liuguoyou/weakalign

End-to-end weakly-supervised semantic alignment

End-to-end weakly-supervised semantic alignment

About

This is the implementation of the paper "End-to-end weakly-supervised semantic alignment" by I. Rocco, R. Arandjelović and J. Sivic.

For more information check out the project [website] and the paper on [arXiv].

Getting started

Dependencies

The code is implemented using Python 3 and PyTorch 0.2. All dependencies are included in the standard Anaconda distribution.

Training

The code includes scripts for pre-training the models with strong supervision (train_strong.py) as proposed in our previous work, as well as to fine-tune the model using weak supervision (train_weak.py) as proposed in this work.

Training scripts can be found in the scripts/ folder.

Evaluation

Evaluation is implemented in the eval.py file. It can evaluate a single affine or TPS model (with the --model-aff and --model-tps parameters respectively), or a combined affine+TPS model (with the --model) parameter.

The evaluation dataset is passed with the --eval-dataset parameter.

Trained models

Trained models for the baseline method using only strong supervision and the proposed method using additional weak supervision are provided below. You can store them in the trained_models/ folder.

With the provided code below you should obtain the results from Table 2 of the paper.

CNNGeometric with VGG-16 baseline: [affine model],[TPS model]

python eval.py --feature-extraction-cnn vgg --model-aff trained_models/cnngeo_vgg16_affine.pth.tar --model-tps trained_models/cnngeo_vgg16_tps.pth.tar --eval-dataset pf-pascal

CNNGeometric with ResNet-101 baseline: [affine model],[TPS model]

python eval.py --feature-extraction-cnn resnet101 --model-aff trained_models/cnngeo_resnet101_affine.pth.tar --model-tps trained_models/cnngeo_resnet101_tps.pth.tar --eval-dataset pf-pascal

Proposed method: [combined aff+TPS model]

python eval.py --feature-extraction-cnn resnet101 --model trained_models/weakalign_resnet101_affine_tps.pth.tar --eval-dataset pf-pascal

BibTeX

If you use this code in your project, please cite our paper:

@article{Rocco18,
        author       = "Rocco, I. and Arandjelovi\'c, R. and Sivic, J.",
        title        = "End-to-end weakly-supervised semantic alignment",
        journal={arXiv preprint arXiv:1712.06861},
         }