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The problem of computing category agnostic bounding box proposals is utilizedas a core component in many computer vision tasks and thus has lately attracteda lot of attention. In this work we propose a new approach to tackle thisproblem that is based on an active strategy for generating box proposals thatstarts from a set of seed boxes, which are uniformly distributed on the image,and then progressively moves its attention on the promising image areas whereit is more likely to discover well localized bounding box proposals. We callour approach AttractioNet and a core component of it is a CNN-based categoryagnostic object location refinement module that is capable of yielding accurateand robust bounding box predictions regardless of the object category.  We extensively evaluate our AttractioNet approach on several image datasets(i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting onall of them state-of-the-art results that surpass the previous work in thefield by a significant margin and also providing strong empirical evidence thatour approach is capable to generalize to unseen categories. Furthermore, weevaluate our AttractioNet proposals in the context of the object detection taskusing a VGG16-Net based detector and the achieved detection performance on COCOmanages to significantly surpass all other VGG16-Net based detectors while evenbeing competitive with a heavily tuned ResNet-101 based detector. Code as wellas box proposals computed for several datasets are available at::https://github.com/gidariss/AttractioNet.

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization