Weakly Supervised Object Detection with Segmentation Collaboration
This work addresses the challenge of training accurate object detectors with limited supervision, which is crucial for applications like image analysis where manual annotation is costly, though it is an incremental improvement over existing methods.
The paper tackles the problem of weakly supervised object detection, where only image-level labels are available, by proposing a collaborative approach that integrates a generative adversarial segmentation module with a detection module to improve bounding box accuracy, achieving 51.0% accuracy on PASCAL VOC 2007 and outperforming state-of-the-art methods.
Weakly supervised object detection aims at learning precise object detectors, given image category labels. In recent prevailing works, this problem is generally formulated as a multiple instance learning module guided by an image classification loss. The object bounding box is assumed to be the one contributing most to the classification among all proposals. However, the region contributing most is also likely to be a crucial part or the supporting context of an object. To obtain a more accurate detector, in this work we propose a novel end-to-end weakly supervised detection approach, where a newly introduced generative adversarial segmentation module interacts with the conventional detection module in a collaborative loop. The collaboration mechanism takes full advantages of the complementary interpretations of the weakly supervised localization task, namely detection and segmentation tasks, forming a more comprehensive solution. Consequently, our method obtains more precise object bounding boxes, rather than parts or irrelevant surroundings. Expectedly, the proposed method achieves an accuracy of 51.0% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-arts and demonstrating its superiority for weakly supervised object detection.