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Weakly Supervised Semantic Segmentation using Out-of-Distribution Data (CVPR'22)

by Jungbeom Lee¹, Seong Joon Oh^2,3, Sangdoo Yun², Junsuk Choe⁴, Eunji Kim¹, Sungroh Yoon¹

¹ Seoul National University, ² NAVER AI Lab, ³ University of Tübingen, ⁴ Sogang University

Abstract

Weakly supervised semantic segmentation (WSSS) methods are often built on pixel-level localization maps obtained from a classifier. However, training on class labels only, classifiers suffer from the spurious correlation between foreground and background cues (e.g. train and rail), fundamentally bounding the performance of WSSS. There have been previous endeavors to address this issue with additional supervision. We propose a novel source of information to distinguish foreground from the background: Out-of-Distribution (OoD) data, or images devoid of foreground object classes. In particular, we utilize the hard OoDs that the classifier is likely to make false-positive predictions. These samples typically carry key visual features on the background (e.g. rail) that the classifiers often confuse as foreground (e.g. train), so these cues let classifiers correctly suppress spurious background cues. Acquiring such hard OoDs does not require an extensive amount of annotation efforts; it only incurs a few additional image-level labeling costs on top of the original efforts to collect class labels.We propose a method, W-OoD, for utilizing the hard OoDs. W-OoD achieves state-of-the-art performance on Pascal VOC 2012.

Installation

• We refer to the offical implementation of IRN.

Usage

Step 1. Prepare Dataset

• Download PASCAL VOC 2012 benchmark: Download.
• Unzip and move it to "Data/"
• Download OoD images: Download
• Unzip and move it to "WOoD_dataset/openimages"
• Download a list of OoD images here and move 'ood_list.txt' to "WOoD_dataset/openimages"

Step 2. Prepare pre-trained in-distribution classifier

• Pre-trained model used in this paper: Download.
• Move it to "sess/"

Step 3. Obtain the pseudo ground-truth masks for PASCAL VOC train_aug images and evaluate them

bash bash_run_sample.sh

Step 4. Train a semantic segmentation network

• To train DeepLab-v2, we refer to deeplab-pytorch. However, this repo contains only COCO pre-trained model. We use ImageNet pre-trained model, provided by AdvCAM, for a fair comparison with the other methods.

Citation

If you find the code useful, please consider citing our paper using the following BibTeX entry.

@inproceedings{lee2022weakly,
  title={Weakly Supervised Semantic Segmentation using Out-of-Distribution Data},
  author={Lee, Jungbeom and Oh, Seong Joon and Yun, Sangdoo and Choe, Junsuk and Kim, Eunji and Yoon, Sungroh},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16897--16906},
  year={2022}
}

Acknowledgment

• This work is a result of a joint project of NAVER and SNU.
• This code is heavily borrowed from IRN, and AdvCAM.

License

MIT license

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