An easy zero-shot learning combination: Texture Sensitive Semantic
  Segmentation IceHrNet and Advanced Style Transfer Learning Strategy
Zhiyong Yang Yuelong Zhu Xiaoqin Zeng Jun Zong Xiuheng Liu Ran Tao Xiaofei Cong Yufeng Yu
We proposed an easy method of Zero-Shot semantic segmentation by using style
transfer. In this case, we successfully used a medical imaging dataset (Blood
Cell Imagery) to train a model for river ice semantic segmentation. First, we
built a river ice semantic segmentation dataset IPC_RI_SEG using a fixed camera
and covering the entire ice melting process of the river. Second, a
high-resolution texture fusion semantic segmentation network named IceHrNet is
proposed. The network used HRNet as the backbone and added ASPP and Decoder
segmentation heads to retain low-level texture features for fine semantic
segmentation. Finally, a simple and effective advanced style transfer learning
strategy was proposed, which can perform zero-shot transfer learning based on
cross-domain semantic segmentation datasets, achieving a practical effect of
87% mIoU for semantic segmentation of river ice without target training dataset
(25% mIoU for None Stylized, 65% mIoU for Conventional Stylized, our strategy
improved by 22%). Experiments showed that the IceHrNet outperformed the
state-of-the-art methods on the texture-focused dataset IPC_RI_SEG, and
achieved an excellent result on the shape-focused river ice datasets. In
zero-shot transfer learning, IceHrNet achieved an increase of 2 percentage
points compared to other methods. Our code and model are published on
https://github.com/PL23K/IceHrNet.