The effect of scene context on weakly supervised semantic segmentation

TL;DR

This paper introduces a scene recommender system that enhances weakly supervised semantic segmentation by incorporating scene context, improving object discrimination in challenging scenarios with limited labels.

Contribution

It proposes a novel scene recommendation approach to incorporate scene context, boosting segmentation accuracy in weakly supervised settings.

Findings

Scene context addition improves segmentation accuracy.

The method is compatible with existing baselines.

Experimental results validate effectiveness for scene-specific objects.

Abstract

Image semantic segmentation is parsing image into several partitions in such a way that each region of which involves a semantic concept. In a weakly supervised manner, since only image-level labels are available, discriminating objects from the background is challenging, and in some cases, much more difficult. More specifically, some objects which are commonly seen in one specific scene (e.g. 'train' typically is seen on 'railroad track') are much more likely to be confused. In this paper, we propose a method to add the target-specific scenes in order to overcome the aforementioned problem. Actually, we propose a scene recommender which suggests to add some specific scene contexts to the target dataset in order to train the model more accurately. It is notable that this idea could be a complementary part of the baselines of many other methods. The experiments validate the effectiveness of the proposed method for the objects for which the scene context is added.