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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

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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
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The above copyright notice and this permission notice shall be included in
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.