From: NeurIPS.cc/2022/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: The authors propose the use of test-time reconstruction with GFS-Nets as a means of adapting to scenes with a larger number of objects than was encountered during training. They show that this achieves significant performance improvements. However, as Reviewer BAtn pointed out, these performance improvements with test-time reconstruction seem limited to their GFS-Nets architecture, and it in fact hurts performance on a different architecture (Mask2Former). This raises some doubts as to the generality of their test-time reconstruction procedure.

We recommend accepting this paper for the workshop because we believe it will be interesting to the computer vision community to have a segmentation model on which test-time reconstruction significantly improves generalization. However, we encourage the authors to focus on the analysis of the test-time adaptation (and its interaction with architecture), which is the part of the paper that is relevant to the theme of the workshop.