VITA: Video Instance Segmentation via Object Token Association (NeurIPS 2022)

Miran Heo^*, Sukjun Hwang^*, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim (*equal contribution)

[arXiv] [BibTeX]

Updates

Jan 20, 2023: Our new online VIS method "GenVIS" is available at here!
Sep 14, 2022: VITA is accepted to NeurIPS 2022!
Aug 15, 2022: Code and pretrained weights are now available! Thanks for your patience :)

Installation

See installation instructions.

Getting Started

We provide a script train_net_vita.py, that is made to train all the configs provided in VITA.

To train a model with "train_net_vita.py" on VIS, first setup the corresponding datasets following Preparing Datasets for VITA.

Then run with COCO pretrained weights in the Model Zoo:

python train_net_vita.py --num-gpus 8 \
  --config-file configs/youtubevis_2019/vita_R50_bs8.yaml \
  MODEL.WEIGHTS vita_r50_coco.pth

To evaluate a model's performance, use

python train_net_vita.py \
  --config-file configs/youtubevis_2019/vita_R50_bs8.yaml \
  --eval-only MODEL.WEIGHTS /path/to/checkpoint_file

Model Zoo

Pretrained weights on COCO

Name	R-50	R-101	Swin-L
VITA	model	model	model

YouTubeVIS-2019

Name	Backbone	AP	AP50	AP75	AR1	AR10	Download
VITA	R-50	49.8	72.6	54.5	49.4	61.0	model
VITA	Swin-L	63.0	86.9	67.9	56.3	68.1	model

YouTubeVIS-2021

Name	Backbone	AP	AP50	AP75	AR1	AR10	Download
VITA	R-50	45.7	67.4	49.5	40.9	53.6	model
VITA	Swin-L	57.5	80.6	61.0	47.7	62.6	model

OVIS

Name	Backbone	AP	AP50	AP75	AR1	AR10	Download
VITA	R-50	19.6	41.2	17.4	11.7	26.0	model
VITA	Swin-L	27.7	51.9	24.9	14.9	33.0	model

License

The majority of VITA is licensed under a Apache-2.0 License. However portions of the project are available under separate license terms: Detectron2(Apache-2.0 License), IFC(Apache-2.0 License), Mask2Former(MIT License), and Deformable-DETR(Apache-2.0 License).

Citing VITA

If you use VITA in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.

@inproceedings{VITA,
  title={VITA: Video Instance Segmentation via Object Token Association},
  author={Heo, Miran and Hwang, Sukjun and Oh, Seoung Wug and Lee, Joon-Young and Kim, Seon Joo},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022}
}

Acknowledgement

Our code is largely based on Detectron2, IFC, Mask2Former, and Deformable DETR. We are truly grateful for their excellent work.

sukjunhwang/VITA