[NeurIPS 2021] SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning

Related tags

Deep LearningSSUL
Overview

SSUL - Official Pytorch Implementation (NeurIPS 2021)

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
Sungmin Cha1,2*, Beomyoung Kim3*, YoungJoon Yoo2,3, Taesup Moon1
* Equal contribution

1 Department of Electrical and Computer Engineering, Seoul National University
2 NAVER AI Lab
3 Face, NAVER Clova

NeurIPS 2021

Paper

Abtract

This paper introduces a solid state-of-the-art baseline for a class-incremental semantic segmentation (CISS) problem. While the recent CISS algorithms utilize variants of the knowledge distillation (KD) technique to tackle the problem, they failed to fully address the critical challenges in CISS causing the catastrophic forgetting; the semantic drift of the background class and the multi-label prediction issue. To better address these challenges, we propose a new method, dubbed SSUL-M (Semantic Segmentation with Unknown Label with Memory), by carefully combining techniques tailored for semantic segmentation. Specifically, we claim three main contributions. (1) defining unknown classes within the background class to help to learn future classes (help plasticity), (2) freezing backbone network and past classifiers with binary cross-entropy loss and pseudo-labeling to overcome catastrophic forgetting (help stability), and (3) utilizing tiny exemplar memory for the first time in CISS to improve both plasticity and stability. The extensively conducted experiments show the effectiveness of our method, achieving significantly better performance than the recent state-of-the-art baselines on the standard benchmark datasets. Furthermore, we justify our contributions with thorough ablation analyses and discuss different natures of the CISS problem compared to the traditional class-incremental learning targeting classification.

Experimental Results (mIoU all)

Method VOC 10-1 (11 tasks) VOC 15-1 (6 tasks) VOC 5-3 (6 tasks) VOC 19-1 (2 tasks) VOC 15-5 (2 tasks) VOC 5-1 (16 tasks) VOC 2-1 (19 tasks)
MiB 12.65 29.29 46.71 69.15 70.08 10.03 9.88
PLOP 30.45 54.64 18.68 73.54 70.09 6.46 4.47
SSUL 59.25 67.61 56.89 75.44 71.22 48.65 38.32
SSUL-M 64.12 71.37 58.37 76.49 73.02 55.11 44.74
Method ADE 100-5 (11 tasks) ADE 100-10 (6 tasks) ADE 100-50 (2 tasks) ADE 50-50 (3 tasks)
MiB 25.96 29.24 32.79 29.31
PLOP 28.75 31.59 32.94 30.40
SSUL 32.48 33.10 33.58 29.56
SSUL-M 34.56 34.46 34.37 29.77

Getting Started

Requirements

  • torch>=1.7.1
  • torchvision>=0.8.2
  • numpy
  • pillow
  • scikit-learn
  • tqdm
  • matplotlib

Datasets

data_root/
    --- VOC2012/
        --- Annotations/
        --- ImageSet/
        --- JPEGImages/
        --- SegmentationClassAug/
        --- saliency_map/
    --- ADEChallengeData2016
        --- annotations
            --- training
            --- validation
        --- images
            --- training
            --- validation

Download SegmentationClassAug and saliency_map

Class-Incremental Segmentation Segmentation on VOC 2012

DATA_ROOT=your_dataset_root_path
DATASET=voc
TASK=15-1 # [15-1, 10-1, 19-1, 15-5, 5-3, 5-1, 2-1, 2-2]
EPOCH=50
BATCH=32
LOSS=bce_loss
LR=0.01
THRESH=0.7
MEMORY=100 # [0 (for SSUL), 100 (for SSUL-M)]

python main.py --data_root ${DATA_ROOT} --model deeplabv3_resnet101 --gpu_id 0,1 --crop_val --lr ${LR} --batch_size ${BATCH} --train_epoch ${EPOCH} --loss_type ${LOSS} --dataset ${DATASET} --task ${TASK} --overlap --lr_policy poly --pseudo --pseudo_thresh ${THRESH} --freeze --bn_freeze --unknown --w_transfer --amp --mem_size ${MEMORY}

Class-Incremental Segmentation Segmentation on ADE20K

DATA_ROOT=your_dataset_root_path
DATASET=ade
TASK=100-5 # [100-5, 100-10, 100-50, 50-50]
EPOCH=100
BATCH=24
LOSS=bce_loss
LR=0.05
THRESH=0.7
MEMORY=300 # [0 (for SSUL), 300 (for SSUL-M)]

python main.py --data_root ${DATA_ROOT} --model deeplabv3_resnet101 --gpu_id 0,1 --crop_val --lr ${LR} --batch_size ${BATCH} --train_epoch ${EPOCH} --loss_type ${LOSS} --dataset ${DATASET} --task ${TASK} --overlap --lr_policy warm_poly --pseudo --pseudo_thresh ${THRESH} --freeze --bn_freeze --unknown --w_transfer --amp --mem_size ${MEMORY}

Qualitative Results

Acknowledgement

Our implementation is based on these repositories: DeepLabV3Plus-Pytorch, Torchvision.

