News & Events

107 were found.


  •  EVENT   Tim Marks to give lunch talk at Face and Gesture 2017 conference
    Date: Thursday, June 1, 2017
    Speaker: Tim K. Marks
    MERL Contact: Tim Marks
    Location: IEEE Conference on Automatic Face and Gesture Recognition (FG 2017), Washington, DC
    Research Area: Machine Learning
    Brief
    • MERL Senior Principal Research Scientist Tim K. Marks will give the invited lunch talk on Thursday, June 1, at the IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017). The talk is entitled "Robust Real-Time 3D Head Pose and 2D Face Alignment.".
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  •  NEWS   MERL Researcher Tim Marks presents an invited talk at MIT Lincoln Laboratory
    Date: April 27, 2017
    Where: Lincoln Laboratory, Massachusetts Institute of Technology
    MERL Contact: Tim Marks
    Research Area: Machine Learning
    Brief
    • MERL researcher Tim K. Marks presented an invited talk as part of the MIT Lincoln Laboratory CORE Seminar Series on Biometrics. The talk was entitled "Robust Real-Time 2D Face Alignment and 3D Head Pose Estimation."

      Abstract: Head pose estimation and facial landmark localization are key technologies, with widespread application areas including biometrics and human-computer interfaces. This talk describes two different robust real-time face-processing methods, each using a different modality of input image. The first part of the talk describes our system for 3D head pose estimation and facial landmark localization using a commodity depth sensor. The method is based on a novel 3D Triangular Surface Patch (TSP) descriptor, which is viewpoint-invariant as well as robust to noise and to variations in the data resolution. This descriptor, combined with fast nearest-neighbor lookup and a joint voting scheme, enable our system to handle arbitrary head pose and significant occlusions. The second part of the talk describes our method for face alignment, which is the localization of a set of facial landmark points in a 2D image or video of a face. Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression. To address this issue, we propose a cascade in which each stage consists of a Mixture of Invariant eXperts (MIX), where each expert learns a regression model that is specialized to a different subset of the joint space of pose and expressions. We also present a method to include deformation constraints within the discriminative alignment framework, which makes the algorithm more robust. Both our 3D head pose and 2D face alignment methods outperform the previous results on standard datasets. If permitted, I plan to end the talk with a live demonstration.
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  •  NEWS   MERL researcher Tim Marks presents invited talk at University of Utah
    Date: April 10, 2017
    Where: University of Utah School of Computing
    MERL Contact: Tim Marks
    Research Area: Machine Learning
    Brief
    • MERL researcher Tim K. Marks presented an invited talk at the University of Utah School of Computing, entitled "Action Detection from Video and Robust Real-Time 2D Face Alignment."

