Temporal-consistent CAMs for Weakly Supervised Video Segmentation in Waste Sorting

TL;DR

This paper introduces a weakly supervised video segmentation method that leverages temporal coherence to produce accurate masks in industrial waste sorting scenarios, reducing annotation costs and improving segmentation quality.

Contribution

The proposed approach integrates temporal consistency into weakly supervised segmentation training, enhancing mask accuracy in video streams without extensive manual annotations.

Findings

Improved segmentation accuracy in waste sorting videos.

Effective use of temporal coherence during classifier training.

Demonstrated benefits on real-world industrial dataset.

Abstract

In industrial settings, weakly supervised (WS) methods are usually preferred over their fully supervised (FS) counterparts as they do not require costly manual annotations. Unfortunately, the segmentation masks obtained in the WS regime are typically poor in terms of accuracy. In this work, we present a WS method capable of producing accurate masks for semantic segmentation in the case of video streams. More specifically, we build saliency maps that exploit the temporal coherence between consecutive frames in a video, promoting consistency when objects appear in different frames. We apply our method in a waste-sorting scenario, where we perform weakly supervised video segmentation (WSVS) by training an auxiliary classifier that distinguishes between videos recorded before and after a human operator, who manually removes specific wastes from a conveyor belt. The saliency maps of this classifier identify materials to be removed, and we modify the classifier training to minimize differences between the saliency map of a central frame and those in adjacent frames, after having compensated object displacement. Experiments on a real-world dataset demonstrate the benefits of integrating temporal coherence directly during the training phase of the classifier. Code and dataset are available upon request.