Official PyTorch implementation of paper: Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation (ICCV 2021 Oral Presentation)

Overview

SML (ICCV 2021, Oral) : Official Pytorch Implementation

This repository provides the official PyTorch implementation of the following paper:

Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
Sanghun Jung* (KAIST AI), Jungsoo Lee* (KAIST AI), Daehoon Gwak (KAIST AI)
Sungha Choi (LG AI Research), and Jaegul Choo (KAIST AI) (*: equal contribution)
ICCV 2021 (Oral)

Paper: arxiv

Youtube Video (English): Youtube

Abstract: Identifying unexpected objects on roads in semantic segmentation (e.g., identifying dogs on roads) is crucial in safety-critical applications. Existing approaches use images of unexpected objects from external datasets or require additional training (e.g., retraining segmentation networks or training an extra network), which necessitate a non-trivial amount of labor intensity or lengthy inference time. One possible alternative is to use prediction scores of a pre-trained network such as the max logits (i.e., maximum values among classes before the final softmax layer) for detecting such objects. However, the distribution of max logits of each predicted class is significantly different from each other, which degrades the performance of identifying unexpected objects in urban-scene segmentation. To address this issue, we propose a simple yet effective approach that standardizes the max logits in order to align the different distributions and reflect the relative meanings of max logits within each predicted class. Moreover, we consider the local regions from two different perspectives based on the intuition that neighboring pixels share similar semantic information. In contrast to previous approaches, our method does not utilize any external datasets or require additional training, which makes our method widely applicable to existing pre-trained segmentation models. Such a straightforward approach achieves a new state-of-the-art performance on the publicly available Fishyscapes Lost & Found leaderboard with a large margin.

Code Contributors

Sanghun Jung [Website] [LinkedIn] [Google Scholar] (KAIST AI)
Jungsoo Lee [Website] [LinkedIn] [Google Scholar] (KAIST AI)

Concept Video

Click the figure to watch the youtube video of our paper!

Youtube Video

Pytorch Implementation

Installation

Clone this repository.

git clone https://github.com/shjung13/Standardized-max-logits.git
cd Standardized-max-logits
pip install -r requirements.txt

Cityscapes data directory

cityscapes
 └ leftImg8bit_trainvaltest
   └ leftImg8bit
     └ train
     └ val
     └ test
 └ gtFine_trainvaltest
   └ gtFine
     └ train
     └ val
     └ test

OoD data directory

Fishyscapes (OoD Dataset)
 └ leftImg8bit_trainvaltest
   └ leftImg8bit
     └ val
 └ gtFine_trainvaltest
   └ gtFine
     └ val

How to Run

Train the segmentation model

CUDA_VISIBLE_DEVICES=0,1 ./scripts/train_r101_os8.sh

Obtain statistics from training samples

CUDA_VISIBLE_DEVICES=0 ./scripts/calc_stat_r101_os8.sh

Evaluate on Out-of-Distribution dataset

Download the pretrained model here and after creating "<Directory Home>/pretrained", place it under the folder.

CUDA_VISIBLE_DEVICES=0 python eval.py --ood_dataset_path <path_to_OoD_dataset>

Quantitative / Qualitative Evaluation

Fishyscapes Learboard

Identified OoD pixels (colored white)

Fishyscapes Leaderboard

Our result is also available at fishyscapes.com.

Citation

@InProceedings{Jung_2021_ICCV,
    author    = {Jung, Sanghun and Lee, Jungsoo and Gwak, Daehoon and Choi, Sungha and Choo, Jaegul},
    title     = {Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {15425-15434}
}

Acknowledgments

We deeply appreciate Hermann Blum and FishyScapes team for their sincere help in providing the baseline performances and helping our team to update our model on the FishyScapes Leaderboard. Our pytorch implementation is heavily derived from NVIDIA segmentation and RobustNet. Thanks to the NVIDIA implementations.

