Abstract
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 \textit{before} and \textit{after} the operator. To promote research in this direction, we introduce WS$^2$ (Weakly Supervised segmentation for Waste-Sorting), the first multiview 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 WS$^2$.
Abstract (translated)
在工业质量控制中，为了在流动的异质物料流中视觉识别不需要的物品，人工操作员通常仍然是不可或缺的。废物分拣是这一领域的显著例子，在此过程中，操作员需通过多条传送带手动移除不必要的物体以选出特定材料。为了解决这个问题实现自动化，计算机视觉系统提供了巨大的潜力，能够准确地在这样的环境下识别和分割不需要的物品。然而，考虑到分类任务的数量和多样性，完全监督的方法由于需要大量的标注工作而不现实。令人惊讶的是，那些利用操作员移除动作提供的隐性监督信息的弱监督方法，在这一领域却相对未被充分探索。 本文中我们定义了“前后监督”（Before-After Supervision）的概念，并展示了如何仅通过使用操作员介入前后的视觉差异来训练分割网络。为促进该领域的研究，我们引入了WS$^2$(Weakly Supervised segmentation for Waste-Sorting)，这是首个包含超过1.1万张高分辨率视频帧的多视图数据集，这些图像是在传送带上捕捉到的操作前后影像。此外，我们还提出了一种稳健的端到端管道，并用其对WS$^2$上几种最先进的弱监督分割方法进行了基准测试。
URL
https://arxiv.org/abs/2509.06485
