# RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions

arXiv: https://arxiv.org/abs/2109.01820 · HTML: https://mlsanity.com/paper/2109.01820/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 64 references that are in the map.
- First posted on arXiv 4 Sep 2021.

## Builds on (most cited first, 30 of 42)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018)
- [Virtual KITTI 2](https://mlsanity.com/paper/2001.10773/) (2020)
- [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://mlsanity.com/paper/1904.07392/) (2019)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://mlsanity.com/paper/2006.02334/) (2020)
- [High-Resolution Representations for Labeling Pixels and Regions](https://mlsanity.com/paper/1904.04514/) (2019)
- [FcaNet: Frequency Channel Attention Networks](https://mlsanity.com/paper/2012.11879/) (2020)
- [Non-Local Recurrent Network for Image Restoration](https://mlsanity.com/paper/1806.02919/) (2018)
- [Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution](https://mlsanity.com/paper/1904.05049/) (2019)
- [See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks](https://mlsanity.com/paper/2001.06810/) (2020)
- [MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects](https://mlsanity.com/paper/1804.09194/) (2018)
- [A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https://mlsanity.com/paper/2005.07431/) (2020)
- [Automatic adaptation of object detectors to new domains using self-training](https://mlsanity.com/paper/1904.07305/) (2019)
- [Night-to-Day Image Translation for Retrieval-based Localization](https://mlsanity.com/paper/1809.09767/) (2018)
- [VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition](https://mlsanity.com/paper/1710.06288/) (2017)
- [Towards Segmenting Anything That Moves](https://mlsanity.com/paper/1902.03715/) (2019)
- [Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature Adaptation](https://mlsanity.com/paper/2010.01402/) (2020)
- [CNN-based Lidar Point Cloud De-Noising in Adverse Weather](https://mlsanity.com/paper/1912.03874/) (2019)
- [Object Discovery in Videos as Foreground Motion Clustering](https://mlsanity.com/paper/1812.02772/) (2018)
- [Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night](https://mlsanity.com/paper/1907.08719/) (2019)
- [Robustness of Object Detectors in Degrading Weather Conditions](https://mlsanity.com/paper/2106.08795/) (2021)
- [FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving](https://mlsanity.com/paper/1910.05395/) (2019)
- [Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection](https://mlsanity.com/paper/2106.01483/) (2021)

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

- [FriendNet: Detection-Friendly Dehazing Network](https://mlsanity.com/paper/2403.04443/) (2024)
- [CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks](https://mlsanity.com/paper/2404.03191/) (2024)

## Abstract (arXiv)

Segmenting each moving object instance in a scene is essential for many applications. But like many other computer vision tasks, this task performs well in optimal weather, but then adverse weather tends to fail. To be robust in weather conditions, the usual way is to train network in data of given weather pattern or to fuse multiple sensors. We focus on a new possibility, that is, to improve its resilience to weather interference through the network's structural design. First, we propose a novel FPN structure called RiWFPN with a progressive top-down interaction and attention refinement module. RiWFPN can directly replace other FPN structures to improve the robustness of the network in non-optimal weather conditions. Then we extend SOLOV2 to capture temporal information in video to learn motion information, and propose a moving object instance segmentation network with RiWFPN called RiWNet. Finally, in order to verify the effect of moving instance segmentation in different weather disturbances, we propose a VKTTI-moving dataset which is a moving instance segmentation dataset based on the VKTTI dataset, taking into account different weather scenes such as rain, fog, sunset, morning as well as overcast. The experiment proves how RiWFPN improves the network's resilience to adverse weather effects compared to other FPN structures. We compare RiWNet to several other state-of-the-art methods in some challenging datasets, and RiWNet shows better performance especially under adverse weather conditions.