WS2: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting

In industrial quality control, to visually recognize unwanted items within a moving heterogeneous stream, human operators are often still indispensable. Waste-sorting stands as a significant example, where operators on multiple conveyor belts manually remove unwanted objects to select specific materials. To automate this recognition problem, computer vision systems offer great potential in accurately identifying and segmenting unwanted items in such settings. Unfortunately, considering the multitude and the variety of sorting tasks, fully supervised approaches are not a viable option to address this challange, as they require extensive labeling efforts. Surprisingly, weakly supervised alternatives that leverage the implicit supervision naturally provided by the operator in his removal action are relatively unexplored. In this paper, we define the concept of Before-After Supervision, illustrating how to train a segmentation network by leveraging only the visual differences between images acquired before and after the operator. To promote research in this direction, we introduce WS2 (Weakly Supervised segmentation for Waste-Sorting), the first multi-view dataset consisting of more than 11 000 high-resolution video frames captured on top of a conveyor belt, including 'before' and 'after' images. We also present a robust end-to-end pipeline, used to benchmark several state-of-the-art weakly supervised segmentation methods on WS21The WS2 dataset is publicly available for download at https://zenodo.org/records/14793518, all the details are reported in the supplementary material.