Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
This work addresses the problem of subtle attribute recognition in computer vision, which is incremental as it builds on fine-grained recognition but introduces a new dataset and method for specific challenging cases.
The paper tackles the challenging task of distinguishing sunrise from sunset images, a subtle attribute recognition problem, by proposing a new pairwise learning strategy that significantly outperforms baseline methods and even human performance, and also achieves state-of-the-art results when applied to other tasks like temperature estimation.
The difficulty of image recognition has gradually increased from general category recognition to fine-grained recognition and to the recognition of some subtle attributes such as temperature and geolocation. In this paper, we try to focus on the classification between sunrise and sunset and hope to give a hint about how to tell the difference in subtle attributes. Sunrise vs. sunset is a difficult recognition task, which is challenging even for humans. Towards understanding this new problem, we first collect a new dataset made up of over one hundred webcams from different places. Since existing algorithmic methods have poor accuracy, we propose a new pairwise learning strategy to learn features from selective pairs of images. Experiments show that our approach surpasses baseline methods by a large margin and achieves better results even compared with humans. We also apply our approach to existing subtle attribute recognition problems, such as temperature estimation, and achieve state-of-the-art results.