# ClearVision: Leveraging CycleGAN and SigLIP-2 for Robust All-Weather Classification in Traffic Camera Imagery

arXiv: https://arxiv.org/abs/2504.19684 · HTML: https://mlsanity.com/paper/2504.19684/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [EVA-02: A Visual Representation for Neon Genesis](https://mlsanity.com/paper/2303.11331/) (2023)
- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [MASK-CNN-Transformer For Real-Time Multi-Label Weather Recognition](https://mlsanity.com/paper/2304.14857/) (2023)
- [Near real-time map building with multi-class image set labelling and classification of road conditions using convolutional neural networks](https://mlsanity.com/paper/2001.09947/) (2020)

## Built on by (most cited first, 2 of 2)

- [Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems](https://mlsanity.com/paper/2506.14096/) (2025)
- [Temporal Zoom Networks: Distance Regression and Continuous Depth for Efficient Action Localization](https://mlsanity.com/paper/2511.03943/) (2025)

## Abstract (arXiv)

Adverse weather conditions challenge safe transportation, necessitating robust real-time weather detection from traffic camera imagery. We propose a novel framework combining CycleGAN-based domain adaptation with efficient contrastive learning to enhance weather classification, particularly in low-light nighttime conditions. Our approach leverages the lightweight SigLIP-2 model, which employs pairwise sigmoid loss to reduce computational demands, integrated with CycleGAN to transform nighttime images into day-like representations while preserving weather cues. Evaluated on an Iowa Department of Transportation dataset, the baseline EVA-02 model with CLIP achieves a per-class overall accuracy of 96.55% across three weather conditions (No Precipitation, Rain, Snow) and a day/night overall accuracy of 96.55%, but shows a significant day-night gap (97.21% day vs. 63.40% night). With CycleGAN, EVA-02 improves to 97.01% per-class accuracy and 96.85% day/night accuracy, boosting nighttime performance to 82.45%. Our Vision-SigLIP-2 + Text-SigLIP-2 + CycleGAN + Contrastive configuration excels in nighttime scenarios, achieving the highest nighttime accuracy of 85.90%, with 94.00% per-class accuracy and 93.35% day/night accuracy. This model reduces training time by 89% (from 6 hours to 40 minutes) and inference time by 80% (from 15 seconds to 3 seconds) compared to EVA-02. By narrowing the day-night performance gap from 33.81 to 8.90 percentage points, our framework provides a scalable, efficient solution for all-weather classification using existing camera infrastructure.