# A CNN-RNN Architecture for Multi-Label Weather Recognition

arXiv: https://arxiv.org/abs/1904.10709 · HTML: https://mlsanity.com/paper/1904.10709/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 13 of 13)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning](https://mlsanity.com/paper/2104.14042/) (2021)
- [Weather GAN: Multi-Domain Weather Translation Using Generative Adversarial Networks](https://mlsanity.com/paper/2103.05422/) (2021)
- [MASK-CNN-Transformer For Real-Time Multi-Label Weather Recognition](https://mlsanity.com/paper/2304.14857/) (2023)
- [SEM-POS: Grammatically and Semantically Correct Video Captioning](https://mlsanity.com/paper/2303.14829/) (2023)
- [Real-Time Environment Condition Classification for Autonomous Vehicles](https://mlsanity.com/paper/2405.19305/) (2024)
- [Towards Stable Co-saliency Detection and Object Co-segmentation](https://mlsanity.com/paper/2209.12138/) (2022)
- [A system of vision sensor based deep neural networks for complex driving scene analysis in support of crash risk assessment and prevention](https://mlsanity.com/paper/2106.10319/) (2021)
- [Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives](https://mlsanity.com/paper/2204.02149/) (2022)
- [RobuMTL: Enhancing Multi-Task Learning Robustness Against Weather Conditions](https://mlsanity.com/paper/2601.10921/) (2026)
- [Contextual Intelligent Decisions: Expert Moderation of Machine Outputs for Fair Assessment of Commercial Driving](https://mlsanity.com/paper/2202.09816/) (2022)
- [Modeling Weather Uncertainty for Multi-weather Co-Presence Estimation](https://mlsanity.com/paper/2403.20092/) (2024)
- [Predicting Subsurface Abnormalities Growth using Physics-Informed Neural Networks](https://mlsanity.com/paper/2609.01417/) (2026)

## Abstract (arXiv)

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.