High-fidelity Pseudo-labels

This repository is the official implementation of the paper High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation, WACV, 2024 [1]. It contains the implementation of the binomial-based importance sampling loss (ISL) and feature similarity loss (FSL) for SEAM [2]. The losses are implemented in tool/probutils.py and used in train_cam_<voc/coco>.py.

Installation

Install with conda:

• Install miniconda
• conda create -n hfpl python=3.6
• conda activate hfpl
• pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html

Install on a singularity container:

• Install singularity
• singularity build --fakeroot pytorch.sif pytorch.def

Download and configure data:

• VOC: ./config_voc.sh
• COCO: ./config_coco.sh (requires make)

Download the ImageNet pretrained weights ilsvrc-cls_rna-a1_cls1000_ep-0001.params from here and put them in a new folder named pretrained.

Training and evaluation

Run training/inference/evaluation on all three stages CAM/AffinityNet/final (note: writes to ./exp/ and overwrites previous runs):

• VOC 2012: ./run_voc.sh
• COCO 2014: ./run_coco.sh

Note that the ImageNet pretrained model path needs to be set manually in lib/net/backbone/resnet38d.py, which is ./pretrained by default.

For training on multiple GPUs, update the GPUS field in the config file voc12/config_voc2012.py or coco/config_coco2014.py to match the number of available GPUs on your system.

Acknowledgements

This code was based on the following repositories:

• YudeWang/SEAM [2]
• YudeWang/semantic-segmentation-codebase [2]
• jiwoon-ahn/irn [3]
• davisvideochallenge/davis2017-evaluation [4]
• cocodataset/cocoapi

References

[1] Arvi Jonnarth, Yushan Zhang, and Michael Felsberg. High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation. WACV, 2024.

[2] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation. CVPR, 2020.

[3] Jiwoon Ahn and Suha Kwak. Learning Pixel-Level Semantic Affinity with Image-Level Supervision for Weakly Supervised Semantic Segmentation. CVPR, 2018.

[4] Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, and Alexander Sorkine-Hornung. A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation. CVPR, 2016.