Generative Adversarial Networks for Unsupervised Object Co-localization

TL;DR

This paper proposes a novel unsupervised object co-localization method using GAN discriminators, revealing that high image diversity hampers localization and achieving competitive accuracy with weakly-supervised methods.

Contribution

It introduces a new approach leveraging GAN discriminator analysis for unsupervised object localization, highlighting the negative impact of image diversity on localization performance.

Findings

High image diversity in GANs negatively affects object localization.

Discriminator focus extends beyond target objects to background and other objects.

Proposed method achieves competitive accuracy on benchmark datasets.

Abstract

This paper introduces a novel approach for unsupervised object co-localization using Generative Adversarial Networks (GANs). GAN is a powerful tool that can implicitly learn unknown data distributions in an unsupervised manner. From the observation that GAN discriminator is highly influenced by pixels where objects appear, we analyze the internal layers of discriminator and visualize the activated pixels. Our important finding is that high image diversity of GAN, which is a main goal in GAN research, is ironically disadvantageous for object localization, because such discriminators focus not only on the target object, but also on the various objects, such as background objects. Based on extensive evaluations and experimental studies, we show the image diversity and localization performance have a negative correlation. In addition, our approach achieves meaningful accuracy for unsupervised object co-localization using publicly available benchmark datasets, even comparable to state-of-the-art weakly-supervised approach.