Object Discovery with a Copy-Pasting GAN
By Relja Arandjelovi'c, Andrew Zisserman. 2019
59 citations recorded by the source corpus.
We tackle the problem of object discovery, where objects are segmented for a given input image, and the system is trained without using any direct supervision whatsoever. A novel copy-pasting GAN framework is proposed, where the generator learns to discover an object in one image by compositing it into another image such that the discriminator cannot tell that the resulting image is fake. After carefully addressing subtle issues, such as preventing the generator from `cheating', this game results in the generator learning to select objects, as copy-pasting objects is most likely to fool the discriminator. The system is shown to work well on four very different datasets, including large object appearance variations in challenging cluttered backgrounds.
Subfields: Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Dacheng Tao
- Andrea Vedaldi
- Xiaogang Wang
- Jitendra Malik
- Andreas Geiger
- Andrew Zisserman
Citations in the corpus
- GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations (2019, background): “[22] and Bielski and Favaro [23] segment a single foreground object per image, Arandjelović and Zisserman [24] are able to segment several synthetic objects superimposed on natural images.”
- Distilling Localization for Self-Supervised Representation Learning (2020, methodology): “As a result, a network cannot be expected to discover objects unless it is driven to do so [1].”
- GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields (2020, background): “Recent works therefore investigate how the synthesis process can be controlled at the object-level [3,4,7,18,19,26,45,86,90].”
- Learning to Manipulate Individual Objects in an Image (2020, background): “Later, [2] proposes Copy-Pasting GAN to discover binary object masks in images; however, special care has to be taken to prevent trivial solutions.”