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Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation (SIPE)

<img style="zoom:100%" alt="framework" src='fig/framework1.png'>

The implementation of Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation, Qi Chen, Lingxiao Yang, Jianhuang Lai, and Xiaohua Xie, CVPR 2022.

Abstract

Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has attracted much attention due to low annotation costs. Existing methods often rely on Class Activation Mapping (CAM) that measures the correlation between image pixels and classifier weight. However, the classifier focuses only on the discriminative regions while ignoring other useful information in each image, resulting in incomplete localization maps. To address this issue, we propose a Self-supervised Image-specific Prototype Exploration (SIPE) that consists of an Image-specific Prototype Exploration (IPE) and a General-Specific Consistency (GSC) loss. Specifically, IPE tailors prototypes for every image to capture complete regions, formed our Image-Specific CAM (IS-CAM), which is realized by two sequential steps. In addition, GSC is proposed to construct the consistency of general CAM and our specific IS-CAM, which further optimizes the feature representation and empowers a self-correction ability of prototype exploration. Extensive experiments are conducted on PASCAL VOC 2012 and MS COCO 2014 segmentation benchmark and results show our SIPE achieves new state-of-the-art performance using only image-level labels.

Environment

Usage

Step 1. Prepare Dataset

Step 2. Train SIPE

# PASCAL VOC 2012
bash run_voc.sh

# MS COCO 2014
bash run_coco.sh

Step 3. Train Fully Supervised Segmentation Models

To train fully supervised segmentation models, we refer to deeplab-pytorch and seamv1.

Results

Localization maps

DatasetModelmIoU (Train)WeightTraining log
PASCAL VOC 2012CVPR submit58.65DownloadLogfile
PASCAL VOC 2012This repo58.88DownloadLogfile
MS COCO 2014CVPR submit34.41DownloadLogfile
MS COCO 2014This repo35.05DownloadLogfile

Segmentation maps

DatasetModelmIoU (Val)mIoU (Test)Weight
PASCAL VOC 2012WideResNet3868.269.5Download
PASCAL VOC 2012ResNet10168.869.7Download
MS COCO 2014WideResNet3843.6-Download
MS COCO 2014ResNet10140.6-Download

Citation

@InProceedings{Chen_2022_CVPR_SIPE,
    author    = {Chen, Qi and Yang, Lingxiao and Lai, Jian-Huang and Xie, Xiaohua},
    title     = {Self-Supervised Image-Specific Prototype Exploration for Weakly Supervised Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {4288-4298}
}