Salvage of Supervision in Weakly Supervised Object Detection and Segmentation

Abstract

Weakly supervised vision tasks, including detection and segmentation, have attracted much attention in the vision community recently. However, the lack of detailed and precise annotations in the weakly supervised case leads to a large accuracy gap between weakly- and fully-supervised methods. In this paper, we propose a new framework, Salvage of Supervision(SoS), with the key idea being to effectively harness every potentially useful supervisory signal in weakly supervised vision tasks. Starting with weakly supervised object detection(WSOD), we propose SoS-WSOD to shrink the technology gap between WSOD and FSOD, which utilizes the weak image-level labels, the pseudo-labels, and the power of semi-supervised object detection for WSOD. Moreover, SoS-WSOD removes restrictions in traditional WSOD methods, including the reliance on ImageNet pretraining and inability to use modern backbones. The SoS framework also extends to weakly supervised semantic segmentation and instance segmentation. On several weakly supervised vision benchmarks, SoS achieves significant performance boost and generalization ability.