Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute
Recognition
release_373uavcewbaynnm6ikl2vhvdci
by
Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu
2017
Abstract
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.
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