Ali Diba, Vivek Sharma, Rainer Stiefelhagen and Luc Van Gool. In IEEE CVPR Workshop on Compact and Efficient Feature Representation and Learning in Computer Vision (CEFRL), 2019

Abstract

The deep generative adversarial networks (GAN) recently have been shown to be promising for different computer vision applications, like image editing, synthesizing high-resolution images, generating videos, etc. These networks and the corresponding learning scheme can handle various visual space mappings. We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discovery in object detection pipelines. A crucial advantage of our method is that it learns a new deep similarity metric, to distinguish multiple objects in one image. We demonstrate that the network can act as an encoder-decoder generating part of an image which contains an object, or as a modified deep CNN to represent images for object detection in the supervised and weakly supervised scheme. Our ranking GAN offers a novel way to search through images for object-specific patterns. We have conducted experiments for different scenarios and demonstrate the method performance for object synthesizing and weakly supervised object detection and classification using the MS-COCO and PASCAL VOC datasets.