Institute Talks

- Rıza Alp Güler

- N3.022 (Aquarium)

Non-planar object deformations result in challenging but informative signal variations. We aim to recover this information in a feedforward manner by employing discriminatively trained convolutional networks. We formulate the task as a regression problem and train our networks by leveraging upon manually annotated correspondences between images and 3D surfaces. In this talk, the focus will be on our recent work "DensePose", where we form the "COCO-DensePose" dataset by introducing an efficient annotation pipeline to collect correspondences between 50K persons appearing in the COCO dataset and the SMPL 3D deformable human-body model. We use our dataset to train CNN-based systems that deliver dense correspondences 'in the wild', namely in the presence of background, occlusions, multiple objects and scale variations. We experiment with fully-convolutional networks and region-based DensePose-RCNN model and observe a superiority of the latter; we further improve accuracy through cascading, obtaining a system that delivers highly accurate results in real time (http://densepose.org).

Organizers: Georgios Pavlakos

- Rıza Alp Güler

- N3.022 (Aquarium)

Non-planar object deformations result in challenging but informative signal variations. We aim to recover this information in a feedforward manner by employing discriminatively trained convolutional networks. We formulate the task as a regression problem and train our networks by leveraging upon manually annotated correspondences between images and 3D surfaces. In this talk, the focus will be on our recent work "DensePose", where we form the "COCO-DensePose" dataset by introducing an efficient annotation pipeline to collect correspondences between 50K persons appearing in the COCO dataset and the SMPL 3D deformable human-body model. We use our dataset to train CNN-based systems that deliver dense correspondences 'in the wild', namely in the presence of background, occlusions, multiple objects and scale variations. We experiment with fully-convolutional networks and region-based DensePose-RCNN model and observe a superiority of the latter; we further improve accuracy through cascading, obtaining a system that delivers highly accurate results in real time (http://densepose.org).

Organizers: Georgios Pavlakos

- Daniel Scharstein

- Ground Floor Seminar Room (N0.002)

Two talks for the price of one! I will present my recent work on the challenging problem of stereo matching of scenes with little or no surface texture, attacking the problem from two very different angles. First, I will discuss how surface orientation priors can be added to the popular semi-global matching (SGM) algorithm, which significantly reduces errors on slanted weakly-textured surfaces. The orientation priors serve as a soft constraint during matching and can be derived in a variety of ways, including from low-resolution matching results and from monocular analysis and Manhattan-world assumptions. Second, we will examine the pathological case of Mondrian Stereo -- synthetic scenes consisting solely of solid-colored planar regions, resembling paintings by Piet Mondrian. I will discuss assumptions that allow disambiguating such scenes, present a novel stereo algorithm employing symbolic reasoning about matched edge segments, and discuss how similar ideas could be utilized in robust real-world stereo algorithms for untextured environments.

Organizers: Anurag Ranjan

- Jim Mainprice

- N3.022 (Aquarium)

Humans act upon their environment through motion, the ability to plan their movements is therefore an essential component of their autonomy. In recent decades, motion planning has been widely studied in robotics and computer graphics. Nevertheless robots still fail to achieve human reactivity and coordination. The need for more efficient motion planning algorithms has been present through out my own research on "human-aware" motion planning, which aims to take the surroundings humans explicitly into account. I believe imitation learning is the key to this particular problem as it allows to learn both, new motion skills and predictive models, two capabilities that are at the heart of "human-aware" robots while simultaneously holding the promise of faster and more reactive motion generation. In this talk I will present my work in this direction.

Archived Talks

- Georgios Arvanitidis

- S2 Seminar Room

The fundamental building block in many learning models is the distance measure that is used. Usually, the linear distance is used for simplicity. Replacing this stiff distance measure with a flexible one could potentially give a better representation of the actual distance between two points. I will present how the normal distribution changes if the distance measure respects the underlying structure of the data. In particular, a Riemannian manifold will be learned based on observations. The geodesic curve can then be computed—a length-minimizing curve under the Riemannian measure. With this flexible distance measure we get a normal distribution that locally adapts to the data. A maximum likelihood estimation scheme is provided for inference of the parameters mean and covariance, and also, a systematic way to choose the parameter defining the Riemannian manifold. Results on synthetic and real world data demonstrate the efficiency of the proposed model to fit non-trivial probability distributions.

Organizers: Philipp Hennig

- Prof. Dr. Hedvig Kjellström

- N3.022 / Aquarium

In this talk I will first outline my different research projects. I will then focus on the EACare project, a quite newly started multi-disciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer's disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. I describe the scope and method of the project, and report on a first Wizard of Oz prototype.

- Yeara Kozlov

- Aquarium

Creating convincing human facial animation is challenging. Face animation is often hand-crafted by artists separately from body motion. Alternatively, if the face animation is derived from motion capture, it is typically performed while the actor is relatively still. Recombining the isolated face animation with body motion is non-trivial and often results in uncanny results if the body dynamics are not properly reflected on the face (e.g. cheeks wiggling when running). In this talk, I will discuss the challenges of human soft tissue simulation and control. I will then present our method for adding physical effects to facial blendshape animation. Unlike previous methods that try to add physics to face rigs, our method can combine facial animation and rigid body motion consistently while preserving the original animation as closely as possible. Our novel simulation framework uses the original animation as per-frame rest-poses without adding spurious forces. We also propose the concept of blendmaterials to give artists an intuitive means to control the changing material properties due to muscle activation.

