Visual Computing Seminar (Fall 2018)
The Visual computing seminar is a weekly seminar series on topics in Visual Computing.
Why: The motivation for creating this seminar is that EPFL has a critical mass of people who are working on subtly related topics in computational photography, computer graphics, geometry processing, human–computer interaction, computer vision and signal processing. Having a weekly point of interaction will provide exposure to interesting work in this area and increase awareness of our shared interests and other commonalities like the use of similar computational tools — think of this as the visual computing edition of the “Know thy neighbor” seminar series.
Who: The target audience are faculty, students and postdocs in the visual computing disciplines, but the seminar is open to anyone and guests are welcomed. There is no need to formally enroll in a course. The format is very flexible and will include 45 minute talks with Q&A, talks by external visitors, as well as shorter presentations. In particular, the seminar is also intended as a way for students to obtain feedback on shorter ~20min talks preceding a presentation at a conference. If you are a student or postdoc in one of the visual computing disciplines, you’ll probably receive email from me soon on scheduling a presentation.
Where and when: every Wednesday in BC01 (note the changed location!). Food is served at 11:50, and the actual talk starts at 12:15.
How to be notified: If you want to be kept up to date with announcements, please send me an email and I’ll put you on the list. If you are working in LCAV, CVLAB, IVRL, LGG, LSP, IIG, CHILI, LDM or RGL, you are automatically subscribed to future announcements, so there is nothing you need to do.
You may add the seminar events to Google Calendar (click the '+' button in the bottom-right corner), or download the iCal file.
Blue Brain Brayns, A platform for high fidelity large-scale and interactive visualization of scientific data and brain structures
The Blue Brain Project has made major efforts to create morphologically accurate neurons to simulate sub-cellular and electrical activities, for example, molecular simulations of neuron biochemistry or multi-scale simulations of neuronal function.
Overcoming neural brainwashing
We identify a phenomenon, which we dub neural brainwashing, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search. Adding weight plasticity in neural architecture search preserves the best models to the end of the search process leading to improved results in both natural language processing and computer vision tasks.
Good Features to Correlate for Visual Tracking
In this talk, I will mainly talk about visual object tracking problem which I worked on during my Ph.D. studies. As a secondary topic, I will mention my recent research activities about garment virtualization by deep learning.
|24.10.2018||Krishna Kanth Nakka||
Deep Attentional Structured Representation Learning for Visual Recognition
|31.10.2018||Kevin Gonyop Kim||
Expanding experience of the learners in vocational education
Vocational education and training (VET) that takes place in dual contexts of school and workplace is a well-established secondary education system in Switzerland. Although it is known as an effective system for developing vocational competence, there exists some gap between what they are supposed to learn and what they practice at workplaces. The workplace experiences are usually limited to concrete situations in particular environments and their connections to the general knowledge learned from schools are often weak.
DESIA: A General Framework for Designing Interlocking Assemblies
Interlocking assemblies have a long history in the design of puzzles, furniture, architecture, and other complex geometric structures. The key defining property of interlocking assemblies is that all component parts are immobilized by their geometric arrangement, preventing the assembly from falling apart. Computer graphics research has recently contributed design tools that allow creating new interlocking assemblies. However, these tools focus on specific kinds of assemblies and explore only a limited space of interlocking configurations, which restricts their applicability for design.
In this talk, we present a new general framework for designing interlocking assemblies. The core idea is to represent part relationships with
a family of base Directional Blocking Graphs and leverage efficient graph analysis tools to compute an interlocking arrangement of parts. This avoids the exponential complexity of brute-force search. Our algorithm iteratively constructs the geometry of assembly components, taking advantage of all existing blocking relations for constructing successive parts. As a result, our approach supports a wider range of assembly forms compared to previous methods and provides significantly more design flexibility. We show that our framework facilitates efficient design of complex interlocking assemblies, including new solutions that cannot be achieved by state of the art approaches.
Deformable surface reconstruction from a single view
Active Drone Based Human Pose Estimation
Self-supervised Representation Learning for Human Motion Capture