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Institute Talks

Physical Reasoning and Robot Manipulation

Talk
  • 11 December 2018 • 15:00 16:00
  • Marc Toussaint
  • 2R4 Werner Köster lecture hall

Animals and humans are excellent in conceiving of solutions to physical and geometric problems, for instance in using tools, coming up with creative constructions, or eventually inventing novel mechanisms and machines. Cognitive scientists coined the term intuitive physics in this context. It is a shame we do not yet have good computational models of such capabilities. A main stream of current robotics research focusses on training robots for narrow manipulation skills - often using massive data from physical simulators. Complementary to that we should also try to understand how basic principles underlying physics can directly be used to enable general purpose physical reasoning in robots, rather than sampling data from physical simulations. In this talk I will discuss an approach called Logic-Geometric Programming, which builds a bridge between control theory, AI planning and robot manipulation. It demonstrates strong performance on sequential manipulation problems, but also raises a number of highly interesting fundamental problems, including its probabilistic formulation, reactive execution and learning.

Organizers: Katherine Kuchenbecker Ildikó Papp-Wiedmann Barbara Kettemann Matthias Tröndle

Magnetically Guided Multiscale Robots and Soft-robotic Grippers

Talk
  • 11 December 2018 • 11:00 12:00
  • Dr. František Mach
  • Stuttgart 2P4

The state-of-the-art robotic systems adopting magnetically actuated ferromagnetic bodies or even whole miniature robots have recently become a fast advancing technological field, especially at the nano and microscale. The mesoscale and above all multiscale magnetically guided robotic systems appear to be the advanced field of study, where it is difficult to reflect different forces, precision and also energy demands. The major goal of our talk is to discuss the challenges in the field of magnetically guided mesoscale and multiscale actuation, followed by the results of our research in the field of magnetic positioning systems and the magnetic soft-robotic grippers.

Organizers: Metin Sitti

Learning Dynamics from Kinematics: Estimating Foot Pressure from Video

Talk
  • 12 December 2018 • 10:00 11:00
  • Yanxi Liu
  • Aquarium (N3.022)

Human pose stability analysis is the key to understanding locomotion and control of body equilibrium, with numerous applications in the fields of Kinesiology, Medicine and Robotics. We propose and validate a novel approach to learn dynamics from kinematics of a human body to aid stability analysis. More specifically, we propose an end-to-end deep learning architecture to regress foot pressure from a human pose derived from video. We have collected and utilized a set of long (5min +) choreographed Taiji (Tai Chi) sequences of multiple subjects with synchronized motion capture, foot pressure and video data. The derived human pose data and corresponding foot pressure maps are used jointly in training a convolutional neural network with residual architecture, named “PressNET”. Cross validation results show promising performance of PressNet, significantly outperforming the baseline method under reasonable sensor noise ranges.

Organizers: Nadine Rueegg

Self-Supervised Representation Learning for Visual Behavior Analysis and Synthesis

Talk
  • 14 December 2018 • 12:00 13:00
  • Prof. Dr. Björn Ommer
  • PS Aquarium

Understanding objects and their behavior from images and videos is a difficult inverse problem. It requires learning a metric in image space that reflects object relations in real world. This metric learning problem calls for large volumes of training data. While images and videos are easily available, labels are not, thus motivating self-supervised metric and representation learning. Furthermore, I will present a widely applicable strategy based on deep reinforcement learning to improve the surrogate tasks underlying self-supervision. Thereafter, the talk will cover the learning of disentangled representations that explicitly separate different object characteristics. Our approach is based on an analysis-by-synthesis paradigm and can generate novel object instances with flexible changes to individual characteristics such as their appearance and pose. It nicely addresses diverse applications in human and animal behavior analysis, a topic we have intensive collaboration on with neuroscientists. Time permitting, I will discuss the disentangling of representations from a wider perspective including novel strategies to image stylization and new strategies for regularization of the latent space of generator networks.

Organizers: Joel Janai

Generating Faces & Heads: Texture, Shape and Beyond.

