The official implementation of the CVPR2022 paper:

Language as Queries for Referring
Video Object Segmentation

Language as Queries for Referring Video Object Segmentation

Jiannan Wu, Yi Jiang, Peize Sun, Zehuan Yuan, Ping Luo

Abstract

In this work, we propose a simple and unified framework built upon Transformer, termed ReferFormer. It views the language as queries and directly attends to the most relevant regions in the video frames. Concretely, we introduce a small set of object queries conditioned on the language as the input to the Transformer. In this manner, all the queries are obligated to find the referred objects only. They are eventually transformed into dynamic kernels which capture the crucial object-level information, and play the role of convolution filters to generate the segmentation masks from feature maps. The object tracking is achieved naturally by linking the corresponding queries across frames. This mechanism greatly simplifies the pipeline and the end-to-end framework is significantly different from the previous methods. Extensive experiments on Ref-Youtube-VOS, Ref-DAVIS17, A2D-Sentences and JHMDB-Sentences show the effectiveness of ReferFormer.

Update

(2022/12/19) We add the results on RefCOCO/+/g validation set.
(2022/07/31) We upload the files for joint-training.
(2022/04/04) We upload the data conversion and main files for pre-training.
(2022/03/11) We upload the model on Ref-Youtube-VOS by jointly training Ref-Youtube-VOS and Ref-COCO/+/g, which leads to higher performance.
(2022/03/03) ReferFormer is accepted by CVPR2022. 👏

Demo

Ref-DAVIS17

Ref-Youtube-VOS

Requirements

We test the codes in the following environments, other versions may also be compatible:

CUDA 11.1
Python 3.7
Pytorch 1.8.1

Installation

Please refer to install.md for installation.

Data Preparation

Please refer to data.md for data preparation.

We provide the pretrained model for different visual backbones. You may download them here and put them in the directory pretrained_weights.

After the organization, we expect the directory struture to be the following:

ReferFormer/
├── data/
│   ├── ref-youtube-vos/
│   ├── ref-davis/
│   ├── a2d_sentences/
│   ├── jhmdb_sentences/
├── davis2017/
├── datasets/
├── models/
├── scipts/
├── tools/
├── util/
├── pretrained_weights/
├── eval_davis.py
├── main.py
├── engine.py
├── inference_ytvos.py
├── inference_davis.py
├── opts.py
...

Model Zoo

All the models are trained using 8 NVIDIA Tesla V100 GPU. You may change the --backbone parameter to use different backbones (see here).

Note: If you encounter the OOM error, please add the command --use_checkpoint (we add this command for Swin-L, Video-Swin-S and Video-Swin-B models).

Ref-Youtube-VOS

To evaluate the results, please upload the zip file to the competition server.

Backbone	J&F	CFBI J&F	Pretrain	Model	Submission	CFBI Submission
ResNet-50	55.6	59.4	weight	model	link	link
ResNet-101	57.3	60.3	weight	model	link	link
Swin-T	58.7	61.2	weight	model	link	link
Swin-L	62.4	63.3	weight	model	link	link
Video-Swin-T*	56.0	-	-	model	link	-
Video-Swin-T	59.4	-	weight	model	link	-
Video-Swin-S	60.1	-	weight	model	link	-
Video-Swin-B	62.9	-	weight	model	link	-

* indicates the model is trained from scratch.

Joint training with Ref-COCO/+/g datasets.	Backbone	J&F	J	F	Model
ResNet-50	58.7	57.4	60.1	model	link
ResNet-101	59.3	58.1	60.4	model	link
Swin-L	64.2	62.3	66.2	model	link
Video-Swin-T	62.6	59.9	63.3	model	link
Video-Swin-S	63.3	61.4	65.2	model	link
Video-Swin-B	64.9	62.8	67.0	model	link

Ref-DAVIS17

As described in the paper, we report the results using the model trained on Ref-Youtube-VOS without finetune.

Backbone	J&F	J	F	Model
ResNet-50	58.5	55.8	61.3	model
Swin-L	60.5	57.6	63.4	model
Video-Swin-B	61.1	58.1	64.1	model

A2D-Sentences

The pretrained models are the same as those provided for Ref-Youtube-VOS.

Backbone	Overall IoU	Mean IoU	mAP	Pretrain	Model
Video-Swin-T*	72.3	64.1	48.6	-	model \| log
Video-Swin-T	77.6	69.6	52.8	weight	model \| log
Video-Swin-S	77.7	69.8	53.9	weight	model \| log
Video-Swin-B	78.6	70.3	55.0	weight	model \| log

* the model is trained from scratch and set --num_frames 6.

JHMDB-Sentences

As described in the paper, we report the results using the model trained on A2D-Sentences without finetune.

Backbone	Overall IoU	Mean IoU	mAP	Model
Video-Swin-T*	70.0	69.3	39.1	model
Video-Swin-T	71.9	71.0	42.2	model
Video-Swin-S	72.8	71.5	42.4	model
Video-Swin-B	73.0	71.8	43.7	model

* the model is trained from scratch and set --num_frames 6.

RefCOCO/+/g

We also support evaluate on RefCOCO/+/g validation set by using the pretrained weights (num_frames=1). Specifically, we measure the P@0.5 and overall IoU (oIoU) for REC and RIS tasks, respectively.

REC (referring epression understanding):

Backbone	RefCOCO	RefCOCO+	RefCOCOg	Model
ResNet-50	85.0	79.2	79.0	weight
ResNet-101	85.4	75.8	79.9	weight
Swin-T	86.7	77.2	80.6	weight
Swin-L	89.8	80.0	83.9	weight

RIS (referring image segmentation):

Backbone	RefCOCO	RefCOCO+	RefCOCOg	Model
ResNet-50	71.1	64.1	64.1	weight
ResNet-101	71.8	61.1	64.9	weight
Swin-T	72.9	62.4	66.1	weight
Swin-L	77.1	65.8	69.3	weight

Get Started

Please see Ref-Youtube-VOS, Ref-DAVIS17, A2D-Sentences and JHMDB-Sentences for details.

Acknowledgement

This repo is based on Deformable DETR and VisTR. We also refer to the repositories MDETR and MTTR. Thanks for their wonderful works.

Citation

@article{wu2022referformer,
      title={Language as Queries for Referring Video Object Segmentation}, 
      author={Jiannan Wu and Yi Jiang and Peize Sun and Zehuan Yuan and Ping Luo},
      journal={arXiv preprint arXiv:2201.00487},
      year={2022},
}

wjn922 / ReferFormer

readme