CCAM (Unsupervised)

Code repository for our paper "CCAM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation" in CVPR 2022.

The repository includes full training, evaluation, and visualization codes on CUB-200-2011, ILSVRC2012, and PASCAL VOC2012 datasets.

We provide the extracted class-agnostic bounding boxes (on CUB-200-2011 and ILSVRC2012) and background cues (on PASCAL VOC12) at here.

Dependencies

• Python 3
• PyTorch 1.7.1
• OpenCV-Python
• Numpy
• Scipy
• MatplotLib
• Yaml
• Easydict

Dataset

CUB-200-2011

You will need to download the images (JPEG format) in CUB-200-2011 dataset at here. Make sure your data/CUB_200_2011 folder is structured as follows:

├── CUB_200_2011/
|   ├── images
|   ├── images.txt
|   ├── bounding_boxes.txt
|   ...
|   └── train_test_split.txt

You will need to download the images (JPEG format) in ILSVRC2012 dataset at here. Make sure your data/ILSVRC2012 folder is structured as follows:

ILSVRC2012

├── ILSVRC2012/ 
|   ├── train
|   ├── val
|   ├── val_boxes
|   |   ├——val
|   |   |   ├—— ILSVRC2012_val_00050000.xml
|   |   |   ├—— ...
|   ├── train.txt
|   └── val.txt

PASCAL VOC2012

You will need to download the images (JPEG format) in PASCAL VOC2012 dataset at here. Make sure your data/VOC2012 folder is structured as follows:

├── VOC2012/
|   ├── Annotations
|   ├── ImageSets
|   ├── SegmentationClass
|   ├── SegmentationClassAug
|   └── SegmentationObject

For WSOL task

please refer to the directory of './WSOL'

cd WSOL

For WSSS task

please refer to the directory of './WSSS'

cd WSSS

Comparison with CAM

CUSTOM DATASET

As CCAM is an unsupervised method, it can be applied to various scenarios, like ReID, Saliency detection, or skin lesion detection. We provide an example to apply CCAM on your custom dataset like 'Market-1501'.

cd CUSTOM

Reference

If you are using our code, please consider citing our paper.

@article{xie2022contrastive,
  title={Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation},
  author={Xie, Jinheng and Xiang, Jianfeng and Chen, Junliang and Hou, Xianxu and Zhao, Xiaodong and Shen, Linlin},
  journal={arXiv preprint arXiv:2203.13505},
  year={2022}
}