Few-shot Shape Recognition by Learning Deep Shape-aware Features

TL;DR

This paper introduces a novel few-shot shape descriptor that learns deep, shape-aware features using a dual attention mechanism and shape primitives, enabling accurate recognition of unseen shapes with limited samples.

Contribution

It proposes the first few-shot shape descriptor with a shape decomposition and reconstruction framework, improving generalization to unseen shapes in few-shot scenarios.

Findings

Significantly outperforms state-of-the-art methods in few-shot shape classification.

The shape primitives enable interpretable and extendable shape representations.

The approach achieves high accuracy across five diverse datasets.

Abstract

Traditional shape descriptors have been gradually replaced by convolutional neural networks due to their superior performance in feature extraction and classification. The state-of-the-art methods recognize object shapes via image reconstruction or pixel classification. However , these methods are biased toward texture information and overlook the essential shape descriptions, thus, they fail to generalize to unseen shapes. We are the first to propose a fewshot shape descriptor (FSSD) to recognize object shapes given only one or a few samples. We employ an embedding module for FSSD to extract transformation-invariant shape features. Secondly, we develop a dual attention mechanism to decompose and reconstruct the shape features via learnable shape primitives. In this way, any shape can be formed through a finite set basis, and the learned representation model is highly interpretable and extendable to unseen shapes. Thirdly, we propose a decoding module to include the supervision of shape masks and edges and align the original and reconstructed shape features, enforcing the learned features to be more shape-aware. Lastly, all the proposed modules are assembled into a few-shot shape recognition scheme. Experiments on five datasets show that our FSSD significantly improves the shape classification compared to the state-of-the-art under the few-shot setting.