License

SSUL
Copyright 2021-present NAVER Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Owner
Clova AI Research
Open source repository of Clova AI Research, NAVER & LINE
Clova AI Research
Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch

Who has never met a forged picture on the web ? No one ! Everyday we are constantly facing fake pictures touched up in Photoshop but it is not always

Rony Abecidan 77 Dec 16, 2022
Official repository for MixFaceNets: Extremely Efficient Face Recognition Networks

MixFaceNets This is the official repository of the paper: MixFaceNets: Extremely Efficient Face Recognition Networks. (Accepted in IJCB2021) https://i

Fadi Boutros 51 Dec 13, 2022
Code for the USENIX 2017 paper: kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels Blazing fast x86-64 VM kernel fuzzing framework with performant VM reloads for Linux, MacOS an

Chair for Sys­tems Se­cu­ri­ty 541 Nov 27, 2022
An AI Assistant More Than a Toolkit

tymon An AI Assistant More Than a Toolkit The reason for creating framework tymon is simple. making AI more like an assistant, helping us to complete

TymonXie 46 Oct 24, 2022
This is a repository for a No-Code object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operating systems.

OpenVINO Inference API This is a repository for an object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operati

BMW TechOffice MUNICH 68 Nov 24, 2022
A Simple Framwork for CV Pre-training Model (SOCO, VirTex, BEiT)

A Simple Framwork for CV Pre-training Model (SOCO, VirTex, BEiT)

Sense-GVT 14 Jul 07, 2022
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.

SCOOD-UDG (ICCV 2021) This repository is the official implementation of the paper: Semantically Coherent Out-of-Distribution Detection Jingkang Yang,

Jake YANG 62 Nov 21, 2022
Neon-erc20-example - Example of creating SPL token and wrapping it with ERC20 interface in Neon EVM

Example of wrapping SPL token by ERC2-20 interface in Neon Requirements Install

7 Mar 28, 2022
Towards uncontrained hand-object reconstruction from RGB videos

Towards uncontrained hand-object reconstruction from RGB videos Yana Hasson, Gül Varol, Ivan Laptev and Cordelia Schmid Project page Paper Table of Co

Yana 69 Dec 27, 2022
Eff video representation - Efficient video representation through neural fields

Neural Residual Flow Fields for Efficient Video Representations 1. Download MPI

41 Jan 06, 2023
This game was designed to encourage young people not to gamble on lotteries, as the probablity of correctly guessing the number is infinitesimal!

Lottery Simulator 2022 for Web Launch Application Developed by John Seong in Ontario. This game was designed to encourage young people not to gamble o

John Seong 2 Sep 02, 2022
TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020)

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020) About The goal of our research problem is illustrated below: give

59 Dec 09, 2022
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization Official PyTorch implementation for our URST (Ultra-Resolution Sty

czczup 148 Dec 27, 2022
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Dec 28, 2022
Efficiently Disentangle Causal Representations

Efficiently Disentangle Causal Representations Install dependency pip install -r requirements.txt Main experiments Causality direction prediction cd

4 Apr 01, 2022
Deep functional residue identification

DeepFRI Deep functional residue identification Citing @article {Gligorijevic2019, author = {Gligorijevic, Vladimir and Renfrew, P. Douglas and Koscio

Flatiron Institute 156 Dec 25, 2022
Everything you need to know about NumPy( Creating Arrays, Indexing, Math,Statistics,Reshaping).

Everything you need to know about NumPy( Creating Arrays, Indexing, Math,Statistics,Reshaping).

1 Feb 14, 2022
The openspoor package is intended to allow easy transformation between different geographical and topological systems commonly used in Dutch Railway

Openspoor The openspoor package is intended to allow easy transformation between different geographical and topological systems commonly used in Dutch

7 Aug 22, 2022
Bayesian Optimization Library for Medical Image Segmentation.

bayesmedaug: Bayesian Optimization Library for Medical Image Segmentation. bayesmedaug optimizes your data augmentation hyperparameters for medical im

Şafak Bilici 7 Feb 10, 2022
ML From Scratch

ML from Scratch MACHINE LEARNING TOPICS COVERED - FROM SCRATCH Linear Regression Logistic Regression K Means Clustering K Nearest Neighbours Decision

Tanishq Gautam 66 Nov 02, 2022