PARTS: Unsupervised Segmentation With Slots, Attention and Independence Maximization

From an early age, humans perceive the visual world as composed of coherent objects with distinctive properties such as shape, size, and color. There is great interest in building models that are able to learn similar structure, ideally in an unsupervised manner. Learning such structure from complex 3D scenes that include clutter, occlusions, interactions, and camera motion is still an open challenge. We present a model that is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner. Our model (named PARTS) builds on recent approaches that utilize iterative amortized inference and transition dynamics for deep generative models. We achieve dramatic improvements in performance by introducing several novel contributions. We introduce a recurrent slot-attention like encoder which allows for top-down influence during inference. Unlike prior work, we eschew using an auto-regressive prior when modeling image sequences, and demonstrate that a fixed frame-independent prior is superior for the purpose of scene segmentation and representation learning. We demonstrate our model's success on three different video datasets (the popular benchmark CLEVRER; a simulated 3D Playroom environment; and a real-world Robotics Arm dataset). Finally, we analyze the contributions of the various model components and the representations learned by the model.

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