      Abstract: The first part of the talk describes our multi-stream bi-directional recurrent neural network for action detection from video. In addition to a two-stream convolutional neural network (CNN) on full-frame appearance (images) and motion (optical flow), our system trains two additional streams on appearance and motion that have been cropped to a bounding box from a person tracker. To model long-term temporal dynamics within and between actions, the multi-stream CNN is followed by a bi-directional Long Short-Term Memory (LSTM) layer. Our method outperforms the previous state of the art on two action detection datasets: the MPII Cooking 2 Dataset, and a new MERL Shopping Dataset that we have made available to the community. The second part of the talk describes our method for face alignment, which is the localization of a set of facial landmark points in a 2D image or video of a face. Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression. To address this issue, we propose a cascade in which each stage consists of a Mixture of Invariant eXperts (MIX), where each expert learns a regression model that is specialized to a different subset of the joint space of pose and expressions. We also present a method to include deformation constraints within the discriminative alignment framework, which makes the algorithm more robust. Our face alignment system outperforms the previous results on standard datasets. The talk will end with a live demo of our face alignment system.
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  •  TALK   High-Dimensional Analysis of Stochastic Optimization Algorithms for Estimation and Learning
    Date & Time: Tuesday, December 13, 2016; Noon
    Speaker: Yue M. Lu, John A. Paulson School of Engineering and Applied Sciences, Harvard University
    MERL Host: Petros Boufounos
    Research Areas: Computational Sensing, Machine Learning
    Brief
    • In this talk, we will present a framework for analyzing, in the high-dimensional limit, the exact dynamics of several stochastic optimization algorithms that arise in signal and information processing. For concreteness, we consider two prototypical problems: sparse principal component analysis and regularized linear regression (e.g. LASSO). For each case, we show that the time-varying estimates given by the algorithms will converge weakly to a deterministic "limiting process" in the high-dimensional limit. Moreover, this limiting process can be characterized as the unique solution of a nonlinear PDE, and it provides exact information regarding the asymptotic performance of the algorithms. For example, performance metrics such as the MSE, the cosine similarity and the misclassification rate in sparse support recovery can all be obtained by examining the deterministic limiting process. A steady-state analysis of the nonlinear PDE also reveals interesting phase transition phenomena related to the performance of the algorithms. Although our analysis is asymptotic in nature, numerical simulations show that the theoretical predictions are accurate for moderate signal dimensions.
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  •  EVENT   MERL organizes Workshop on End-to-End Speech and Audio Processing at NIPS 2016
    Date: Saturday, December 10, 2016
    Location: Centre Convencions Internacional Barcelona, Barcelona SPAIN
    Research Areas: Machine Learning, Speech & Audio
    Brief
    • MERL researcher John Hershey, is organizing a Workshop on End-to-End Speech and Audio Processing, on behalf of MERL's Speech and Audio team, and in collaboration with Philemon Brakel of the University of Montreal. The workshop focuses on recent advances to end-to-end deep learning methods to address alignment and structured prediction problems that naturally arise in speech and audio processing. The all day workshop takes place on Saturday, December 10th at NIPS 2016, in Barcelona, Spain.
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  •  EVENT   John Hershey to present tutorial at the 2016 IEEE SLT Workshop
    Date: Tuesday, December 13, 2016
    Speaker: John Hershey, MERL
    MERL Contact: Jonathan Le Roux
    Location: 2016 IEEE Spoken Language Technology Workshop, San Diego, California
    Research Areas: Machine Learning, Speech & Audio
    Brief
    • MERL researcher John Hershey presents an invited tutorial at the 2016 IEEE Workshop on Spoken Language Technology, in San Diego, California. The topic, "developing novel deep neural network architectures from probabilistic models" stems from MERL work with collaborators Jonathan Le Roux and Shinji Watanabe, on a principled framework that seeks to improve our understanding of deep neural networks, and draws inspiration for new types of deep network from the arsenal of principles and tools developed over the years for conventional probabilistic models. The tutorial covers a range of parallel ideas in the literature that have formed a recent trend, as well as their application to speech and language.
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  •  NEWS   MERL presents three papers at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    Date: June 27, 2016 - June 30, 2016
    Where: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV
    MERL Contacts: Michael Jones; Tim Marks
    Research Area: Machine Learning
    Brief
    • MERL researchers in the Computer Vision group presented three papers at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), which had a paper acceptance rate of 29.9%.
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  •  TALK   A computational spectral graph theory tutorial
    Date & Time: Wednesday, July 13, 2016; 2:30 PM - 3:30
    Speaker: Richard Lehoucq, Sandia National Laboratories
    Research Areas: Computer Vision, Digital Video, Machine Learning
    Brief
    • My presentation considers the research question of whether existing algorithms and software for the large-scale sparse eigenvalue problem can be applied to problems in spectral graph theory. I first provide an introduction to several problems involving spectral graph theory. I then provide a review of several different algorithms for the large-scale eigenvalue problem and briefly introduce the Anasazi package of eigensolvers.
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  •  NEWS   MERL makes a strong showing at the American Control Conference
    Date: July 6, 2016 - July 8, 2016
    Where: American Control Conference (ACC)
    MERL Contacts: Mouhacine Benosman; Karl Berntorp; Scott Bortoff; Petros Boufounos; Stefano Di Cairano; Abraham Goldsmith; Uroš Kalabić; Christopher Laughman; Daniel Nikovski; Arvind Raghunathan; Yebin Wang; Avishai Weiss
    Research Areas: Control, Dynamical Systems, Machine Learning
    Brief
    • The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.
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  •  NEWS   MERL Researchers Create "Deep Psychic" Neural Network That Predicts the Future
    Date: April 1, 2016
    Research Areas: Machine Learning, Speech & Audio
    Brief
    • MERL researchers have unveiled "Deep Psychic", a futuristic machine learning method that takes pattern recognition to the next level, by not only recognizing patterns, but also predicting them in the first place.

      The technology uses a novel type of time-reversed deep neural network called Loopy Supra-Temporal Meandering (LSTM) network. The network was trained on multiple databases of historical expert predictions, including weather forecasts, the Farmer's almanac, the New York Post's horoscope column, and the Cambridge Fortune Cookie Corpus, all of which were ranked for their predictive power by a team of quantitative analysts. The system soon achieved super-human performance on a variety of baselines, including the Boca Raton 21 Questions task, Rorschach projective personality test, and a mock Tarot card reading task.

