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Analogical Networks casts fine-grained 3D visual parsing as analogy-forming inference.

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Analogy-Forming Transformers for Few-Shot 3D Parsing Analogy-Forming Transformers for Few-Shot 3D Parsing Nikolaos Gkanatsios * , Mayank Singh * , Zhaoyuan Fang , Shubham Tulsiani , Katerina Fragkiadaki Carnegie Mellon University * Equal contribution Paper Code Arxiv Abstract We present Analogical Networks , a model that casts fine-grained 3D visual parsing as analogy-forming inference: instead of mapping input scenes to part labels, which is hard to adapt in a few-shot manner to novel inputs, our model retrieves related scenes from memory and their corresponding part structures, and predicts analogous part structures in the input object 3D point cloud, via an end-to-end learnable modulation mechanism. By conditioning on more than one retrieved memories, compositions of structures are predicted, that mix and match parts across the retrieved memories. One-shot, few-shot or many-shot learning are treated uniformly in Analogical Netwo...

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