# pebal

**Repository Path**: shenghsin/pebal

## Basic Information

- **Project Name**: pebal
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-22
- **Last Updated**: 2024-02-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PEBAL
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/pixel-wise-energy-biased-abstention-learning/anomaly-detection-on-fishyscapes-1)](https://paperswithcode.com/sota/anomaly-detection-on-fishyscapes-1?p=pixel-wise-energy-biased-abstention-learning)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/pixel-wise-energy-biased-abstention-learning/anomaly-detection-on-lost-and-found)](https://paperswithcode.com/sota/anomaly-detection-on-lost-and-found?p=pixel-wise-energy-biased-abstention-learning)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/pixel-wise-energy-biased-abstention-learning/anomaly-detection-on-road-anomaly)](https://paperswithcode.com/sota/anomaly-detection-on-road-anomaly?p=pixel-wise-energy-biased-abstention-learning)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/pixel-wise-energy-biased-abstention-learning/anomaly-detection-on-fishyscapes-l-f)](https://paperswithcode.com/sota/anomaly-detection-on-fishyscapes-l-f?p=pixel-wise-energy-biased-abstention-learning)

> [**ECCV'22 Oral**] [**Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes**](https://arxiv.org/pdf/2111.12264.pdf)
>
> by [Yu Tian*](https://yutianyt.com/), Yuyuan Liu*, [Guansong Pang](https://sites.google.com/site/gspangsite/home?authuser=0), [Fengbei Liu](https://fbladl.github.io/), Yuanhong Chen, [Gustavo Carneiro](https://cs.adelaide.edu.au/~carneiro/).
>

<img width="933" alt="Screen Shot 2022-06-11 at 2 56 11 pm" src="https://user-images.githubusercontent.com/102338056/173174121-f515ce6d-a865-4dcd-aa00-4b0e4fd5d448.png">
<!-- ![image](https://user-images.githubusercontent.com/19222962/161691512-61a2dfa8-2079-465c-abaa-5b8fdf42e5f7.png) -->

## Update

* :sparkles:  **[Results](https://segmentmeifyoucan.com/leaderboard)** on **Segment-Me-if-You-Can** has been released!
* :beers: Our newest work **[RPL](https://github.com/yyliu01/RPL)** for anomaly segmentation has been accepted in **ICCV'23**!


## Installation

Please install the dependencies and dataset based on this [***installation***](./docs/installation.md) document.

## Getting started

Please follow this [***instruction***](./docs/before_start.md) document to reproduce our results.

## Acknowledgement & Citation

The code is partially borrowed from [CPS](https://github.com/charlesCXK/TorchSemiSeg). Many thanks for their great work.

If you find this repo useful for your research, please consider citing our paper:

```bibtex
@misc{tian2021pixelwise,
      title={Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes}, 
      author={Yu Tian and Yuyuan Liu and Guansong Pang and Fengbei Liu and Yuanhong Chen and Gustavo Carneiro},
      year={2021},
      eprint={2111.12264},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

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