RefCLIP

This is the official implementation of "RefCLIP: A Universal Teacher for Weakly Supervised Referring Expression Comprehension". In this paper,we propose a novel one-stage contrastive model called RefCLIP, which achieves weakly supervised REC via anchor-based cross-modal contrastive learning. Based on RefCLIP, we propose a weakly supervised training scheme for common REC models, that is, to train any REC model by means of pseudo-labels.

Installation

• Clone this repo

git clone https://github.com/AnonymousPaperID5299/RefCLIP.git
cd RefCLIP

• Create a conda virtual environment and activate it

conda create -n refclip python=3.7 -y
conda activate refclip

• Install Pytorch following the official installation instructions
• Install apex following the official installation guide
• Compile the DCN layer:

cd utils/DCN
./make.sh

pip install -r requirements.txt
wget https://github.com/explosion/spacy-models/releases/download/en_vectors_web_lg-2.1.0/en_vectors_web_lg-2.1.0.tar.gz -O en_vectors_web_lg-2.1.0.tar.gz
pip install en_vectors_web_lg-2.1.0.tar.gz

Data Preparation

• Download images and Generate annotations according to SimREC.

• Download the pretrained weights of YoloV3 from OneDrive.

• The project structure should look like the following:

| -- RefCLIP
     | -- data
        | -- anns
            | -- refcoco.json
            | -- refcoco+.json
            | -- refcocog.json
            | -- refclef.json
        | -- images
            | -- train2014
                | -- COCO_train2014_000000000072.jpg
                | -- ...
            | -- refclef
                | -- 25.jpg
                | -- ...
     | -- config
     | -- datasets
     | -- models
     | -- utils

• NOTE: our YoloV3 is trained on COCO’s training images, excluding those in RefCOCO, RefCOCO+, and RefCOCOg’s validation+testing.

RefCLIP

Training

python train.py --config ./config/[DATASET_NAME].yaml

Evaluation

python test.py --config ./config/[DATASET_NAME].yaml --eval-weights [PATH_TO_CHECKPOINT_FILE]

Weakly Supervised Training Scheme

• Use RefCLIP to generate pseudo-labels.
• Train model with pseudo-labels according to RealGIN, SimREC, TransVG and MattNet.

Model Zoo

RefCLIP

Method	RefCOCO			RefCOCO+			RefCOCOg	ReferItGame
	val	testA	testB	val	testA	testB	val-g	test
RefCLIP	60.36	58.58	57.13	40.39	40.45	38.86	47.87	39.58

Weakly Supervised Training Scheme

Method	RefCOCO			RefCOCO+			RefCOCOg	ReferItGame
	val	testA	testB	val	testA	testB	val-g	test
RefCLIP_RealGIN	59.43	58.49	57.36	37.08	38.70	35.82	46.10	37.56
RefCLIP_SimREC	62.57	62.70	61.22	39.13	40.81	36.59	45.68	42.33
RefCLIP_TransVG	64.08	63.67	63.93	39.32	39.54	36.29	45.70	42.64
RefCLIP_MattNet	69.31	67.23	71.27	43.01	44.80	41.09	51.31	-

Citation

@InProceedings{Jin_2023_CVPR,
    author    = {Jin, Lei and Luo, Gen and Zhou, Yiyi and Sun, Xiaoshuai and Jiang, Guannan and Shu, Annan and Ji, Rongrong},
    title     = {RefCLIP: A Universal Teacher for Weakly Supervised Referring Expression Comprehension},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {2681-2690}
}

Acknowledgement

Thanks a lot for the nicely organized code from the following repos

• SimREC