The year 2020 has presented many challenges, but it did not stop new AI research breakthroughs from the global community. Here is a list of 10 best papers and their presentations from this year’s top AI conferences across machine learning, computer vision, NLP, robotics, and more.
The pandemic in 2020 has caused all major AI conferences to go virtual and thus made the latest research discussions much more accessible to the global community. Here, we put together a list of 10 notable AI papers from this year’s top conferences and publications, covering new research in computer vision, natural language processing, reinforcement learning, recommendation systems, robotics, and more. For each paper, we provide a short summary and link to the original conference talk or explanatory video for a quick grasp of the research highlights.
1. Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
Award: CVPR 2020 Best Paper
Authors: Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi (University of Oxford)
In this paper, the authors present a technique that learns the 3D deformable object categories from raw, single-view, and weakly symmetric object category images which can be trained without any manual supervision. The proposed model works on the basis of an autoencoder network which divides the input scene into albedo, illumination, viewpoint, and depth information. It creates a photo-geometric autoencoding pipeline that is trained using the reconstruction loss without any 3D ground truths, multiple views, or prior shape models. The simulation results show that the proposed method can accurately recover the 3D shape of human faces, cat faces, and cars with fine details, and outperforms other unsupervised methods and even the DepthNet model that uses supervision on 2D image correspondences. [Paper Link]
Watch the conference talk video:
