NeurIPS 2020

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

Meta Review

This submission proposes a method animal 2D pose estimation and tracking given limited amounts of ground truth annotations. It initially received four reviews with diverging scores (5,6,7,4), which remained unchanged after the rebuttal. The reviewers appreciated importance of the application, solid empirical performance compared to DeepLabCut (including tests on downstream tasks) and insightful analysis of the learned representations. At the same time, the main concerns of the reviewers were limited methodological novelty beyond applying known methods to the new domain of animal tracking, as well as limitations in the empirical studies. This case was further discussed between the AC and the SAC, who arrived to the conclusion that the merits of this submission in advancing animal tracking outweigh its limitations. The final recommendation is to accept as a poster.