HFAN: Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation (ECCV 2022)

Note: Google Drive subscription has expired, and we have migrated models to Baidu Drive.

Introduction

This work has been accepted to ECCV 2022, and we will update the camera-ready version soon.

Gensheng Pei, Yazhou Yao*, Guo-Sen Xie*, Fumin Shen, Zhenmin Tang, Jinhui Tang. "Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation", European Conference on Computer Vision (ECCV), 2022.

Performance vs. Speed

Figure 1: Performance of HFAN-Small and HFAN-Medium on DAVIS-16.

Overview

This repository is the official PyTorch implementation of the anonymous paper:

Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
FAM relies on sharing primary objects in images across modalities to align appearance and motion features to address the mismatch of primary object positions between video frames and their corresponding optical flows.
FAT constructs a feature adaptation weight to automatically enhance cross-modal features to tackle the modal mismatch problem between aligned feature maps.

Figure 2: The framework of HFAN.

Dependencies

We use MMSegmentation to implement our model, and CUDA 10.1 to run our experiments. Please refer to the guidelines in MMSegmentation v0.11.0.

To simplify the reproduction steps, we only need to install

pip install torch==1.7.1 torchvision==0.8.2
pip install mmcv-full==1.3.8 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.0/index.html
pip install opencv-python

Dataset Preparation

Download the DAVIS dataset from DAVIS website.
Download the YouTube-VOS dataset from YouTube-VOS website.
To quickly reproduce the proposed method, we upload the processed data to Baidu Drive (DAVIS and YouTube-VOS).
Please ensure the datasets are organized as following format.

|DAVIS2SEG
|--frame
|--flow
|--mask

|YouTube2SEG
|--frame
|--flow
|--mask

Training

Note that the locations in the code where the paths need to be modified are shown below:
infer.py in line 35.
local_configs/hfan/*.160k.py in lines 3, 4, 69.
local_configs/hfan/*.refine.py in lines 3, 67, 111.

Download MiT weights pretrained on ImageNet-1K, and put them in a folder checkpoint/.

Train HFAN-Small

# two gpus training (V100 32G)
# Please set OMP_NUM_THREADS=(1 or Your CPUs) when training with multiple GPUs.
## First
CUDA_VISIBLE_DEVICES=0,1 OMP_NUM_THREADS=16 bash tools/dist_train.sh local_configs/hfan/hfan.small.512x512.160k.py 2 --seed 1208 --deterministic --work-dir hfan-small
## Second
CUDA_VISIBLE_DEVICES=0,1 OMP_NUM_THREADS=16 bash tools/dist_train.sh local_configs/hfan/hfan.small.512x512.refine.py 2 --seed 1208 --deterministic --work-dir hfan-small

Train HFAN-Medium

# two gpus training (V100 32G)
## First
CUDA_VISIBLE_DEVICES=0,1 OMP_NUM_THREADS=16 bash tools/dist_train.sh local_configs/hfan/hfan.medium.512x512.160k.py 2 --seed 1208 --deterministic --work-dir hfan-medium
## Second
CUDA_VISIBLE_DEVICES=0,1 OMP_NUM_THREADS=16 bash tools/dist_train.sh local_configs/hfan/hfan.medium.512x512.refine.py 2 --seed 1208 --deterministic --work-dir hfan-medium

Testing

Download HFAN-Small, and put it in a folder checkpoint/.

Evaluate HFAN-Small:

# single gpu (V100 32G)
python infer.py \
    --config local_configs/hfan/hfan.small.512x512.refine.py \
    --checkpoint checkpoint/HFAN-s-converted.pth \
    --output_dir ./output_path/hfan-small
    
# single gpu (V100 32G) with multi-scale
python infer.py \
    --config local_configs/hfan/hfan.small.512x512.refine.py \
    --checkpoint checkpoint/HFAN-s-converted.pth \
    --output_dir ./output_path/hfan-small-MS --aug-test

Download HFAN-Medium, and put it in a folder checkpoint/.

Evaluate HFAN-Medium

# single gpu (V100 32G)
python infer.py \
    --config local_configs/hfan/hfan.medium.512x512.refine.py \
    --checkpoint checkpoint/HFAN-m-converted.pth \
    --output_dir ./output_path/hfan-medium
    
# single gpu (V100 32G) with multi-scale
python infer.py \
    --config local_configs/hfan/hfan.medium.512x512.refine.py \
    --checkpoint checkpoint/HFAN-m-converted.pth \
    --output_dir ./output_path/hfan-medium-MS --aug-test

Results

We report the results from the current codebase as follows, which match the performance reported in our original paper. For unsupervised video object segmentation, the matlab version of evaluation code can be obtained from DAVIS-EValuation, and the multiprocessing python version is available PyDavis16EvalToolbox. The evaluation toolbox for video salient object detection task is available VSOD.

