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Download fileVisibility Estimation via Deep Label Distribution Learning
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posted on 2021-06-08, 12:34 authored by Mofei Song, Han XuHan Xu, Xiao Fan LiuXiao Fan Liu, Qian LiThis paper proposes an image-based visibility estimation method with deep label distribution learning. To train
an accurate model for visibility estimation, it is important to
obtain the precise ground truth for every image. However,
the ground-truth visibility is difficult to be labeled due to its
high ambiguity. To solve this problem, we associate a label
distribution to each image. The label distribution contains all the
possible visibilities with their probabilities. To learn from such
annotation, we employ a CNN-RNN model for visibility-aware
feature extraction and a conditional probability neural network
for distribution prediction. Our experiment shows that labeling
the image with visibility distribution can not only overcome
the inaccurate annotation problem, but also boost the learning
performance without the increase of training examples.
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Email Address of Submitting Author
songmf@seu.edu.cnORCID of Submitting Author
0000-0002-9912-1560Submitting Author's Institution
Southeast UniversitySubmitting Author's Country
- China