1 research outputs found
A CNN-RNN Architecture for Multi-Label Weather Recognition
Weather Recognition plays an important role in our daily lives and many
computer vision applications. However, recognizing the weather conditions from
a single image remains challenging and has not been studied thoroughly.
Generally, most previous works treat weather recognition as a single-label
classification task, namely, determining whether an image belongs to a specific
weather class or not. This treatment is not always appropriate, since more than
one weather conditions may appear simultaneously in a single image. To address
this problem, we make the first attempt to view weather recognition as a
multi-label classification task, i.e., assigning an image more than one labels
according to the displayed weather conditions. Specifically, a CNN-RNN based
multi-label classification approach is proposed in this paper. The
convolutional neural network (CNN) is extended with a channel-wise attention
model to extract the most correlated visual features. The Recurrent Neural
Network (RNN) further processes the features and excavates the dependencies
among weather classes. Finally, the weather labels are predicted step by step.
Besides, we construct two datasets for the weather recognition task and explore
the relationships among different weather conditions. Experimental results
demonstrate the superiority and effectiveness of the proposed approach. The new
constructed datasets will be available at
https://github.com/wzgwzg/Multi-Label-Weather-Recognition.Comment: One weather recognition dataset is constructe