Few-Shot Strip Steel Surface Defect Segmentation via Pre-Trained Variational Auto-Encoder-Based Latent Gaussian Process Regression.

Abstract

Recently, few-shot strip steel surface defect segmentation has received more and more concerns. However, the existing few-shot segmentation methods usually adopt the frozen encoder, which is pre-trained on the classification task and can only provide class-related knowledge. Therefore, we propose a novel method, namely pre-trained variational auto-encoder based latent gaussian process regression (LGPR), to conduct few-shot strip steel surface defect segmentation. Firstly, different from previous methods, the frozen Variational Auto-Encoder (VAE) based encoder and decoder, which are pre-trained by using the pixel-level self-supervised task (i.e., image reconstruction), can provide rich image-related knowledge. This ensures the effective characterization of defect regions. Secondly, by deploying a gaussian process regression in the latent feature space generated by the VAE-based encoder, pixel-level correlation between support features and query features can be efficiently built. This operation is non-parametric and doesn't bring any training overhead. Besides, we deploy transformer-based projectors to dig long-range contextual cues of support and query features. Extensive experiments are performed on two public datasets, and the experimental results clearly show that our model consistently outperforms the state-of-the-art models with a large margin. Both the codes and results are publicly available at https://github.com/Hlao-hub/LGPR.