We present Analogical Networks, a model that encodes domain knowledge explicitly, in a collection of structured labelled 3D scenes, in addition to implicitly, as model parameters, and segments 3D object scenes with analogical reasoning: instead of mapping a scene to part segments directly, our model first retrieves related scenes from memory and their corresponding part structures, and then predicts analogous part structures for the input scene, 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 Networks, by conditioning on the appropriate set of memories, whether taken from a single, few or many memory exemplars, and inferring analogous parses. We show Analogical Networks are competitive with state-of-the-art 3D segmentation transformers in many-shot settings, and outperform them, as well as existing paradigms of meta-learning and few-shot learning, in few-shot settings. Analogical Networks successfully segment instances of novel object categories simply by expanding their memory, without any weight updates. Our code and models are publicly available in the project webpage: http://analogicalnets.github.io/.

翻译：我们提出类比网络（Analogical Networks），该模型将领域知识显式编码为结构化标记的三维场景集合，同时隐式编码为模型参数，并通过类比推理分割三维物体场景：不同于直接将场景映射为部件分割，我们的模型首先从记忆中检索相关场景及其对应的部件结构，然后通过端到端可学习的调制机制，为输入场景预测相似的部件结构。通过基于多个检索记忆的条件约束，模型可预测跨检索记忆混合匹配的部件组合结构。在类比网络中，一次性、少样本及多样本学习被统一处理，通过基于单个、少量或多个记忆样例的条件约束，推理出类比解析结果。实验表明，类比网络在多样本设置下与最先进的三维分割变换器性能相当，在少样本设置下则超越现有元学习与少样本学习范式。类比网络仅通过扩展记忆即可成功分割新物体类别的实例，无需任何权重更新。我们的代码与模型在项目网页公开：http://analogicalnets.github.io/。