Awesome

Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection

Authors: Siyuan Yao, Hao Sun, Tian-Zhu Xiang, Xiao Wang and Xiaochun Cao

This work has been submitted to the IEEE Transactions on Image Processing (TIP) and is currently under review.

The training and testing experiments are conducted using PyTorch with 4 NVIDIA GeForce RTX 3090 GPUs of 24 GB Memory.

Overview

In this paper, we propose a hierarchical graph interaction network termed HGINet for camouflaged object detection, which is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Specifically, we first design a region-aware token focusing attention (RTFA) with dynamic token clustering to excavate the potentially distinguishable tokens in the local region. Afterwards, a hierarchical graph interaction transformer (HGIT) is proposed to construct bi-directional aligned communication between hierarchical features in the latent interaction space for visual semantics enhancement. Furthermore, we propose a decoder network with confidence aggregated feature fusion (CAFF) modules, which progressively fuses the hierarchical interacted features to refine the local detail in ambiguous regions. Extensive experiments conducted on the prevalent datasets, i.e. COD10K, CAMO, NC4K and CHAMELEON demonstrate the superior performance of HGINet compared to existing state-of-the-art methods.

Prepare Data

The training and testing datasets can be downloaded at COD10K-train, COD10K-test + CAMO-test + CHAMELEON and NC4K, respectively.

Installation

conda create -n HGINet python=3.8
conda activate HGINet
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install openmim==0.3.9
mim install mmcv-full==1.7.0 mmengine==0.8.4
pip install mmsegmentation==0.30.0 timm h5py einops fairscale imageio fvcore pysodmetrics

Training

python -m torch.distributed.launch --nproc_per_node=<#GPU> --master_port=<port> train.py --path "<COD dataset path>" --pretrain "<pretrain model path>"

Pretrain model weight is stored in Google Drive. After downloading, please change the file path in the corresponding code or the training command.

[!note] It is better to train with an overall batch size at least 16 due to the 'BatchNorm2D'.

Testing & Evaluation

python test.py
python eval.py

Our well-trained model is stored in Google Drive. After downloading, please change the file path in the corresponding code.

[!note] If you want to debug with the same model parameters used for testing “Prediction results", use the previous codebase and this well-trained model.

Results

Prediction maps

The prediction results of our HGINet are stored in Google Drive. Please check.

Citation

@article{yao2024hierarchical,
         title={Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection}, 
         author={Yao, Siyuan and Sun, Hao and Xiang, Tian-Zhu and Wang, Xiao and Cao, Xiaochun},
         journal={arXiv preprint arXiv:2408.15020},
         year={2024}
}

Acknowledgement

This repository is built using the BiFormer and FSPNet repositories.