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Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Ondrej Biza · Sjoerd van Steenkiste · Mehdi S. M. Sajjadi · Gamaleldin Elsayed · Aravindh Mahendran · Thomas Kipf

Wed Jul 26 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #137

Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning. Recent slot-based neural networks that learn about objects in a self-supervised manner have made exciting progress in this direction. However, they typically fall short at adequately capturing spatial symmetries present in the visual world, which leads to sample inefficiency, such as when entangling object appearance and pose. In this paper, we present a simple yet highly effective method for incorporating spatial symmetries via slot-centric reference frames. We incorporate equivariance to per-object pose transformations into the attention and generation mechanism of Slot Attention by translating, scaling, and rotating position encodings. These changes result in little computational overhead, are easy to implement, and can result in large gains in terms of data efficiency and overall improvements to object discovery. We evaluate our method on a wide range of synthetic object discovery benchmarks namely CLEVR, Tetrominoes, CLEVRTex, Objects Room and MultiShapeNet, and show promising improvements on the challenging real-world Waymo Open dataset.

Author Information

Ondrej Biza (Northeastern University, Boston Dynamics AI Institute)
Sjoerd van Steenkiste (IDSIA)
Mehdi S. M. Sajjadi (Google)
Gamaleldin Elsayed (Google DeepMind)

Gamaleldin F. Elsayed is a Research Scientist at Google DeepMind interested in deep learning and computational neuroscience research. In particular, his research is focused on studying properties and problems of artificial neural networks and designing better machine learning models with inspiration from neuroscience. In 2017, he completed his PhD in Neuroscience from Columbia University at the Center for Theoretical Neuroscience. During his PhD, he contributed to the field of computational neuroscience through designing machine learning methods for identifying and validating structures in complex neural data. Prior to that, he completed his B.S. from The American University in Cairo with a major in Electronics Engineering and a minor in Computer Science, and earned M.S. degrees in electrical engineering from KAUST and Washington University in St. Louis. Before his Graduate studies, he was also a professional athlete and Olympian Fencer. He competed at The 2008 Olympic Games in Beijing with the Egyptian Saber team.

Aravindh Mahendran (Google)
Thomas Kipf (Google DeepMind)

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