Talk
  • 17 December 2018 • 11:00 12:00
  • Stefanos Zafeiriou
  • PS Aquarium

The past few years with the advent of Deep Convolutional Neural Networks (DCNNs), as well as the availability of visual data it was shown that it is possible to produce excellent results in very challenging tasks, such as visual object recognition, detection, tracking etc. Nevertheless, in certain tasks such as fine-grain object recognition (e.g., face recognition) it is very difficult to collect the amount of data that are needed. In this talk, I will show how, using DCNNs, we can generate highly realistic faces and heads and use them for training algorithms such as face and facial expression recognition. Next, I will reverse the problem and demonstrate how by having trained a very powerful face recognition network it can be used to perform very accurate 3D shape and texture reconstruction of faces from a single image. Finally, I will demonstrate how to create very lightweight networks for representing 3D face texture and shape structure by capitalising upon intrinsic mesh convolutions.

Organizers: Dimitris Tzionas

Deep learning on 3D face reconstruction, modelling and applications

Talk
  • 19 December 2018 • 11:00 12:00
  • Yao Feng
  • PS Aquarium

In this talk, I will present my understanding on 3D face reconstruction, modelling and applications from a deep learning perspective. In the first part of my talk, I will discuss the relationship between representations (point clouds, meshes, etc) and network layers (CNN, GCN, etc) on face reconstruction task, then present my ECCV work PRN which proposed a new representation to help achieve state-of-the-art performance on face reconstruction and dense alignment tasks. I will also introduce my open source project face3d that provides examples for generating different 3D face representations. In the second part of the talk, I will talk some publications in integrating 3D techniques into deep networks, then introduce my upcoming work which implements this. In the third part, I will present how related tasks could promote each other in deep learning, including face recognition for face reconstruction task and face reconstruction for face anti-spoofing task. Finally, with such understanding of these three parts, I will present my plans on 3D face modelling and applications.

Organizers: Timo Bolkart

Mind Games

IS Colloquium
  • 21 December 2018 • 11:00 12:00
  • Peter Dayan
  • IS Lecture Hall

Much existing work in reinforcement learning involves environments that are either intentionally neutral, lacking a role for cooperation and competition, or intentionally simple, when agents need imagine nothing more than that they are playing versions of themselves. Richer game theoretic notions become important as these constraints are relaxed. For humans, this encompasses issues that concern utility, such as envy and guilt, and that concern inference, such as recursive modeling of other players, I will discuss studies treating a paradigmatic game of trust as an interactive partially-observable Markov decision process, and will illustrate the solution concepts with evidence from interactions between various groups of subjects, including those diagnosed with borderline and anti-social personality disorders.

TBA

IS Colloquium
  • 28 January 2019 • 11:15 12:15
  • Florian Marquardt

Organizers: Matthias Bauer

  • Cordelia Schmid
  • MRC seminar room (0.A.03)

In the first part of our talk, we present an approach for large displacement optical flow. Optical flow computation is a key component in many computer vision systems designed for tasks such as action
detection or activity  recognition. Inspired by the large displacement optical flow of Brox and  Malik, our approach  DeepFlow  combines a novel matching algorithm with a variational approach . Our matching algorithm builds upon a multi-stage architecture interleaving convolutions and max-pooling.  DeepFlow efficiently handles large displacements  occurring in realistic videos, and shows competitive performance on optical flow benchmarks.

In the second part of our talk, we present a state-of-the-art approach  for action recognition based  on motion stabilized trajectory  descriptors and a Fisher vector representation.  We briefly review the recent trajectory-based video features and, then, introduce their motion stabilized version, combining human detection and dominant motion estimation. Fisher vectors summarize the information of a video efficiently. Results on several of the recent action datasets as well as the TrecVid MED dataset show that our approach outperforms the state-of-the-art


  • Jiri Matas
  • Max Planck House Lecture Hall

Computer vision problems often involve optimization of two quantities, one of which is time. Such problems can be formulated as time-constrained optimization or performance-constrained search for the fastest algorithm. We show that it is possible to obtain quasi-optimal time-constrained solutions to some vision problems by applying Wald's theory of sequential decision-making. Wald assumes independence of observation, which is rarely true in computer vision. We address the problem by combining Wald's sequential probability ratio test and AdaBoost. The solution, called the WaldBoost, can be viewed as a principled way to build a close-to-optimal “cascade of classifiers” of the Viola-Jones type. The approach will be demonstrated on four tasks: (i) face detection, (ii) establishing reliable correspondences between image, (iii) real-time detection of interest points and (iv) model search and outlier detection using RANSAC. In the face detection problem, the objective is learning the fastest detector satisfying constraints on false positive and false negative rates. The correspondence pruning addresses the problem of fast selection with a predefined false negative rated. In interest point problem we show how a fast implementation of known detectors can obtained by Waldboost. The “mimicked” detectors provide a training set of positive and negative examples of interest ponts and WaldBoost learns a detector, (significantly) faster than the providers of the training set, formed as a linear combination of efficiently computable feature. In RANSAC, we show how to exploit Wald's test in a randomised model verification procedure to obtain an algorithm significantly faster than deterministic verification yet with equivalent probabilistic guarantees of correctness.

Organizers: Gerard Pons-Moll


Scalable Surface-Based Stereo Matching

Talk
  • 10 April 2014 • 14:00:00
  • Daniel Scharstein
  • MRC seminar room (0.A.03)

Stereo matching -- establishing correspondences between images taken from nearby viewpoints -- is one of the oldest problems in computer vision.  While impressive progress has been made over the last two decades, most current stereo methods do not scale to the high-resolution images taken by today's cameras since they require searching the full space of all possible disparity hypotheses over all pixels.

In this talk I will describe a new scalable stereo method that only evaluates a small portion of the search space.  The method first generates plane hypotheses from matched sparse features, which are then refined into surface hypotheses using local slanted plane sweeps over a narrow disparity range.  Finally, each pixel is assigned to one of the local surface hypotheses. The technique achieves significant speedups over previous algorithms and achieves state-of-the-art accuracy on high-resolution stereo pairs of up to 19 megapixels.

I will also present a new dataset of high-resolution stereo pairs with subpixel-accurate ground truth, and provide a brief outlook on the upcoming new version of the Middlebury stereo benchmark.


  • Simo Särkkä
  • Max Planck House Lecture Hall

Gaussian process regression is a non-parametric Bayesian machine learning paradigm, where instead of estimating parameters of fixed-form functions, we model the whole unknown functions as Gaussian processes. Gaussian processes are also commonly used for representing uncertainties in models of dynamic systems in many applications such as tracking, navigation, and automatic control systems. The latter models are often formulated as state-space models, where the use of non-linear Kalman filter type of methods is common. The aim of this talk is to discuss connections of Kalman filtering methods and Gaussian process regression. In particular, I discuss representations of Gaussian processes as state-space models, which enable the use of computationally efficient Kalman-filter-based (or more general Bayesian-filter-based) solutions to Gaussian process regression problems. This also allows for computationally efficient inference in latent force models (LFM), which are models combining first-principles mechanical models with non-parametric Gaussian process regression models.

Organizers: Philipp Hennig


  • Rainer Dahlhaus
  • Max Planck House Lecture Hall

(joint work with Jan. C. Neddermeyer) A technique for online estimation of spot volatility for high-frequency data is developed. The algorithm works directly on the transaction data and updates the volatility estimate immediately after the occurrence of a new transaction. Furthermore, a nonlinear market microstructure noise model is proposed that reproduces several stylized facts of high frequency data. A computationally efficient particle filter is used that allows for the approximation of the unknown efficient prices and, in combination with a recursive EM algorithm, for the estimation of the volatility curve. We neither assume that the transaction times are equidistant nor do we use interpolated prices. We also make a distinction between volatility per time unit and volatility per transaction and provide estimators for both. More precisely we use a model with random time change where spot volatility is decomposed into spot volatility per transaction times the trading intensity - thus highlighting the influence of trading intensity on volatility.

