Learning Unbiased Zero-Shot Semantic Segmentation Networks Via Transductive Transfer
Fengmao Lv, Haiyang Liu, Yichen Wang, Jiayi Zhao, Guowu Yang
Abstract
Semantic segmentation aims to obtain a detailed understanding of images. Deep learning has achieved great advances in semantic segmentation over the past years. In practice, however, the classes do not always correspond to the ones in the training stage. Since it is impractical to collect sufficient labeled data for all classes, zero-shot semantic segmentation has received increasing attentions recently. Although semantic segmentation neural networks can transfer knowledge from seen classes to unseen classes by incorporating the class-level semantic information, it shows a strong bias towards seen classes. In this letter, we propose an easy-to-implement transductive approach to alleviate the prediction bias in zero-shot semantic segmentation. We assume that both source images with full pixel-level labels and unlabeled target images are available for training. The source images are used to build the relationship between visual images and class-level semantic embeddings. On the other hand, the target images are used to alleviate the bias towards seen classes. Comprehensive experiments over the PASCAL dataset clearly demonstrate the effectiveness of our approach.