<ul><li>Object-centric architectures can learn to extract distinct object representations from visual scenes.</li><li>RGB color space is commonly assumed to be optimal for unsupervised learning in computer vision.</li><li>This work challenges the assumption and explores the use of other color spaces, such as HSV.</li><li>The proposed approach, using the RGB-S color space, improves reconstruction and disentanglement in object-centric representation learning.</li></ul>

Leveraging Color Channel Independence for Improved Unsupervised Object Detection

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