Few-shot Shape Recognition by Learning Deep Shape-aware Features
Wenlong Shi Changsheng Lu Ming Shao Yinjie Zhang Siyu Xia Piotr Koniusz
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.