Visibility Estimation via Deep Label Distribution Learning
semanticscholar(2021)
摘要
This 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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