Jul 24, 2023
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Slot attention has shown remarkable object-centric representation learning performance in computer vision tasks without requiring any supervision. Despite of its object-centric binding ability brought by compositional modelling, as a deterministic module, slot attention lacks the ability to generate novel scenes. In this paper, we propose the Slot-VAE, a generative model that integrates slot attention with the hierarchical VAE framework for object-centric structured image generation. From each image, the model simultaneously infers a global scene representation to capture high-level scene structure and object-centric slot representations to embed individual object components. During generation, slot representations are generated from global scene representation to ensure coherent scene structure. Our extensive evaluation of the image generation ability indicates that Slot-VAE achieves better or comparable scene structure accuracy and sample quality compared to slot-based baselines.Slot attention has shown remarkable object-centric representation learning performance in computer vision tasks without requiring any supervision. Despite of its object-centric binding ability brought by compositional modelling, as a deterministic module, slot attention lacks the ability to generate novel scenes. In this paper, we propose the Slot-VAE, a generative model that integrates slot attention with the hierarchical VAE framework for object-centric structured image generation. From each i…
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