DAVIS-16, Unsupervised Video Object Segmentation

Metrics	J Mean ↑	J Recall ↑	J Decay ↓	F Mean ↑	F Recall ↑	F Decay ↓	J&F Mean ↑	FPS ↑
HFAN-Small (SS)	86.2	96.7	4.6	87.1	95.5	2.3	86.7	20.8
HFAN-Small (MS)	87.1	96.8	4.8	87.7	95.3	2.5	87.4	2.5
HFAN-Medium (SS)	86.8	96.1	4.3	88.2	95.3	1.1	87.5	14.4
HFAN-Medium (MS)	88.0	96.2	4.5	89.3	95.4	2.0	88.7	1.4

Long-Videos, Unsupervised Video Object Segmentation

Metrics	J Mean ↑	J Recall ↑	J Decay ↓	F Mean ↑	F Recall ↑	F Decay ↓	J&F Mean ↑
HFAN-Small	74.9	82.5	14.8	76.1	86.0	16.0	75.5
HFAN-Medium	80.2	91.2	9.4	83.2	96.5	7.1	81.7

DAVIS-16, Video Salient Object Detection

Metrics	S ↑	E ↑	F ↑	MAE ↓
HFAN-Small	0.934	0.983	0.929	0.009
HFAN-Medium	0.938	0.983	0.935	0.008

Ablation Studies

To facilitate the ablation study, we decompose the various modules of the model in the codebase. Download links are provided for each ablation version of the model.

Impact of Data Input

# single gpu (V100 32G)
## Image frame only
python infer.py \
    --config local_configs/hfan/hfan.small.512x512.refine.py \
    --checkpoint checkpoint/im-converted.pth \
    --options model.decode_head.select_method=im \
    --output_dir ./output_path/im
## Optical flow only    
python infer.py \
    --config local_configs/hfan/hfan.small.512x512.refine.py \
    --checkpoint checkpoint/fw-converted.pth \
    --options model.decode_head.select_method=fw \
    --output_dir ./output_path/fw
## Baseline    
python infer.py \
    --config local_configs/hfan/hfan.small.512x512.refine.py \
    --checkpoint checkpoint/baseline-converted.pth \
    --options model.decode_head.select_method=base \
    --output_dir ./output_path/baseline

Input	J Mean ↑	ΔJ	F Mean ↑	ΔF	download
Image frame only	79.1	-3.9	79.8	-3.5	model / mask
Optical flow only	77.9	-5.1	76.5	-6.8	model / mask
Baseline	83.0	-	83.3	-	model / mask

Efficacy of Crucial Modules

# single gpu (V100 32G)
## FAM 
python infer.py \
    --config local_configs/hfan/hfan.small.512x512.refine.py \
    --checkpoint checkpoint/FAM-converted.pth \
    --options model.decode_head.select_method=fam \
    --output_dir ./output_path/FAM
## FAT
python infer.py \
    --config local_configs/hfan/hfan.small.512x512.refine.py \
    --checkpoint checkpoint/FAT-converted.pth \
    --options model.decode_head.select_method=fat \
    --output_dir ./output_path/FAT
## HFAN  
python infer.py \
    --config local_configs/hfan/hfan.small.512x512.refine.py \
    --checkpoint checkpoint/HFAN-s-converted.pth \
    --options model.decode_head.select_method=hfan \
    --output_dir ./output_path/HFAN

Variants	J Mean ↑	ΔJ	F Mean ↑	ΔF	download
Baseline	83.0	-	83.3	-	model / mask
Baseline + FAM	85.2	+2.2	85.6	+2.3	model / mask
Baseline + FAT	85.0	+2.0	86.1	+2.8	model / mask
Baseline + HFAN	86.2	+3.2	87.1	+3.8	model / mask

Efficacy of Backbone

Backbone	J Mean ↑	F Mean ↑	FPS ↑	download
MiT-b0 (SS/MS)	81.5/83.4	80.8/82.3	24.0/3.4	model / mask
MiT-b1 (SS/MS)	86.2/87.1	87.1/87.7	20.8/2.5	model / mask
MiT-b2 (SS/MS)	86.8/88.0	88.2/89.3	14.4/1.4	model / mask
MiT-b3 (SS/MS)	86.8/88.2	88.8/90.0	10.6/1.0	model / mask
Swin-Tiny (SS/MS)	86.0/87.2	87.3/87.9	12.8/1.1	model / mask
ResNet-101 (SS/MS)	86.7/87.5	87.5/88.1	12.4/1.3	model / mask

Visualize Ablated Versions

Feature-level visualization Mask-level visualization

Qualitative Results

Mask-level visualization

Citation

If you find this useful in your research, please consider citing:

@inproceedings{
title={Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation},
author={Gensheng Pei, Yazhou Yao, Guo-Sen Xie, Fumin Shen, Zhenmin Tang, Jinhui Tang},
booktitle={European Conference on Computer Vision (ECCV)},
year={2022}
}

Name		Name	Last commit message	Last commit date
Latest commit History 53 Commits
assets		assets
demo		demo
local_configs		local_configs
mmseg		mmseg
tools		tools
.gitignore		.gitignore
README.md		README.md
infer.py		infer.py

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HFAN: Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation (ECCV 2022)

Note: Google Drive subscription has expired, and we have migrated models to Baidu Drive.

Introduction

Performance vs. Speed

Overview

Dependencies

Dataset Preparation

Training

Testing

Results

Ablation Studies

Visualize Ablated Versions

Qualitative Results

Citation

About

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Contributors 2

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NUST-Machine-Intelligence-Laboratory/HFAN

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HFAN: Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation (ECCV 2022)

Note: Google Drive subscription has expired, and we have migrated models to Baidu Drive.

Introduction

Performance vs. Speed

Overview

Dependencies

Dataset Preparation

Training

Testing

Results

Ablation Studies

Visualize Ablated Versions

Qualitative Results

Citation

About

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