CVPR 2023 – A Strong Baseline for Generalized Few-Shot Semantic Segmentation

In this episode we discuss A Strong Baseline for Generalized Few-Shot Semantic Segmentation
by Sina Hajimiri, Malik Boudiaf, Ismail Ben Ayed, Jose Dolz. The paper focuses on introducing a generalized few-shot segmentation framework with a simple and easy-to-optimize inference phase and training process. They propose a model based on the InfoMax principle, where the Mutual Information (MI) between the learned feature representations and their corresponding predictions is maximized. The proposed model improves the few-shot segmentation benchmarks, PASCAL-5i and COCO-20i, by 7% to 26% and 3% to 12%, respectively, for novel classes in 1-shot and 5-shot scenarios. The code used in the study is publicly available.