[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
Multi-resolution SeqMatch based long-term Place Recognition

MRS-SLAM for long-term place recognition In this work, we imply an multi-resolution sambling based visual place recognition method. This work is based

METASLAM 6 Dec 06, 2022
Toward Spatially Unbiased Generative Models (ICCV 2021)

Toward Spatially Unbiased Generative Models Implementation of Toward Spatially Unbiased Generative Models (ICCV 2021) Overview Recent image generation

Jooyoung Choi 88 Dec 01, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022
StarGAN - Official PyTorch Implementation (CVPR 2018)

StarGAN - Official PyTorch Implementation ***** New: StarGAN v2 is available at https://github.com/clovaai/stargan-v2 ***** This repository provides t

Yunjey Choi 5.1k Jan 04, 2023
Unofficial TensorFlow implementation of the Keyword Spotting Transformer model

Keyword Spotting Transformer This is the unofficial TensorFlow implementation of the Keyword Spotting Transformer model. This model is used to train o

Intelligent Machines Limited 8 May 11, 2022
A python implementation of Yolov5 to detect fire or smoke in the wild in Jetson Xavier nx and Jetson nano

yolov5-fire-smoke-detect-python A python implementation of Yolov5 to detect fire or smoke in the wild in Jetson Xavier nx and Jetson nano You can see

20 Dec 15, 2022
Provide partial dates and retain the date precision through processing

Prefix date parser This is a helper class to parse dates with varied degrees of precision. For example, a data source might state a date as 2001, 2001

Friedrich Lindenberg 13 Dec 14, 2022
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis

iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis Andreas Bl

CompVis Heidelberg 36 Dec 25, 2022
Image Super-Resolution by Neural Texture Transfer

SRNTT: Image Super-Resolution by Neural Texture Transfer Tensorflow implementation of the paper Image Super-Resolution by Neural Texture Transfer acce

Zhifei Zhang 413 Nov 30, 2022
View model summaries in PyTorch!

torchinfo (formerly torch-summary) Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensor

Tyler Yep 1.5k Jan 05, 2023
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

Holy Wu 35 Jan 01, 2023
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022
Simple Baselines for Human Pose Estimation and Tracking

Simple Baselines for Human Pose Estimation and Tracking News Our new work High-Resolution Representations for Labeling Pixels and Regions is available

Microsoft 2.7k Jan 05, 2023
Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism

Period-alternatives-of-Softmax Experimental Demo for our paper 'Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechani

slwang9353 0 Sep 06, 2021
University of Rochester 2021 Summer REU focusing on music sentiment transfer using CycleGAN

Music-Sentiment-Transfer University of Rochester 2021 Summer REU focusing on music sentiment transfer using CycleGAN Poster: Music Sentiment Transfer

Miles Sigel 2 Jan 24, 2022
FastyAPI is a Stack boilerplate optimised for heavy loads.

FastyAPI A FastAPI based Stack boilerplate for heavy loads. Explore the docs » View Demo · Report Bug · Request Feature Table of Contents About The Pr

Ali Chaayb 47 Dec 27, 2022
Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [CVPR 2021]

Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [CVPR 2021] Abstract Analyzing complex scenes with DNN is a challenging ta

Irene Yuan 24 Jun 27, 2022
This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)"

Gait3D-Benchmark This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild

82 Jan 04, 2023
《K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters》(2020)

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters This repository is the implementation of the paper "K-Adapter: Infusing Knowledge

Microsoft 118 Dec 13, 2022
Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

M-LSD: Towards Light-weight and Real-time Line Segment Detection Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Det

123 Jan 04, 2023