Google organizes a workshop to bring together key researchers from academia and Google to exchange ideas and forge new collaborations. The key theme of the workshop is computational imaging, which aims to produce visual representations of data and physical processes beyond what current imaging instruments can do today by simultaneously designing algorithms and hardware.
Prof. Paolo Favaro is invited to speak at the event and presents the latest work in his group on image deblurring from the classic model-based methods to the more recent deep learning approaches.
Our research  contributed to the discovery of important patterns in the EEG signals of coma patients. Read more about it in the official news article here.
S. Jonas, A. Rossetti, M. Oddo, S. Jenni, P. Favaro and F. Zubler, "EEG-based Outcome Prediction after Cardiac Arrest with Convolutional Neural Networks: Performance and Visualization of Discriminative Features", in Human Brain Mapping, 2019.
PhD student Adam Bielski just got his paper accepted (with spotlight!) in the upcoming NeurIPS conference. It is his first publication since he started the PhD in our group. Congratulations on your excellent work.
Please find the abstract below and keep an eye on our publications page as it will get updated with details about the NeurIPS submission.
We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments can be perturbed locally relative to a given background without affecting the realism of a scene. Our approach is to first train a generative model of a layered scene. The layered representation consists of a background image, a foreground image and the mask of the foreground. A composite image is then obtained by overlaying the masked foreground image onto the background. The generative model is trained in an adversarial fashion against a discriminator, which forces the generative model to produce realistic composite images. To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background. Because the generator is unaware of the shift before computing its output, it must produce layered representations that are realistic for any such random perturbation. Finally, we learn to segment an image by defining an autoencoder consisting of an encoder, which we train, and the pre-trained generator as the decoder, which we freeze. The encoder maps an image to a feature vector, which is fed as input to the generator to give a composite image matching the original input image. Because the generator outputs an explicit layered representation of the scene, the encoder learns to detect and segment objects. We demonstrate this framework on real images of several object categories.
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) is one of the biggest conferences in computer science . This year it took place in Long Beach, California from June 16-20. With an unprecedented 1300 accepted papers and over 5000 submissions this year, the conference is currently growing at an exponential rate. From the graphic below we can also see that the top keywords of interest are: image, detection, 3D object, video, segmentation, adversarial, recognition, visual. The frequency of the topics graph, representation, and cloud have doubled.
Our group has two accepted papers in CVPR 2019:
Learning to Extract Flawless Slow Motion from Blurry Videos
M. Jin, Z. Hu and P. Favaro
Full text and more information is available on our publications page.
Credit: Conference statistics and plots by Hoseong Lee.