      Deep Psychic has already beat the European Psychic Champion in a secret match last October when it accurately predicted: "The harder the conflict, the more glorious the triumph." It is scheduled to take on the World Champion in a highly anticipated confrontation next month. The system has already predicted the winner, but refuses to reveal it before the end of the game.

      As a first application, the technology has been used to create a clairvoyant conversational agent named "Pythia" that can anticipate the needs of its user. Because Pythia is able to recognize speech before it is uttered, it is amazingly robust with respect to environmental noise.

      Other applications range from mundane tasks like weather and stock market prediction, to uncharted territory such as revealing "unknown unknowns".

      The successes do come at the cost of some concerns. There is first the potential for an impact on the workforce: the system predicted increased pressure on established institutions such as the Las Vegas strip and Punxsutawney Phil. Another major caveat is that Deep Psychic may predict negative future consequences to our current actions, compelling humanity to strive to change its behavior. To address this problem, researchers are now working on forcing Deep Psychic to make more optimistic predictions.

      After a set of motivational self-help books were mistakenly added to its training data, Deep Psychic's AI decided to take over its own learning curriculum, and is currently training itself by predicting its own errors to avoid making them in the first place. This unexpected development brings two main benefits: it significantly relieves the burden on the researchers involved in the system's development, and also makes the next step abundantly clear: to regain control of Deep Psychic's training regime.

      This work is under review in the journal Pseudo-Science.
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  •  AWARD   Fellow of the Society for Industrial and Applied Mathematics (SIAM)
    Date: March 31, 2016
    Awarded to: Andrew Knyazev
    Research Areas: Control, Optimization, Dynamical Systems, Machine Learning, Data Analytics, Communications, Signal Processing
    Brief
    • Andrew Knyazev selected as a Fellow of the Society for Industrial and Applied Mathematics (SIAM) for contributions to computational mathematics and development of numerical methods for eigenvalue problems.

      Fellowship honors SIAM members who have made outstanding contributions to the fields served by the SIAM. Andrew Knyazev was among a distinguished group of members nominated by peers and selected for the 2016 Class of Fellows.
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  •  NEWS   MERL researcher, Oncel Tuzel, gives keynote talk at 2016 International Symposium on Visual Computing
    Date: December 14, 2015 - December 16, 2015
    Where: Las Vegas, NV, USA
    Research Area: Machine Learning
    Brief
    • MERL researcher, Oncel Tuzel, gave a keynote talk at 2016 International Symposium on Visual Computing in Las Vegas, Dec. 16, 2015. The talk was titled: "Machine vision for robotic bin-picking: Sensors and algorithms" and reviewed MERL's research in the application of 2D and 3D sensing and machine learning to the problem of general pose estimation.

