Enhancing Spatial Consistency and Class-Level Diversity for Segmenting Fine-Grained Objects

Qi Zhao, Binghao Liu, Shuchang Lyu, Chunlei Wang, Yifan Yang

Abstract: Semantic segmentation is a fundamental computer vision task attracting a lot of attention. However, limited works focus on semantic segmentation on fine-grained class scenario, which has more classes and greater inter-class similarity. Due to the lack of data available for this task, we establish two segmentation benchmarks, CUB-seg and FGSCR42-seg, based on CUB and FGSCR42 datasets. To solve the two major problems in this task, spatial inconsistency and extremely similar classes confusion, we propose the Spatial Consistency and Class-level Diversity enhancement Network. First, we build the Spatial Consistency Enhancement Module to take advantage of the low-frequency information in the feature, enhancing the spatial consistency. Second, Fine-grained Regions Contrastive Loss is designed to make the features of different classes more discriminative, promoting the class-level diversity. Extensive experiments show that our method can significantly improve the performance compared to baseline models. Visualization study also prove the effectiveness of our method for enhancing spatial consistency and class-level diversity.