Weakly Supervised Object Detection Research Articles

Learning Object Scale With Click Supervision for Object Detection

Weakly-supervised object detection has recently attracted increasing attention since it only requires image-level annotations. However, the performance obtained by existing methods is still far from being satisfactory compared with fully-supervised object detection methods. To achieve a good trade-off between annotation cost and object detection performance, we propose a simple yet effective method which incorporates CNN visualization with click supervision to generate the pseudo ground-truths (i.e., bounding boxes). These pseudo ground-truths can be used to train a fully-supervised detector. To estimate the object scale, we firstly adopt a proposal selection algorithm to preserve high-quality proposals, and then generate Class Activation Maps (CAMs) for these preserved proposals by the proposed CNN visualization algorithm called Spatial Attention CAM. Finally, we fuse these CAMs together to generate pseudo ground-truths and train a fully-supervised object detector with these ground-truths. Experimental results on the PASCAL VOC 2007 and VOC 2012 datasets show that the proposed method can obtain much higher accuracy for estimating the object scale, compared with the state-of-the-art image-level based methods and the center-click based method.

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection.

Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a novel deep network for WSOD. Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning (MIL), our strategy generates proposal clusters to learn refined instance classifiers by an iterative process. The proposals in the same cluster are spatially adjacent and associated with the same object. This prevents the network from concentrating too much on parts of objects instead of whole objects. We first show that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then show that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method. The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one. Experiments are conducted on the PASCAL VOC, ImageNet detection, and MS-COCO benchmarks for WSOD. Results show that our method outperforms the previous state of the art significantly.

Weakly-supervised object detection via mining pseudo ground truth bounding-boxes

Recently, weakly-supervised object detection has attracted much attention, since it does not require expensive bounding-box annotations while training the network. Although significant progress has also been made, there is still a large gap on the performance between weakly-supervised and fully-supervised object detection. To mitigate this gap, some works try to use the pseudo ground truths generated by a weakly-supervised detector to train a supervised detector. However, such approaches incline to find the most representative parts instead of the whole body of an object, and only seek one ground truth bounding-box per class even though many same-class instances exist in an image. To address these issues, we propose a weakly-supervised to fully-supervised framework (W2F), where a weakly-supervised detector is implemented using multiple instance learning. And then, we propose a pseudo ground-truth excavation (PGE) algorithm to find the accurate pseudo ground truth bounding-box for each instance. Moreover, the pseudo ground-truth adaptation (PGA) algorithm is designed to further refine those pseudo ground truths mined by PGE algorithm. Finally, the mined pseudo ground truths are used as supervision to train a fully-supervised detector. Additionally, we also propose an iterative ground-truth learning (IGL) approach, which enhances the quality of the pseudo ground truths by using the predictions of the fully-supervised detector iteratively. Extensive experiments on the challenging PASCAL VOC 2007 and 2012 benchmarks strongly demonstrate the effectiveness of our method. We obtain 53.1% and 49.4% mAP on VOC2007 and VOC2012 respectively, which is a significant improvement over previous state-of-the-art methods.