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Supervised object detection and semantic segmentation require object or evenpixel level annotations. When there exist image level labels only, it ischallenging for weakly supervised algorithms to achieve accurate predictions.The accuracy achieved by top weakly supervised algorithms is stillsignificantly lower than their fully supervised counterparts. In this paper, wepropose a novel weakly supervised curriculum learning pipeline for multi-labelobject recognition, detection and semantic segmentation. In this pipeline, wefirst obtain intermediate object localization and pixel labeling results forthe training images, and then use such results to train task-specific deepnetworks in a fully supervised manner. The entire process consists of fourstages, including object localization in the training images, filtering andfusing object instances, pixel labeling for the training images, andtask-specific network training. To obtain clean object instances in thetraining images, we propose a novel algorithm for filtering, fusing andclassifying object instances collected from multiple solution mechanisms. Inthis algorithm, we incorporate both metric learning and density-basedclustering to filter detected object instances. Experiments show that ourweakly supervised pipeline achieves state-of-the-art results in multi-labelimage classification as well as weakly supervised object detection and verycompetitive results in weakly supervised semantic segmentation on MS-COCO,PASCAL VOC 2007 and PASCAL VOC 2012.

Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning