also organized and chaired an international workshop on Stochastic Geometry and Big Data at Sophia Antipolis, France, in November 2015. She is part of the organizing committees of the workshop EarthVision at IEEE CVPR 2017 (Honolulu, USA), GRETSI 2017 symposium (Juan les Pins, France) and ISPRS 2020 congress (Nice, France).

    Her main research interest is in image processing using probabilistic models. She also works on parameter estimation, statistical learning and optimization techniques.

    Host: Ram Nevatia, Antonio Ortega

    Webcast: https://bluejeans.com/137883736

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://bluejeans.com/137883736

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • PhD Defense - Benjamin Ford

    Thu, Jul 20, 2017 @ 12:00 PM - 02:00 PM

    Computer Science

    University Calendar


    Date/Time: July 20, 2017. 12 PM to 2 PM
    Location: RTH 526
    PhD Candidate: Benjamin Ford
    Committee Members: Milind Tambe, Richard John, Eric Rice, Ning Wang
    Title: Real-World Evaluation and Deployment of Wildlife Crime Prediction Models

    Abstract:
    Conservation agencies worldwide must make the most efficient use of their limited resources to protect natural resources from over-harvesting and animals from poaching. Predictive modeling, a tool to increase efficiency, is seeing increased usage in conservation domains such as to protect wildlife from poaching. Many works in this wildlife protection domain, however, fail to train their models on real-world data or test their models in the real world. My thesis proposes novel poacher behavior models that are trained on real-world data and are tested via first-of-their-kind tests in the real world.

    First, I proposed a paradigm shift in traditional adversary behavior modeling techniques from Quantal-Response based models to decision tree based models. Based on this shift, I proposed an ensemble of spatially-aware decision trees, INTERCEPT, that outperformed the prior state-of-the-art and then also presented results from a one-month pilot field test of the ensemble's predictions in Uganda's Queen Elizabeth Protected Area (QEPA). This field test represented the first time that a machine learning-based poacher behavior modeling application was tested in the field.

    Second, I proposed a hybrid spatio-temporal model that led to further performance improvements. To validate this model, I designed and conducted a large-scale, eight-month field test of this model's predictions in QEPA. This field test, where rangers patrolled over 450 km in the largest and longest field test of a machine learning-based poacher behavior model to date in this domain, successfully demonstrated the selectiveness of the model's predictions; the model successfully predicted, with statistical significance, where rangers would find more snaring activity and also where rangers would not find as much snaring activity. I also conducted detailed analysis of the behavior of my predictive model.

    Third, beyond wildlife poaching, I also provided novel models for human adversary behavior modeling -- graph aware behavior models -- in wildlife or other contraband smuggling networks and tested them against human subjects. Lastly, I examined human considerations of deployment in new domains and the importance of easily-interpretable models and results. While such interpretability has been a recurring theme in all my thesis work, I also created a game-theoretic inspection strategy application that generated randomized factory inspection schedules and also contained visualization and explanation components for users.

    Location: Ronald Tutor Hall of Engineering (RTH) - 526

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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  • CAIS Seminar: Dr. Amy Greenwald (Brown University) - The Interplay of Agent and Market Design

    Fri, Jul 21, 2017 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Dr. Amy Greenwald, Brown University

    Talk Title: The Interplay of Agent and Market Design

    Series: Center for AI in Society (CAIS) Seminar Series

    Abstract: We humans make hundreds of routine decisions daily. More often than not, the impact of our decisions depends on the decisions of others. As AI progresses, we are offloading more and more of these decisions to artificial agents. Dr. Greenwald's research is aimed at building AI agents that make effective decisions in multi-agent--part human, part artificial--environments. The bulk of her efforts in this space have been relevant to economic domains, mostly in service of perfecting market designs. In this talk, she will discuss AI agent design in applications ranging from renewable energy markets to online ad exchanges to wireless spectrum auctions

    Biography: Dr. Amy Greenwald is an Associate Professor of Computer Science at Brown University in Providence, Rhode Island. She studies game-theoretic and economic interactions among computational agents, applied to areas like autonomous bidding in wireless spectrum auctions and ad exchanges. In 2011, she was named a Fulbright Scholar to the Netherlands (though she declined the award). She was awarded a Sloan Fellowship in 2006; she was nominated for the 2002 Presidential Early Career Award for Scientists and Engineers (PECASE); and she was named one of the Computing Research Association's Digital Government Fellows in 2001. Before joining the faculty at Brown, Dr. Greenwald was employed by IBM's T.J. Watson Research Center. Her paper entitled "Shopbots and Pricebots" (joint work with Jeff Kephart) was named Best Paper at IBM Research in 2000.

    Host: Milind Tambe

    Location: Ronald Tutor Hall of Engineering (RTH) - 217

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • NL Seminar- Neural Sequence Models: Interpretation and Augmentation

    Fri, Jul 21, 2017 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Xing Shi, USC/ISI

    Talk Title: Neural Sequence Models: Interpretation and Augmentation

    Series: Natural Language Seminar

    Abstract: Recurrent neural networks RNN have been successfully applied to various Natural Language Processing tasks, including language modeling, machine translation, text generation, etc. However, several obstacles still stand in the way: First, due to the RNN's distributional nature, few interpretations of its internal mechanism are obtained, and it remains a black box. Second, because of the large vocabulary sets involved, the text generation is very time consuming. Third, there is no flexible way to constrain the generation of the sequence model with external knowledge. Last, huge training data must be collected to guarantee the performance of these neural models, whereas annotated data such as parallel data used in machine translation are expensive to obtain. This work aims to address the four challenges mentioned above.

    To further understand the internal mechanism of the RNN, I choose neural machine translation NMT systems as a testbed. I first investigate how NMT outputs target strings of appropriate lengths, locating a collection of hidden units that learns to explicitly implement this functionality. Then I investigate whether NMT systems learn source language syntax as a by product of training on string pairs. I find that both local and global syntactic information about source sentences is captured by the encoder. Different types of syntax are stored in different layers, with different concentration degrees.

    To speed up text generation, I proposed two novel GPU-based algorithms. 1 Utilize the source/target words alignment information to shrink the target side run-time vocabulary. 2 Apply locality sensitive hashing to find nearest word embeddings. Both methods lead to a 2-3x speedup on four translation tasks without hurting machine translation accuracy as measured by BLEU. Furthermore, I integrate a finite state acceptor into the neural sequence model during generation, providing a flexible way to constrain the output, and I successfully apply this to poem generation, in order to control the pentameter and rhyme.

    Based on above success, I propose to work on the following. 1 Go one further step towards interpretation: find unit feature mappings, learn the unit temporal behavior, and understand different hyper-parameter settings. 2 Improve NMT performance on low-resource language pairs by fusing an external language model, feeding explicit target-side syntax and utilizing better word embeddings.




    Biography: Xing Shi is a PhD student at ISI working with Prof. Kevin Knight.

    Host: Marjan Ghazvininejad and Kevin Knight

    More Info: http://nlg.isi.edu/nl-seminar/

    Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey

    Audiences: Everyone Is Invited

    Posted By: Peter Zamar

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