4 accepted papers
Bayesian inference is the standard for providing full predictive distributions with well calibrated uncertainty estimates. However, scaling to a modern, overparameterized deep learning setting typically comes at the cost of severe and restrictive approximations, sacrificing model predictive stren…
In this work, we solve the problem of motion representation in videos, according to local transformations applied to specific keypoints extracted from static the images. First, we compute the co-ordinates of the keypoints of the body or face through a pre-trained model, and then we introduce a convo…
Recent architectures for object detection adopt a Feature Pyramid Network as a backbone for deep feature extraction. Many works focus on the design of pyramid networks which produce richer feature representations. In this work, we opt to learn a dataset-specific architecture for Feature Pyramid Netw…
In this paper, we present Wasserstein Inception Distance (WInD), a novel metric for evaluating performance of Generative Adversarial Networks (GANs). The proposed metric extends on the rationale of the previously proposed Frechet Inception Distance (FID), in the sense that GAN performance is quantif…