Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset Challenge

• URL: http://arxiv.org/abs/2406.03799v2
• Date: Fri, 7 Jun 2024 02:18:56 GMT
• Title: Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset Challenge
• Authors: Nan Zhang, Xidan Zhang, Jianing Wei, Fangjun Wang, Zhiming Tan,
• Abstract summary: This report describes the winning solution to the WeatherProof dataset challenge. We propose an enhanced semantic segmentation pipeline for this challenge. We introduce a new dataset WeatherProofExtra with wider viewing angle and employ data augmentation methods. Our solution is ranked 1st on the final leaderboard.
• Score: 2.722639169181398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This report describes the winning solution to the WeatherProof Dataset Challenge (CVPR 2024 UG2+ Track 3). Details regarding the challenge are available at https://cvpr2024ug2challenge.github.io/track3.html. We propose an enhanced semantic segmentation pipeline for this challenge. Firstly, we improve semantic segmentation models, using backbone pretrained with Depth Anything to improve UperNet model and SETRMLA model, and adding language guidance based on both weather and category information to InternImage model. Secondly, we introduce a new dataset WeatherProofExtra with wider viewing angle and employ data augmentation methods, including adverse weather and super-resolution. Finally, effective training strategies and ensemble method are applied to improve final performance further. Our solution is ranked 1st on the final leaderboard. Code will be available at https://github.com/KaneiGi/WeatherProofChallenge.

Related papers

• PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training [90.06520673092702]
  We present PointRegGPT, boosting 3D point cloud registration using generative point-cloud pairs for training. To our knowledge, this is the first generative approach that explores realistic data generation for indoor point cloud registration.
  arXiv  Detail & Related papers  (2024-07-19T06:29:57Z)
• Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation [9.322345758563886]
  We present our solution for the semantic segmentation in adverse weather, in UG2+ Challenge at CVPR 2024. We initialize the InternImage-H backbone with pre-trained weights from the large-scale joint dataset and enhance it with the state-of-the-art Upernet segmentation method. Our proposed solution demonstrates advanced performance on the test set and achieves 3rd position in this challenge.
  arXiv  Detail & Related papers  (2024-06-09T15:56:35Z)
• A Two-Stage Adverse Weather Semantic Segmentation Method for WeatherProof Challenge CVPR 2024 Workshop UG2+ [10.069192320623031]
  We propose a two-stage deep learning framework for the WeatherProof dataset challenge. In the challenge, our solution achieved a competitive score of 0.43 on the Mean Intersection over Union (mIoU) metric, securing a respectable rank of 4th.
  arXiv  Detail & Related papers  (2024-06-08T16:22:26Z)
• WeatherProof: A Paired-Dataset Approach to Semantic Segmentation in Adverse Weather [9.619700283574533]
  We introduce a general paired-training method that leads to improved performance on images in adverse weather conditions. We create the first semantic segmentation dataset with accurate clear and adverse weather image pairs. We find that training on these paired clear and adverse weather frames which share an underlying scene results in improved performance on adverse weather data.
  arXiv  Detail & Related papers  (2023-12-15T04:57:54Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• 1st Place Solution of The Robust Vision Challenge (RVC) 2022 Semantic Segmentation Track [67.56316745239629]
  This report describes the winning solution to the semantic segmentation task of the Robust Vision Challenge on ECCV 2022. Our method adopts the FAN-B-Hybrid model as the encoder and uses Segformer as the segmentation framework. The proposed method could serve as a strong baseline for the multi-domain segmentation task and benefit future works.
  arXiv  Detail & Related papers  (2022-10-23T20:52:22Z)
• Stratified Transformer for 3D Point Cloud Segmentation [89.9698499437732]
  Stratified Transformer is able to capture long-range contexts and demonstrates strong generalization ability and high performance. To combat the challenges posed by irregular point arrangements, we propose first-layer point embedding to aggregate local information. Experiments demonstrate the effectiveness and superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets.
  arXiv  Detail & Related papers  (2022-03-28T05:35:16Z)
• LID 2020: The Learning from Imperfect Data Challenge Results [242.86700551532272]
  Learning from Imperfect Data workshop aims to inspire and facilitate the research in developing novel approaches. We organize three challenges to find the state-of-the-art approaches in weakly supervised learning setting. This technical report summarizes the highlights from the challenge.
  arXiv  Detail & Related papers  (2020-10-17T13:06:12Z)
• Unsupervised Point Cloud Pre-Training via Occlusion Completion [18.42664414305454]
  We describe a simple pre-training approach for point clouds. It works in three steps: Mask all points occluded in a camera view; 2. Learn an encoder-decoder model to reconstruct the occluded points; 3. Use the encoder weights as initialisation for downstream point cloud tasks.
  arXiv  Detail & Related papers  (2020-10-02T16:43:14Z)
• 1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation [116.25081559037872]
  This article introduces the solutions of the two champion teams, MMfruit' for the detection track and MMfruitSeg' for the segmentation track, in OpenImage Challenge 2019. It is commonly known that for an object detector, the shared feature at the end of the backbone is not appropriate for both classification and regression. We propose the Decoupling Head (DH) to disentangle the object classification and regression via the self-learned optimal feature extraction.
  arXiv  Detail & Related papers  (2020-03-17T06:45:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.