Boosting Adverse Weather Crowd Counting via Multi-queue Contrastive Learning
Primary Area: representation learning for computer vision, audio, language, and other modalities
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Keywords: Crowd counting, adverse weather, multi-queue contrastive learning.
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Abstract: Currently, most crowd counting methods perform well under normal weather conditions. However, they often struggle to maintain their performance in extreme and adverse weather conditions due to significant differences in the domain and a lack of adverse weather images for training. To address this issue and enhance the model's robustness in adverse weather, we propose a two-stage crowd counting method. In the first stage, we introduce a multi-queue MoCo contrastive learning strategy to tackle the problem of class imbalance caused by weather variations. This strategy facilitates the learning of weather-aware representations by the model. In the second stage, we employ the supervised contrastive loss to guide the refinement process, enabling the conversion of the weather-aware representations to the normal weather domain. In addition, we also created a new synthetic adverse weather dataset. Extensive experimental results show that our method achieves competitive performance.
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Submission Number: 5061
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