PEEKABOO: Hiding parts of an image for unsupervised object localization

TL;DR

PEEKABOO introduces a simple, effective single-stage framework for unsupervised object localization by hiding parts of images and using remaining context to infer object locations, outperforming existing methods.

Contribution

It proposes a novel context-based learning approach with image masking for unsupervised object localization, reducing computational complexity and improving accuracy.

Findings

Competitive performance on benchmark datasets

Effective in both object discovery and salient object detection

Simplifies the unsupervised localization process

Abstract

Localizing objects in an unsupervised manner poses significant challenges due to the absence of key visual information such as the appearance, type and number of objects, as well as the lack of labeled object classes typically available in supervised settings. While recent approaches to unsupervised object localization have demonstrated significant progress by leveraging self-supervised visual representations, they often require computationally intensive training processes, resulting in high resource demands in terms of computation, learnable parameters, and data. They also lack explicit modeling of visual context, potentially limiting their accuracy in object localization. To tackle these challenges, we propose a single-stage learning framework, dubbed PEEKABOO, for unsupervised object localization by learning context-based representations at both the pixel- and shape-level of the localized objects through image masking. The key idea is to selectively hide parts of an image and leverage the remaining image information to infer the location of objects without explicit supervision. The experimental results, both quantitative and qualitative, across various benchmark datasets, demonstrate the simplicity, effectiveness and competitive performance of our approach compared to state-of-the-art methods in both single object discovery and unsupervised salient object detection tasks. Code and pre-trained models are available at: https://github.com/hasibzunair/peekaboo