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Masking Improves Contrastive Self-Supervised Learning for ConvNets and Saliency Tells You Where

License: MIT arXiv

Code for the paper "Masking Improves Contrastive Self-Supervised Learning for ConvNets and Saliency Tells You Where"

Paper: https://arxiv.org/abs/2309.12757
Authors: Zhi-Yi Chin $^{\dagger*}$, Chieh-Ming Jiang $^{\dagger*}$, Ching-Chun Huang $^\dagger$, Pin-Yu Chen $^\ddagger$, Wei-Chen Chiu $^\dagger$ (*equal contribution)
$^\dagger$ National Yang Ming Chiao Tung University, $\ddagger$ IBM Research

To be published in WACV 2024

Overview

Our paper addresses the challenge of incorporating masking operations into contrastive-learning for ConvNets to improve self-supervised learning. We've observed that random masking in self-supervised learning can disproportionately emphasize salient objects in one view, leading to misrepresentative contrastive relationships; therefore, we introduce an approach that rectifies this bias by promoting a balanced distribution of masked regions while considering saliency. We also introduce hard negative samples by masking larger regions of salient patches. Our experiments demonstrate superior performance compared to existing methods across various datasets and downstream tasks.

Setup

conda env create --file environment.yml -n mask
conda activate mask

Data Preparation

Download ImageNet

Download link: https://image-net.org/challenges/LSVRC/2012/2012-downloads.php

  1. Signup and login
  2. wget {data link address}
  3. tar -xvf {XXX.tar} -C {XXX}
  4. Decompress tar files: python decompresses_tar_dir.py -d {XXX}
  5. Make validation images have the same folder structure as training images: python val_split.py --root [IMAGENET_ROOT] --val-img-dir {ORIGINAL_VAL_DIR} --val-annot-dir {VAL_ANNOT_DIR} --train-img-dir {TRAIN_IMG_DIR} --save-dir {PROCESSED_VAL_DIR}

Make ImageNet-100

Modify imagenet path data path and imagenet100 saving path inside imagenet100.sh

bash imagenet100.sh

Experiments

SSL pre-train the encoder

CUDA_VISIBLE_DEVICES=0,1,2,3 python main_moco.py \
    -a resnet50 
    --lr 0.015 --batch-size 128 --epochs 200 \
    --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 \
    --rank 0 --mlp --moco-t 0.2 --aug-plus --cos \
    --save-dir {RES_AND_LOG_SAVE_PATH} \
    --alpha 2.0 --num-nonsem 1 \
    --strategy hp \
    {DATA_PATH}
  • If you are using a 4 GPU machine set --lr 0.015 --batch-size 128, 8 GPU machine set --lr 0.03 --batch-size 256
  • --alpha: hard negative weight
  • --num-nonsem: how many hard negative views per qk pair
  • --strategy: choose 1 masking stratgey setting from: hp, blur, and mean

Fine-tune linear classifier

CUDA_VISIBLE_DEVICES=0,1,2,3 python main_lincls.py \
  -a resnet50 \
  --lr 30.0 \
  --batch-size 256 \
  --pretrained {ENCODER_CKPT} \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  --save-dir {RES_SAVE_PATH} \
  --n-classes 100 \
  --strategy hp \
  {DATA_PATH}

Paper Results

Classification (Linear Evaluation)

Detection and Segmentation

Reference Repositories

Citation

DOI

Please cite our paper if it's helpful to your work!

@inproceedings{chin2023masking,
  title={Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where},
  author={Zhi-Yi Chin and Chieh-Ming Jiang and Ching-Chun Huang and Pin-Yu Chen and Wei-Chen Chiu},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}
  year={2024},
}