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Institute Talks

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

  • Dr. Islam S. M. Khali
  • Stuttgart 2P4

Mechanical removal of blood clots is a promising approach towards the treatment of vascular diseases caused by the pathological clot formation in the circulatory system. These clots can form and travel to deep seated regions in the circulatory system, and result in significant problems as blood flow past the clot is obstructed. A microscopi-cally small helical microrobot offers great promise in the minimally-invasive removal of these clots. These helical microrobots are powered and controlled remotely using externally-applied magnetic fields for motion in two- and three-dimensional spaces. This talk will describe the removal of blood clots in vitro using a helical robot under ultrasound guidance. The talk will briefly introduce the interactions between the helical microrobot and the fibrin network of the blood clots during its removal. It will also introduce the challenges unique to medical imaging at micro-scale, followed by the concepts and theory of the closed-loop motion control using ultrasound feedback. It will then cover the latest experimental results for helical and flagellated microrobots and their biomedical and nanotechnology applications.

Organizers: Metin Sitti


Daniel Renjewski: bipedal gait mechanisms

Talk
  • 04 May 2018 • 11 12
  • Daniel Renjewski
  • 2p4

Daniel Renjewski presents research in bipedal gait mechanisms: 'Passive mechanisms for increased power and efficiency in bipedal gait’


  • Dr. Yiğit Mengüç
  • Room 3P02 - Stuttgart

Incredible biological capabilities have emerged through evolution. Of special note is the material intelligence that defines the bodies of living things, blurring the line between brain and body. Material robotics research takes the approach of imbuing power, control, sensing, and actuation into all aspects of a (primarily soft) robot body. In this talk, the research topics of material robotics currently underway in the mLab at Oregon State University will be presented. Soft active materials designed and researched in the mLab include liquid metal, biodegradable elastomers, and electroactive fluids. Bioinspired mechanisms include octopus-inspired soft muscles, gecko-inspired adhesives, and snake-like locomotors. Such capabilities, however, introduce new fundamental challenge in making materially-enabled robots. To address these limitation, the mLab is also innovating in techniques to rapidly and scalably manufacture soft materials. Though significant challenges remain to be solved, the development of such soft and materially-enabled components promises to bring robots more and more into our daily lives.

Organizers: Metin Sitti


  • JP Lewis
  • PS Aquarium, 3rd floor, north, MPI-IS

The definition of art has been debated for more than 1000 years, and continues to be a puzzle. While scientific investigations offer hope of resolving this puzzle, machine learning classifiers that discriminate art from non-art images generally do not provide an explicit definition, and brain imaging and psychological theories are at present too coarse to provide a formal characterization. In this work, rather than approaching the problem using a machine learning approach trained on existing artworks, we hypothesize that art can be defined in terms of preexisting properties of the visual cortex. Specifically, we propose that a broad subset of visual art can be defined as patterns that are exciting to a visual brain. Resting on the finding that artificial neural networks trained on visual tasks can provide predictive models of processing in the visual cortex, our definition is operationalized by using a trained deep net as a surrogate “visual brain”, where “exciting” is defined as the activation energy of particular layers of this net. We find that this definition easily discriminates a variety of art from non-art, and further provides a ranking of art genres that is consistent with our subjective notion of ‘visually exciting’. By applying a deep net visualization technique, we can also validate the definition by generating example images that would be classified as art. The images synthesized under our definition resemble visually exciting art such as Op Art and other human- created artistic patterns.

Organizers: Michael Black


Automatic Understanding of the Visual World

Talk
  • 26 April 2018 • 11:00 12:00
  • Dr. Cordelia Schmid
  • N3.022

One of the central problems of artificial intelligence is machine perception, i.e., the ability to understand the visual world based on input from sensors such as cameras. In this talk, I will present recent progress with respect to data generation using weak annotations, motion information and synthetic data. I will also discuss our recent results for action recognition, where human tubes and tubelets have shown to be successful. Our tubelets moves away from state-of-the-art frame based approaches and improve classification and localization by relying on joint information from several frames. I also show how to extend this type of method to weakly supervised learning of actions, which allows us to scale to large amounts of data with sparse manual annotation. Furthermore, I discuss several recent extensions, including 3D pose estimation.

Organizers: Ahmed Osman


  • Preeya Khanna
  • Heisenbergstr. 3, Room 2P4

Actions constitute the way we interact with the world, making motor disabilities such as Parkinson’s disease and stroke devastating. The neurological correlates of the injured brain are challenging to study and correct given the adaptation, redundancy, and distributed nature of our motor system. However, recent studies have used increasingly sophisticated technology to sample from this distributed system, improving our understanding of neural patterns that support movement in healthy brains, or compromise movement in injured brains. One approach to translating these findings to into therapies to restore healthy brain patterns is with closed-loop brain-machine interfaces (BMIs). While closed-loop BMIs have been discussed primarily as assistive technologies the underlying techniques may also be useful for rehabilitation.

Organizers: Katherine Kuchenbecker


Consistency and minimax rates of random forests

Talk
  • 18 April 2018 • 13:30 14:45
  • Erwan Scornet
  • Tübingen, Main seminar room (N0.002)

The recent and ongoing digital world expansion now allows anyone to have access to a tremendous amount of information. However collecting data is not an end in itself and thus techniques must be designed to gain in-depth knowledge from these large data bases.

Organizers: Mara Cascianelli


  • Alexander Mathis
  • Tübingen, Aquarium (N3.022)

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, yet markers are intrusive (especially for smaller animals), and the number and location of the markers must be determined a priori. Here, we present a highly efficient method for markerless tracking based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in a broad collection of experimental settings: mice odor trail-tracking, egg-laying behavior in drosophila, and mouse hand articulation in a skilled forelimb task. For example, during the skilled reaching behavior, individual joints can be automatically tracked (and a confidence score is reported). Remarkably, even when a small number of frames are labeled (≈200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.

Organizers: Melanie Feldhofer


Machine Learning for Tactile Manipulation

IS Colloquium
  • 13 April 2018 • 11:00 12:00
  • Jan Peters
  • MPI-IS Stuttgart, Heisenbergstr. 3, Room 2P4

Today’s robots have motor abilities and sensors that exceed those of humans in many ways: They move more accurately and faster; their sensors see more and at a higher precision and in contrast to humans they can accurately measure even the smallest forces and torques. Robot hands with three, four, or five fingers are commercially available, and, so are advanced dexterous arms. Indeed, modern motion-planning methods have rendered grasp trajectory generation a largely solved problem. Still, no robot to date matches the manipulation skills of industrial assembly workers despite that manipulation of mechanical objects remains essential for the industrial assembly of complex products. So, why are current robots still so bad at manipulation and humans so good?

Organizers: Katherine Kuchenbecker


BodyNet: Volumetric Inference of 3D Human Body Shapes

Talk
  • 10 April 2018 • 16:00 17:00
  • Gül Varol
  • N3.022

Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging due to factors such as variation in human bodies, clothing and viewpoint. Prior methods addressing this problem typically attempt to fit parametric body models with certain priors on pose and shape. In this work we argue for an alternative representation and propose BodyNet, a neural network for direct inference of volumetric body shape from a single image. BodyNet is an end-to-end trainable network that benefits from (i) a volumetric 3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them results in performance improvement as demonstrated by our experiments. To evaluate the method, we fit the SMPL model to our network output and show state-of-the-art results on the SURREAL and Unite the People datasets, outperforming recent approaches. Besides achieving state-of-the-art performance, our method also enables volumetric body-part segmentation.