no code implementations • ICML 2020 • Alexander Vezhnevets, Yuhuai Wu, Maria Eckstein, Rémi Leblond, Joel Z. Leibo
This paper investigates generalisation in multi-agent games, where the generality of the agent can be evaluated by playing against opponents it hasn't seen during training.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 3 Mar 2015 • Davide Modolo, Alexander Vezhnevets, Vittorio Ferrari
We present Context Forest (ConF), a technique for predicting properties of the objects in an image based on its global appearance.
no code implementations • CVPR 2015 • Davide Modolo, Alexander Vezhnevets, Olga Russakovsky, Vittorio Ferrari
We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum.
no code implementations • 6 Jan 2015 • Alexander Vezhnevets, Vittorio Ferrari
We propose a method for annotating the location of objects in ImageNet.
no code implementations • CVPR 2015 • Abel Gonzalez-Garcia, Alexander Vezhnevets, Vittorio Ferrari
First, we exploit context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set.
no code implementations • CVPR 2014 • Alexander Vezhnevets, Vittorio Ferrari
By transferring knowledge from the images that have bounding-box annotations to the others, our method is capable of automatically populating ImageNet with many more bounding-boxes and even pixel-level segmentations.