MaskFreeVIS

Mask-Free Video Instance Segmentation [CVPR 2023].

This is the official pytorch implementation of MaskFreeVIS built on the open-source detectron2. We aim to remove the necessity for expensive video masks and even image masks for training VIS models. Our project website contains more information, including the visual video comparison: vis.xyz/pub/maskfreevis.

Mask-Free Video Instance Segmentation
Lei Ke, Martin Danelljan, Henghui Ding, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu
CVPR 2023

Highlights

• High-performing video instance segmentation without using any video masks or even image mask labels. Using SwinL and built on Mask2Former, MaskFreeVIS achieved 56.0 AP on YTVIS without using any video masks labels. Using ResNet-101, MaskFreeVIS achieves 49.1 AP without using video masks, and 47.3 AP only using COCO mask initialized model.
• Novelty: a new parameter-free Temporal KNN-patch Loss (TK-Loss), which leverages temporal masks consistency using unsupervised one-to-k patch correspondence.
• Simple: TK-Loss is flexible to intergrated with state-of-the-art transformer-based VIS models, with no trainable parameters.

Visualization results of MaskFreeVIS

Introduction

The recent advancement in Video Instance Segmentation (VIS) has largely been driven by the use of deeper and increasingly data-hungry transformer-based models. However, video masks are tedious and expensive to annotate, limiting the scale and diversity of existing VIS datasets. In this work, we aim to remove the mask-annotation requirement. We propose MaskFreeVIS, achieving highly competitive VIS performance, while only using bounding box annotations for the object state. We leverage the rich temporal mask consistency constraints in videos by introducing the Temporal KNN-patch Loss (TK-Loss), providing strong mask supervision without any labels. Our TK-Loss finds one-to-many matches across frames, through an efficient patch-matching step followed by a K-nearest neighbor selection. A consistency loss is then enforced on the found matches. Our mask-free objective is simple to implement, has no trainable parameters, is computationally efficient, yet outperforms baselines employing, e.g., state-of-the-art optical flow to enforce temporal mask consistency. We validate MaskFreeVIS on the YouTube-VIS 2019/2021, OVIS and BDD100K MOTS benchmarks. The results clearly demonstrate the efficacy of our method by drastically narrowing the gap between fully and weakly-supervised VIS performance.

Methods

Installation

Please see Getting Started with Detectron2 for full usage.

Requirements

• Linux or macOS with Python 3.6
• PyTorch 1.9 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this. Note, please check PyTorch version matches that is required by Detectron2.
• Detectron2: follow Detectron2 installation instructions.
• OpenCV is optional but needed by demo and visualization
• pip install -r requirements.txt

CUDA kernel for MSDeformAttn

After preparing the required environment, run the following command to compile CUDA kernel for MSDeformAttn:

CUDA_HOME must be defined and points to the directory of the installed CUDA toolkit.

cd mask2former/modeling/pixel_decoder/ops
sh make.sh

Building on another system

To build on a system that does not have a GPU device but provide the drivers:

TORCH_CUDA_ARCH_LIST='8.0' FORCE_CUDA=1 python setup.py build install

Example conda environment setup

conda create --name maskfreevis python=3.8 -y
conda activate maskfreevis
conda install pytorch==1.9.0 torchvision==0.10.0 cudatoolkit=11.1 -c pytorch -c nvidia
pip install -U opencv-python

# under your working directory
git clone git@github.com:facebookresearch/detectron2.git
cd detectron2
pip install -e .

cd ..
git clone https://github.com/SysCV/MaskFreeVIS.git
cd MaskFreeVIS
pip install -r requirements.txt
cd mask2former/modeling/pixel_decoder/ops
sh make.sh

Dataset preparation

Please see the document here.

Model Zoo

Video Instance Segmentation (YouTubeVIS)

Using COCO image masks without YTVIS video masks during training:

Config Name	Backbone	AP	download	Training Script	COCO Init Weight
MaskFreeVIS	R50	46.6	model	script	Init
MaskFreeVIS	R101	49.1	model	script	Init
MaskFreeVIS	Swin-L	56.0	model	script	Init

For below two training settings without using pseudo COCO images masks for joint video training, please change the folder to:

cd mfvis_nococo

1. Only using COCO mask initialized model without YTVIS video masks during training:

Config Name	Backbone	AP	download	Training Script	COCO Init Weight
MaskFreeVIS	R50	43.8	model	script	Init
MaskFreeVIS	R101	47.3	model	script	Init

2. Only using COCO box initialized model without YTVIS video masks during training:

Config Name	Backbone	AP	download	Training Script	COCO Box Init Weight
MaskFreeVIS	R50	42.5	model	script	Init

Please see our script folder.

Inference & Evaluation

First download the provided trained model from our model zoo table and put them into the mfvis_models.

mkdir mfvis_models

Refer to our scripts folder for more commands:

Example evaluation scripts:

bash scripts/eval_8gpu_mask2former_r50_video.sh
bash scripts/eval_8gpu_mask2former_r101_video.sh
bash scripts/eval_8gpu_mask2former_swinl_video.sh

Results Visualization

Example visualization script:

bash scripts/visual_video.sh

Citation

If you find MaskFreeVIS useful in your research or refer to the provided baseline results, please star ⭐ this repository and consider citing 📝:

@inproceedings{maskfreevis,
    author={Ke, Lei and Danelljan, Martin and Ding, Henghui and Tai, Yu-Wing and Tang, Chi-Keung and Yu, Fisher},
    title={Mask-Free Video Instance Segmentation},
    booktitle = {CVPR},
    year = {2023}
}  

Acknowledgments

• Thanks BoxInst image-based instance segmentation losses.
• Thanks Mask2Former and VMT for providing useful inference and evaluation toolkits.