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Conference Paper

Adversarial Scene Editing: Automatic Object Removal from Weak Supervision

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Shetty,  Rakshith
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Citation

Shetty, R., Fritz, M., & Schiele, B. (2018). Adversarial Scene Editing: Automatic Object Removal from Weak Supervision. In S. Bengio, H. Wallach, H. Larochelle, K. Graumann, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 31 (pp. 7716-7726). Curran Associates.


Cite as: http://hdl.handle.net/21.11116/0000-0001-7A92-1
Abstract
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to find and remove objects from general scene images using image-level labels and unpaired data in a generative adversarial network (GAN) framework. We achieve this with two key contributions: a two-stage editor architecture consisting of a mask generator and image in-painter that co-operate to remove objects, and a novel GAN based prior for the mask generator that allows us to flexibly incorporate knowledge about object shapes. We experimentally show on two datasets that our method effectively removes a wide variety of objects using weak supervision only