Owner
SangHun
SangHun
MMFlow is an open source optical flow toolbox based on PyTorch

Documentation: https://mmflow.readthedocs.io/ Introduction English | 简体中文 MMFlow is an open source optical flow toolbox based on PyTorch. It is a part

OpenMMLab 688 Jan 06, 2023
bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

Code Submission for: Bio-inspired Min-Nets Improve the Performance and Robustness of Deep Networks Run with docker To build a docker environment, chan

0 Dec 09, 2021
GUI for a Vocal Remover that uses Deep Neural Networks.

GUI for a Vocal Remover that uses Deep Neural Networks.

4.4k Jan 07, 2023
Pytorch code for our paper "Feedback Network for Image Super-Resolution" (CVPR2019)

Feedback Network for Image Super-Resolution [arXiv] [CVF] [Poster] Update: Our proposed Gated Multiple Feedback Network (GMFN) will appear in BMVC2019

Zhen Li 539 Jan 06, 2023
基于DouZero定制AI实战欢乐斗地主

DouZero_For_Happy_DouDiZhu: 将DouZero用于欢乐斗地主实战 本项目基于DouZero 环境配置请移步项目DouZero 模型默认为WP,更换模型请修改start.py中的模型路径 运行main.py即可 SL (baselines/sl/): 基于人类数据进行深度学习

1.5k Jan 08, 2023
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (Paper) (Slides) (Video) RuleBERT is a pre-trained language model that has been fine-tune

16 Aug 24, 2022
Official repo for BMVC2021 paper ASFormer: Transformer for Action Segmentation

ASFormer: Transformer for Action Segmentation This repo provides training & inference code for BMVC 2021 paper: ASFormer: Transformer for Action Segme

42 Dec 23, 2022
Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation

Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation This reposi

First Person Vision @ Image Processing Laboratory - University of Catania 1 Aug 21, 2022
Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

Recursive-NeRF: An Efficient and Dynamically Growing NeRF This is a Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

33 Nov 30, 2022
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning This is the official PyTorch implementation for UniMoCo pape

dddzg 49 Jan 02, 2023
YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4. YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitraril

Adam Van Etten 161 Jan 06, 2023
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
Normalization Matters in Weakly Supervised Object Localization (ICCV 2021)

Normalization Matters in Weakly Supervised Object Localization (ICCV 2021) 99% of the code in this repository originates from this link. ICCV 2021 pap

Jeesoo Kim 10 Feb 01, 2022
Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems"

Code Artifacts Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driv

Andrea Stocco 2 Aug 24, 2022
Using VideoBERT to tackle video prediction

VideoBERT This repo reproduces the results of VideoBERT (https://arxiv.org/pdf/1904.01766.pdf). Inspiration was taken from https://github.com/MDSKUL/M

75 Dec 14, 2022
Torch-ngp - A pytorch implementation of the hash encoder proposed in instant-ngp

HashGrid Encoder (WIP) A pytorch implementation of the HashGrid Encoder from ins

hawkey 1k Jan 01, 2023
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation, NeurIPS 2021 Spotlight

PCAN for Multiple Object Tracking and Segmentation This is the offical implementation of paper PCAN for MOTS. We also present a trailer that consists

ETH VIS Group 328 Dec 29, 2022
Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent

Narya The Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository

Paul Garnier 121 Dec 30, 2022
text_recognition_toolbox: The reimplementation of a series of classical scene text recognition papers with Pytorch in a uniform way.

text recognition toolbox 1. 项目介绍 该项目是基于pytorch深度学习框架,以统一的改写方式实现了以下6篇经典的文字识别论文,论文的详情如下。该项目会持续进行更新,欢迎大家提出问题以及对代码进行贡献。 模型 论文标题 发表年份 模型方法划分 CRNN 《An End-t

168 Dec 24, 2022
Local-Global Stratified Transformer for Efficient Video Recognition

DualFormer This repo is the implementation of our manuscript entitled "Local-Global Stratified Transformer for Efficient Video Recognition". Our model

Sea AI Lab 19 Dec 07, 2022