ECCV 2020: Some Highlights

The 2020 European Conference on Computer Vision took place online, from 23 to 28 August, and consisted of 1360 papers, divided into 104 orals, 160 spotlights and the rest of 1096 papers as posters. As it is the case in recent years with ML and CV conferences, the huge number of papers can be overwhelming at times. Similar to my CVPR2020 post, to get a grasp of the general trends of the conference this year, I will present in this blog post a sort of a snapshot of the conference by summarizing some papers (& listing some) that grabbed my attention. Disclaimer: This post is not a representation of the papers and subjects presented in ECCV 2020; it is just a personnel overview of what I found interesting. The statistics presented in this section are taken from the official Opening & Awards presentation. Let's start by some general statistics: The trends of earlier years continued with more than 200% increase in submitted papers compared to the 2018 conference, and with a similar number of papers to CVPR 2020. As expected, this increase is joined by a corresponding increase in the number of reviewers and area chairs to accommodate this expansion. As expected, the majority of the accepted papers focus on topics related to deep learning, recognition, detection, and understanding. Similar to CVPR 2020, we see an increasing interest in growing areas such as label-efficient methods (e.g., unsupervised learning) and low-level vision. In terms of institutions; similar to ICML this year, Google takes the lead with 180 authors, followed by The Chinese University of Hong Kong with 140 authors and Peking University with 110 authors. In the next sections, we'll present some paper summaries by subject. The task of object detection consists of localizing and classifying objects visible given an input image. The popular framework for object detection consist of pre-defining a set of boxes (ie., a set of geometric priors like anchors or region proposals), which are first classified, followed by a regression step to the adjust the dimensions of the predefined box, and then a post-processing step to remove duplicate predictions.