Organizers: Michel Besserve


Simulation in physical scene understanding

IS Colloquium
  • 28 March 2014 • 11:15 12:45
  • Peter Battaglia
  • Max Planck House Lecture Hall

Our ability to understand a scene is central to how we interact with our environment and with each other. Classic research on visual scene perception has focused on how people "know what is where by looking", but this talk will explore people's ability to infer the "hows" and "whys" of their world, and in particular, how they form a physical understanding of a scene. From a glance we can know so much: not only what objects are where, but whether they are movable, fragile, slimy, or hot; whether they were made by hand, by machine, or by nature; whether they are broken and how they could be repaired; and so on. I posit that these common-sense physical intuitions are made possible by the brain's sophisticated capacity for constructing and manipulating a rich mental representation of a scene via a mechanism of approximate probabilistic simulation -- in short, a physics engine in the head. I will present a series of recent and ongoing studies that develop and test this computational model in a variety of prediction, inference, and planning tasks. Our model captures various aspects of people's experimental judgments, including the accuracy of their performance as well as several illusions and errors. These results help explain core aspects of human mental models that are instrumental to how we understand and act in our everyday world. They also open new directions for developing robotic and AI systems that can perceive, reason, and act the way people do.

Organizers: Michel Besserve


Video-based Analysis of Humans and Their Behavior

Talk
  • 27 March 2014 • 14:00:00
  • Stan Sclaroff
  • MRC Seminar room (0.A.03)

This talk will give an overview of some of the research in the Image and Video Computing Group at Boston University related to image- and video-based analysis of humans and their behavior, including: tracking humans, localizing and classifying actions in space-time, exploiting contextual cues in action classification, estimating human pose from images, analyzing the communicative behavior of children in video, and sign language recognition and retrieval.

Collaborators in this work include (in alphabetical order): Vassilis Athitsos, Qinxun Bai, Margrit Betke, R. Gokberk Cinbis, Kun He, Nazli Ikizler-Cinbis, Hao Jiang, Liliana Lo Presti, Shugao Ma, Joan Nash, Carol Neidle, Agata Rozga, Tai-peng Tian, Ashwin Thangali, Zheng Wu, and Jianming Zhang.


Multi-View Perception of Dynamic Scenes

IS Colloquium
  • 20 March 2014 • 11:15:00 12:30
  • Edmond Boyer
  • Max Planck House Lecture Hall

The INRIA MORPHEO research team is working on the perception of moving shapes using multiple camera systems. Such systems allows to recover dense information on shapes and their motions using visual cues. This opens avenues for research investigations on how to model, understand and animate real dynamic shapes using several videos. In this talk I will more particularly focus on recent activities in the team on two fundamental components of the multi-view perception of dynamic scenes that are: (i) the recovery of time-consistent shape models or shape tracking and (ii) the segmentation of objects in multiple views and over time. 
 

Organizers: Gerard Pons-Moll


  • Prof. Yoshinari Kameda
  • MRC seminar room (0.A.03)

This talk presents our 3D video production method by which a user can watch a  real game from any free viewpoint. Players in the game are captured by 10 cameras and they are reproduced three dimensionally by billboard based representation in real time. Upon producing the 3D video, we have also worked on good user interface that can enable people move the camera intuitively. As the speaker is also working on wide variety of computer vision to augmented reality, selected recent works will be also introduced briefly.

Dr. Yoshinari Kameda started his research from human pose estimation as his Ph.D thesis, then he expands his interested topics from computer vision, human interface, and augmented reality.
He is now an associate professor at University of Tsukuba.
He is also a member of Center for Computational Science of U-Tsukuba where some outstanding super-computer s are in operation.
He served International Symposium on Mixed and Augmented Reality as a area chair for four years (2007-2010).


  • Christof Hoppe
  • MRC Seminar Room

3D reconstruction from 2D still-images (Structure-from-Motion) has reached maturity and together with new image acquisition devices like Micro Aerial Vehicles (MAV), new interesting application scenarios arise. However, acquiring an image set which is suited for a complete and accurate reconstruction is even for expert users a non-trivial task. To overcome this problem, we propose two different methods. In the first part of the talk, we will present a SfM method that performs sparse reconstruction of 10Mpx still-images and a surface extraction from sparse and noisy 3D point clouds in real-time. We therefore developed a novel efficient image localisation method and a robust surface extraction that works in a fully incremental manner directly on sparse 3D points without a densification step. The real-time feedback of the reconstruction quality the enables the user to control the acquisition process interactively. In the second part, we will present ongoing work of a novel view planning method that is designed to deliver a set of images that can be processed by today's multi-view reconstruction pipelines.