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Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation

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[ECCV 2024] Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation

Zhenliang Ni, Xinghao Chen, Yingjie Zhai, Yehui Tang, and Yunhe Wang

🔥 Updates

  • 2024/07/01: The paper of CGRSeg is accepted by ECCV 2024.
  • 2024/05/10: Codes of CGRSeg are released in Pytorch and paper in [arXiv].

📸 Overview

cgrseg2

The overall architecture of CGRSeg. The Rectangular Self-Calibration Module (RCM) is designed for spatial feature reconstruction and pyramid context extraction. The rectangular self-calibration attention (RCA) explicitly models the rectangular region and calibrates the attention shape. The Dynamic Prototype Guided (DPG) head is proposed to improve the classification of the foreground objects via explicit class embedding.

flops

1️⃣ Results

ADE20K

ade20k

COCO-Stuff-10k

coco

Pascal Context

pc

2️⃣ Requirements

  • conda create --name ssa python=3.8 -y
    conda activate ssa
    pip install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchaudio==0.8.2
    pip install timm==0.6.13
    pip install mmcv-full==1.6.1
    pip install opencv-python==4.1.2.30
    pip install "mmsegmentation==0.27.0"

    CGRSeg is built based on mmsegmentation-0.27.0, which can be referenced for data preparation.

3️⃣ Training & Testing

  • Train

    # Single-gpu training
    python train.py local_configs/cgrseg/cgrseg-t_ade20k_160k.py
    
    # Multi-gpu (4-gpu) training
    bash dist_train.sh local_configs/cgrseg/cgrseg-t_ade20k_160k.py 4
  • Test

    # Single-gpu testing
    python test.py local_configs/cgrseg/cgrseg-t_ade20k_160k.py ${CHECKPOINT_FILE} --eval mIoU
    
    # Multi-gpu (4-gpu) testing
    bash dist_test.sh local_configs/cgrseg/cgrseg-t_ade20k_160k.py ${CHECKPOINT_FILE} 4 --eval mIoU

✏️ Reference

If you find CGRSeg useful in your research or applications, please consider giving a star ⭐ and citing using the following BibTeX:

@article{ni2024context,
  title={Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation},
  author={Ni, Zhenliang and Chen, Xinghao and Zhai, Yingjie and Tang, Yehui and Wang, Yunhe},
  journal={arXiv preprint arXiv:2405.06228},
  year={2024}
}

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