Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation
This repository is the official implementation of CVPR 2021 paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.
Requirements
- Tensorflow-1-15
Training
To train the NCE model(s) in the paper, run this command:
python train_nce_distill_model.py \
--region_feat_path=region_features.hdf5 \
--phrase_feat_path=phrase_features.hdf5 \
--glove_path=glove.hdf5
To train the NCE+Distill model(s) in the paper, run this command:
python train_nce_distill_model.py \
--region_feat_path=region_features.hdf5 \
--phrase_feat_path=phrase_features.hdf5 \
--glove_path=glove.hdf5 \
--phrase_to_label_json=phrase_to_label.json
Evaluation
To evaluate the model on Flickr30K, run:
python eval_model.py \
--region_feat_path=region_features_test.hdf5 \
--phrase_feat_path=phrase_features_test.hdf5 \
--glove_path=glove.hdf5 \
--restore_path=checkpoint.meta
Pre-trained Models
You can download pretrained models using Res101 VG
features here:
You can also find the features on Flickr30K test split here.
The pretrained models achieve the following performance on Flickr30K test split:
Model Name | [email protected] | [email protected] | [email protected] |
---|---|---|---|
NCE+Distill | 0.5310 | 0.7394 | 0.7875 |
NCE | 0.5135 | 0.7338 | 0.7833 |
Citation
If you use our implementation in your research or wish to refer to the results published in our paper, please use the following BibTeX entry.
@InProceedings{Wang_2021_CVPR,
author = {Wang, Liwei and Huang, Jing and Li, Yin and Xu, Kun and Yang, Zhengyuan and Yu, Dong},
title = {Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {14090-14100}
}