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Two-shot Video Object Segmentation (CVPR2023)

For the first time, we demonstrate the feasibility of two-shot video object segmentation: two labeled frames per video are almost sufficient for training a decent VOS model.

In this work, we present a simple yet efficient training paradigm to exploit the wealth of information present in unlabeled frames, with only a small amount of labeled data (e.g. 7.3% for YouTube-VOS and 2.9% for DAVIS), our approach still achieves competitive results in contrast to the counterparts trained on full set (2-shot STCN equipped with our approach achieves 85.1%/82.7% on DAVIS 2017/YouTube-VOS 2019, which is -0.1%/-0.0% lower than the STCN trained on full set).

This work has been accepted by CVPR 2023.

Installation

This work follows STCN, please install the running environment and prepare datasets according to the corresponding instructions. Besides, we recommend the version of PyTorch >=1.8.

Phase-1

Phase-1 aims to train a STCN model using two labeled frames and their adjacent unlabeled frames, the trained STCN model is used to predict initialized pseudo labels for phase-2.

To run phase-1, you can use:

sh run_phase1.sh

or using commands:

CUDA_VISIBLE_DEVICES=0,1 OMP_NUM_THREADS=4 \
  python -m torch.distributed.launch --master_port 9846 --nproc_per_node=2 train.py --stage 3 \
  --id phase1 \
  --load_network ckpts/stcn_s0.pth \
  --yv_data util/yv_rand_2frames.json \
  --davis_data  util/davis_rand_2frames.json \
  --semi \
  --semi_thres_upper 0.9 \
  --end_warm 70000 \
  --use_teacher --ema_alpha 0.995

Note, you can also use only 1 GPU having enough memory, but remember to change the argument --batch_size to 2x.

Intermediate Inference

This step uses the trained STCN in phase-1 to predict pseudo labels for unlabeled frames. For each of the two labeled frames in a video, the model takes it as the reference frame to infer the predictions for unlabeled frames from the inference frame to the end frame and in a reverse manner. Therefore, you need to run two times for each labeled frame, and four times per video.

To run intermediate inference, you can use the scripts in run_youtube_phase2 for YouTube-VOS dataset or run_davis_phase2 for DAVIS dataset respectively. Taking YouTube-VOS as an example, you need to run commands as follows:

sh run_youtube_phase2/run_eval_youtube_phase2_left.sh
sh run_youtube_phase2/run_eval_youtube_phase2_leftsecgt.sh
sh run_youtube_phase2/run_eval_youtube_phase2_right.sh
sh run_youtube_phase2/run_eval_youtube_phase2_rightsecgt.sh

Then, you need to merge all predictions as:

cd phase2_scripts
python merge_left_right.py

Phase-2

You can train various VOS methods in phase-2 as it is originally trained through supervised learning, except you need to update pseudo labels for unlabeled frames during training.

To run phase-2, you can use:

sh run_phase2.sh

or using commands:

CUDA_VISIBLE_DEVICES=0,1 OMP_NUM_THREADS=4 \
  python -m torch.distributed.launch --master_port 9846 --nproc_per_node=2 train.py --stage 3 \
  --id phase2 \
  --load_network saves/stcn_s0.pth\
  --yv_data util/yv_rand_2frames.json \
  --davis_data  util/davis_rand_2frames.json \
  --phase2_yv ../vos_phase2/phase1_merge_480p \
  --phase2_davis ../vos_phase2/phase1_merge_davis \
  --phase2_train 0 --phase2_thres 0.99 --phase2_start_update 70000

You need to indicate the pseudo-label path predicted by intermediate inference using --phase2_yv and --phase2_davis for YouTube-VOS and DAVIS respectively.

Citation

Please cite our paper if you find this repo useful! Thx!

@inproceedings{two-shot-vos,
  title={Two-shot Video Object Segmentation},
  author={Yan, Kun and Li, Xiao and Wei, Fangyun and Wang, Jinglu and Zhang, Chenbin and Wang, Ping and Lu, Yan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2257--2267},
  year={2023}
}

You can find our paper here.