Leveraging Color Channel Independence for Improved Unsupervised Object Detection

Bastian Jäckl, Yannick Metz, Udo Schlegel, Daniel A. Keim, Maximilian T. Fischer

Keywords: object detection, visual reasoning, segmentation, unsupervised learning, object centric representation learning, computer vision

TL;DR: Enhancing the common RGB color space with additional uncorrelated color channels significantly increases object detection capabilities of unsupervised object-centric representation learning.

Abstract:

Object-centric architectures can learn to extract distinct object representations from visual scenes, enabling downstream applications on the object level.
Similarly to autoencoder-based image models, object-centric approaches have been trained on the unsupervised reconstruction loss of images encoded by RGB color spaces.
In our work, we challenge the common assumption that RGB images are the optimal target for unsupervised learning in computer vision.
We discuss conceptually and empirically that other color spaces, such as HSV, bear essential characteristics for object-centric representation learning, like robustness to lighting conditions. We further show that models improve when requiring them to predict additional color channels.
Specifically, we propose the RGB-S space, which extends RGB with HSV's saturation component and leads to markedly better reconstruction and disentanglement for five common evaluation datasets.
The use of composite color spaces can be implemented with basically no computational overhead, is agnostic of the models' architecture, and is universally applicable across a wide range of visual computing tasks and training types.
The findings of our approach encourage additional investigations in computer vision tasks beyond object-centric learning.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 11349