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[ECCV 2024] Random Walk on Pixel Manifolds for Anomaly Segmentation of Complex Driving Scenes (RWPM)

[ECCV'24] Random Walk on Pixel Manifolds for Anomaly Segmentation of Complex Driving Scenes

by Zelong Zeng*, Kaname Tomite

Update

05 Dec 2024

• We share the presentation 🎬video of our work RWPM.

17 July 2024

• We share the code of our work RWPM.

Notice

In this work, we only implemented RWPM on the RbA framework. If you want to reproduce our RWPM on other frameworks, please refer to lines 146 to 176 in RWPM/mask2former/modeling/meta_arch/mask_former_head.py and modify the corresponding parts for your application framework.

Installation

See installation instructions for necessary installations and setup

Datasets Preparation

See Dataset Preparation for details on downloading and preparing datasets for the evaluation.

Model Zoo and Baselines

We use the checkpoints files of RbA as the baselines in this project. Refer to the RbA Model Zoo for more information. All RbA based experiments in our paper are used the checkpoint named RbA + COCO Outlier Supervision in the RbA Model Zoo.

Evaluation

We provide evaluate_ood.py for evaluating on OoD datasets. A simple usage for the script is as follows:

python evaluate_ood.py 
  --model_mode selective \ # evaluates the selected models in the models_folder
  --selected_models swin_b_1dl_rba_ood_coco \ # the pre-trained model used for evaluation
  --models_folder ckpts/ \
  --datasets_folder PATH_TO_DATASETS_ROOT \
  --dataset_mode selective \ # evaluate on the selective datasets 
  --selected_datasets road_anomaly \ # the selective datasets 
  --RWPM 1 \ # the flag of RWPM, set 1 to use RWPM, set 0 to not use RWPM
  --CALIBRATION 1 \ # the flag of CALIBRATION, set 1 to use CALIBRATION, set 0 to not use CALIBRATION
  --alpha 0.99 \ # the transition probability of RWPM
  --TT 5 \ # the imeration number of limited iteration strategy
  --temperture 0.01 \ # the temperture of Softmax
  --n_patrion 2 # the partitioning parameter 

The script assumes the following:

• The OoD datasets are setup as described in Datasets Prepration
• The parameter --models_folder is a path to a folder that contains multiple folders, where each folder corresponds to a model. In a model's folder the scripts expects to files: 1) config.yaml and its checkpoint 2) model_final.pth. Setting up the models is explained in RbA Model Zoo Introduction

The scripts supports more finegrained options like selecting subsets of the models in a folder or the datasets. Please check evaluate_ood.py for descriptions of the options.

Acknowledgement

This code is adapted from RbA. Many thanks for their great work.

Citation

If you find this repository helpful for your research, please consider citing our paper:

@inproceedings{10.1007/978-3-031-72646-0_18,
author = {Zeng, Zelong and Tomite, Kaname},
title = {Random Walk on Pixel Manifolds for Anomaly Segmentation of Complex Driving Scenes},
year = {2024},
isbn = {978-3-031-72645-3},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-72646-0_18},
doi = {10.1007/978-3-031-72646-0_18},
booktitle = {Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part III},
pages = {306–323},
numpages = {18},
keywords = {Autonomous vehicles, Anomaly segmentation, Semantic segmentation},
location = {Milan, Italy}
}