This is the code for the paper Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition (TIP 2023).
Chao Liang, Zongxin Yang, Linchao Zhu, Yi Yang.
In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in large-scale annotations and hinders the applications of learning-based models. Although many deep learning based methods have been proposed for handling long-tailed multi-label recognition or label noise respectively, learning with noisy labels in long-tailed multi-label visual data has not been well-studied because of the complexity of long-tailed distribution entangled with multi-label correlation. To tackle such a critical yet thorny problem, this paper focuses on reducing noise based on some inherent properties of multi-label classification and long-tailed learning under noisy cases. In detail, we propose a Stitch-Up augmentation to synthesize a cleaner sample, which directly reduces multi-label noise by stitching up multiple noisy training samples. Equipped with Stitch-Up, a Heterogeneous Co-Learning framework is further designed to leverage the inconsistency between long-tailed and balanced distributions, yielding cleaner labels for more robust representation learning with noisy long-tailed data. To validate our method, we build two challenging benchmarks, named VOC-MLT-Noise and COCO-MLT-Noise, respectively. Extensive experiments are conducted to demonstrate the effectiveness of our proposed method. Compared to a variety of baselines, our method achieves superior results.
conda create -n myenv python=3.7
pip install -r requirements.txt
Download Pascal VOC 2012 and MS COCO 2017 and put them into ./data. Download noisy labels and tar -zxf appendix.tar.gz into ./appendix. The directory structure should be like the following:
appendix
├── coco
│ └── longtail2017
│ ├── class_freq_0.3noise.pkl
│ ├── class_freq_0.5noise.pkl
│ ├── class_freq_0.7noise.pkl
│ ├── class_freq_0.9noise.pkl
│ ├── class_split.pkl
│ ├── img_id.pkl
│ └── training_split.pkl
└── VOCdevkit
└── longtail2012
├── class_freq_0.3noise.pkl
├── class_freq_0.5noise.pkl
├── class_freq_0.7noise.pkl
├── class_freq_0.9noise.pkl
├── class_split.pkl
├── img_id.txt
└── training_split.pkl
python -m tools.train configs/voc/LT_dual_resnet50.py --random_head_alpha 0.7 --balance_head_alpha 0.7 --random_tail_beta 0.1 --balance_tail_beta 0.1 --work_dir work_dirs/voc_0.5
python -m tools.train configs/coco/LT_dual_resnet50.py --random_head_alpha 0.9 --balance_head_alpha 0.9 --work_dir work_dirs/coco_0.5
bash tools/dist_test.sh configs/voc/LT_dual_resnet50.py work_dirs/voc_0.5/epoch_8.pth
bash tools/dist_test.sh configs/coco/LT_dual_resnet50.py work_dirs/coco_0.5/epoch_8.pth
| Backbone | Total | Head | Medium | Tail | Download |
|---|---|---|---|---|---|
| ResNet-50 | 69.67 | 67.45 | 76.70 | 66.07 | model |
| Backbone | Total | Head | Medium | Tail | Download |
|---|---|---|---|---|---|
| ResNet-50 | 50.44 | 56.52 | 49.16 | 45.45 | model |
@article{chao2023stitchup,
author={Liang, Chao and Yang, Zongxin and Zhu, Linchao and Yang, Yi},
journal={IEEE Transactions on Image Processing},
title={Co-Learning Meets Stitch-Up for Noisy Multi-Label Visual Recognition},
year={2023},
volume={32},
number={},
pages={2508-2519},
doi={10.1109/TIP.2023.3270103}}
This project is under the CC-BY-NC 4.0 license. See LICENSE for details.