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：我们提出了类比网络（Analogical Networks），该模型通过显式编码领域知识（以结构化标注的三维场景集合形式）与隐式参数化知识相结合，并借助类比推理实现三维物体场景分割：模型并非直接将场景映射为部件分割结果，而是首先从记忆中检索相关场景及其对应的部件结构，随后通过端到端可学习的调制机制，为输入场景预测类比部件结构。通过基于多个检索记忆进行条件化处理，模型可预测混合匹配不同检索记忆中部件结构的组合。无论记忆样例来自单个、少量还是多个样本，类比网络通过基于适当记忆集合的条件化处理并推断类比解析，统一处理单样本、少样本与多样本学习。实验表明，类比网络在多样本场景下与当前最先进的三维分割变换器性能相当，在少样本场景下则优于现有元学习与少样本学习范式。仅需扩展记忆库而无需更新权重，类比网络即可成功分割新物体类别的实例。我们的代码与模型已在项目网站（http://analogicalnets.github.io/）公开。