Organizers: Timo Bolkart

IS Colloquium

- 21 August 2017 • 11:15 12:15

- Sanmi Koyejo

- Empirical Inference meeting room (MPI-IS building, 4th floor)

Performance metrics are a key component of machine learning systems, and are ideally constructed to reflect real world tradeoffs. In contrast, much of the literature simply focuses on algorithms for maximizing accuracy. With the increasing integration of machine learning into real systems, it is clear that accuracy is an insufficient measure of performance for many problems of interest. Unfortunately, unlike accuracy, many real world performance metrics are non-decomposable i.e. cannot be computed as a sum of losses for each instance. Thus, known algorithms and associated analysis are not trivially extended, and direct approaches require expensive combinatorial optimization. I will outline recent results characterizing population optimal classifiers for large families of binary and multilabel classification metrics, including such nonlinear metrics as F-measure and Jaccard measure. Perhaps surprisingly, the prediction which maximizes the utility for a range of such metrics takes a simple form. This results in simple and scalable procedures for optimizing complex metrics in practice. I will also outline how the same analysis gives optimal procedures for selecting point estimates from complex posterior distributions for structured objects such as graphs. Joint work with Nagarajan Natarajan, Bowei Yan, Kai Zhong, Pradeep Ravikumar and Inderjit Dhillon.

Organizers: Mijung Park

- Mirko Bordignon

- AMD meeting

Writing and maintaining programs for robots poses some interesting challenges. It is hard to generalize them, as their targets are more than computing platforms. It can be deceptive to see them as input to output mappings, as interesting environments result in unpredictable inputs, and mixing reactive and deliberative behavior make intended outputs hard to define. Given the wide and fragmented landscape of components, from hardware to software, and the parties involved in providing and using them, integration is also a non-trivial aspect. The talk will illustrate the work ongoing at Fraunhofer IPA to tackle these challenges, how Open Source is its common trait, and how this translates into the industrial field thanks to the ROS-Industrial initiative.

Organizers: Vincent Berenz

- Manfred K. Warmuth

- Seminar room N4.022, department Schölkopf (4th floor)

Organizers: Bernhard Schölkopf

Talk

- 24 July 2017 • 11:00 12:00

- Ioannis Papantonis

- S2 Seminar Room

We present a way to set the step size of Stochastic Gradient Descent, as the solution of a distance minimization problem. The obtained result has an intuitive interpretation and resembles the update rules of well known optimization algorithms. Also, asymptotic results to its relation to the optimal learning rate of Gradient Descent are discussed. In addition, we talk about two different estimators, with applications in Variational inference problems, and present approximate results about their variance. Finally, we combine all of the above, to present an optimization algorithm that can be used on both mini-batch optimization and Variational problems.

Organizers: Philipp Hennig

IS Colloquium

- 17 July 2017 • 11:15 12:15

- Azzurra Ruggeri

How do young children learn so much about the world, and so efficiently? This talk presents the recent studies investigating theoretically and empirically how children actively seek information in their physical and social environments as evidence to test and dynamically revise their hypotheses and theories over time. In particular, it will focus on how children adapt their active learning strategies. such as question-asking and explorative behavior, in response to the task characteristics, to the statistical structure of the hypothesis space, and to the feedback received. Such adaptiveness and flexibility is crucial to achieve efficiency in situations of uncertainty, when testing alternative hypotheses, making decisions, drawing causal inferences and solving categorization tasks.

Organizers: Philipp Hennig Georg Martius

- Prof. Andrew Blake

- MPI-IS, ground floor seminar room, N0.002

Neural networks have taken the world of computing in general and AI in particular by storm. But in the future, AI will need to revisit generative models. There are several reasons for this – system robustness, precision, transparency, and the high cost of labelling data. This is particularly true of perceptual AI, as needed for autonomous vehicles, where also the need for simulators and the need to confront novel situations, also will demand generative, probabilistic models.

Organizers: Bernhard Schölkopf Michael Black Stefan Schaal

- Matteo Poggi

- PS Seminar Room (N3.022)

Recently, deep learning proved to be successful also on low level vision tasks such as stereo matching. Another recent trend in this latter field is represented by confidence measures, with increasing effectiveness when coupled with random forest classifiers or CNNs. Despite their excellent accuracy in outliers detection, few other applications rely on them. In the first part of the talk, we'll take a look at the latest proposal in terms of confidence measures for stereo matching, as well as at some novel methodologies exploiting these very accurate cues. In the second part, we'll talk about GC-net, a deep network currently representing the state-of-the-art on the KITTI datasets, and its extension to motion stereo processing.

Organizers: Yiyi Liao