DISENTANGLING 3D PROTOTYPICAL NETWORKS FOR FEW-SHOT CONCEPT LEARNING

Abstract

We present neural architectures that disentangle RGB-D images into objects' shapes and styles and a map of the background scene, and explore their applications for few-shot 3D object detection and few-shot concept classification. Our networks incorporate architectural biases that reflect the image formation process, 3D geometry of the world scene, and shape-style interplay. They are trained end-to-end self-supervised by predicting views in static scenes, alongside a small number of 3D object boxes. Objects and scenes are represented in terms of 3D feature grids in the bottleneck of the network. We show that the proposed 3D neural representations are compositional: they can generate novel 3D scene feature maps by mixing object shapes and styles, resizing and adding the resulting object 3D feature maps over background scene feature maps. We show that classifiers for object categories, color, materials, and spatial relationships trained over the disentangled 3D feature sub-spaces generalize better with dramatically fewer examples than the current state-of-the-art, and enable a visual question answering system that uses them as its modules to generalize one-shot to novel objects in the scene.

1. INTRODUCTION

Humans can learn new concepts from just one or a few samples. Consider the example in Figure 1 . Assuming there is a person who has no prior knowledge about blue and carrot, by showing this person an image of a blue carrot and telling him "this is an carrot with blue color", the person can easily generalize from this example to (1) recognizing carrots of varying colors, 3D poses and viewing conditions and under novel background scenes, (2) recognizing the color blue on different objects, (3) combine these two concepts with other concepts to form a novel object coloring he/she has never seen before, e.g., red carrot or blue tomato and (4) using the newly learned concepts to answer questions regarding the visual scene. Motivated by this, we explore computational models that can achieve these four types of generalization for visual concept learning. We propose disentangling 3D prototypical networks (D3DP-Nets), a model that learns to disentangle RGB-D images into objects, their 3D locations, sizes, 3D shapes and styles, and the background scene, as shown in Figure 2 . Our model can learn to detect objects from a few 3D object bounding box annotations and can further disentangle objects into different attributes through a self-supervised view prediction task. Specifically, D3DP-Nets uses differentiable unprojection and rendering operations to go back and forth between the input RGB-D (2.5D) image and a 3D scene feature map. From the scene feature map, our model learns to detect objects and disentangles each object into a 3D shape code and an 1D style code through a shape/style disentangling antoencoder. We use adaptive instance normalization layers (Huang & Belongie, 2017) to encourage shape/style disentanglement within each object. Our key intuition is to represent objects and their shapes in terms of 3D feature representations disentangled from style variability so that the model can correspond objects with similar shape by explicitly rotating and scaling their 3D shape representations during matching.

availability

https://mihirp1998.github.io/project_pages/d3dp/ 