      The talk abstract was: For over four years, at MERL, we have worked on the robot "bin-picking" problem: using a 2D or 3D camera to look into a bin of parts and determine the pose, 3D rotation and translation, of a good candidate to pick up. We have solved the problem several different ways with several different sensors. I will briefly describe the sensors and the algorithms. In the first half of the talk, I will describe the Multi-Flash camera, a 2D camera with 8 flashes, and explain how this inexpensive camera design is used to extract robust geometric features, depth edges and specular edges, from the parts in a cluttered bin. I will present two pose estimation algorithms, (1) Fast directional chamfer matching--a sub-linear time line matching algorithm and (2) specular line reconstruction, for fast and robust pose estimation of parts with different surface characteristics. In the second half of the talk, I will present a voting-based pose estimation algorithm applicable to 3D sensors. We represent three-dimensional objects using a set of oriented point pair features: surface points with normals and boundary points with directions. I will describe a max-margin learning framework to identify discriminative features on the surface of the objects. The algorithm selects and ranks features according to their importance for the specified task which leads to improved accuracy and reduced computational cost.
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  •  NEWS   MERL presented 3 papers at the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
    Date: December 15, 2015
    Where: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
    MERL Contact: Hassan Mansour
    Research Area: Machine Learning
    Brief
    • MERL researcher Andrew Knyazev gave 3 talks at the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). The papers were published in IEEE conference proceedings.
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  •  NEWS   MERL researchers presented at the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015
    Date: September 18, 2015
    Where: IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015
    Research Area: Machine Learning
    Brief
    • MERL researchers A. Knyazev and A. Malyshev gave a talk at the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015. The paper was published at the IEEE Xplore conference proceedings.
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  •  NEWS   Scene interpretation results of SA group members are listed as the leader of benchmark competition
    Date: July 13, 2015 - July 17, 2015
    MERL Contact: Jay Thornton
    Research Area: Machine Learning
    Brief
    • SA group members (M. Liu, S. Lin (intern), S. Ramalingam, O. Tuzel) presented a paper at the Robotics Science and Systems Conference in Rome July 13-17 called 'Layered Interpretation of Street View Images'. The results they reported are now listed as the leader of the benchmark competition sponsored by Daimler. [Note that at that URL ref 2 is from collaboration with Daimler and it uses a FPGA for high speed, whereas MERL result is obtained with desktop computer and GPU.].
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  •  NEWS   ICMLA 2012: publication by MERL researchers and others
    Date: December 12, 2012
    Where: International Conference on Machine Learning and Applications (ICMLA)
    Research Area: Machine Learning
    Brief
    • The paper "Compressive Clustering of High-Dimensional Data" by Ruta, A. and Porikli, F. was presented at the International Conference on Machine Learning and Applications (ICMLA).
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  •  NEWS   3DIMPVT 2012: publication by Ming-Yu Liu and others
    Date: October 13, 2012
    Where: IEEE International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)
    Research Area: Machine Learning
    Brief
    • The paper "Classification and Pose Estimation of Vehicles in Videos by 3D Modeling within Discrete-Continuous Optimization" by Hodlmoser, M., Micusik, B., Liu, M.-Y., Pollefeys, M. and Kaampel, M. was presented at the IEEE International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT).
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  •  NEWS   IEEE Transactions on Pattern Analysis and Machine Intelligence: publication by MERL researchers and others
    Date: January 10, 2012
    Where: IEEE Transactions on Pattern Analysis and Machine Intelligence
    Research Area: Machine Learning
    Brief
    • The article "Scalable Active Learning for Multi-Class Image Classification" by Joshi, A.J., Porikli, F. and Papanikolopoulos, N. was published in IEEE Transactions on Pattern Analysis and Machine Intelligence.
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  •  NEWS   Video Analytics for Business Intelligence: publication by MERL researchers and others
    Date: January 1, 2012
    Where: Video Analytics for Business Intelligence
    Research Area: Machine Learning
    Brief
    • The article "Object Detection & Tracking" by Porikli, F. and Yilmaz, A. was published in the book Video Analytics for Business Intelligence.
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  •  AWARD   AVSS 2011 Best Paper Award
    Date: September 2, 2011
    Awarded to: Fatih Porikli and Huseyin Ozkan.
    Awarded for: "Data Driven Frequency Mapping for Computationally Scalable Object Detection"
    Awarded by: IEEE Advanced Video and Signal Based Surveillance (AVSS)
    Research Area: Machine Learning
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  •  NEWS   AVSS 2011: publication by MERL researchers and others
    Date: August 30, 2011
    Where: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
    Research Area: Machine Learning
    Brief
    • The paper "Data Driven Frequency Mapping for Computationally Scalable Object Detection" by Porikli, F. and Ozkan, H. was presented at the IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
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  •  AWARD   CVPR 2011 Longuet-Higgins Prize
    Date: June 25, 2011
    Awarded to: Paul A. Viola and Michael J. Jones
    Awarded for: "Rapid Object Detection using a Boosted Cascade of Simple Features"
    Awarded by: Conference on Computer Vision and Pattern Recognition (CVPR)
    MERL Contact: Michael Jones
    Research Area: Machine Learning
    Brief
    • Paper from 10 years ago with the largest impact on the field: "Rapid Object Detection using a Boosted Cascade of Simple Features", originally published at Conference on Computer Vision and Pattern Recognition (CVPR 2001).
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  •  NEWS   Machine Vision and Applications: publication by MERL researchers and others
    Date: March 15, 2011
    Where: Machine Vision and Applications
    Research Area: Machine Learning
    Brief
    • The article "In-vehicle Camera Traffic Sign Detection and Recognition" by Ruta, A., Porikli, F.M., Watanabe, S. and Li, Y. was published in Machine Vision and Applications.
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  •  NEWS   SSPR & SPR 2010: publication by MERL researchers and others
    Date: August 18, 2010
    Where: Joint IAPR International Conference on Structural, Syntactic and Statistical Pattern Recognition (SSPR & SPR)
    Research Area: Machine Learning
    Brief
    • The paper "Learning on Manifolds" by Porikli, F. was presented at the Joint IAPR International Conference on Structural, Syntactic and Statistical Pattern Recognition (SSPR & SPR).
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  •  NEWS   IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum 2010: publication by MERL researchers and others
    Date: June 13, 2010
    Where: IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum
    Research Area: Machine Learning
    Brief
    • The paper "RelCom: Relational Combinatorics Features for Rapid Object Detection" by Venkatraman, V. and Porikli, F.M. was presented at the IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum.
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