Specificity-preserving RGB-D Saliency Detection

Related tags

Deep LearningSPNet
Overview

Specificity-preserving RGB-D Saliency Detection

Authors: Tao Zhou, Huazhu Fu, Geng Chen, Yi Zhou, Deng-Ping Fan, and Ling Shao.

1. Preface

  • This repository provides code for "Specificity-preserving RGB-D Saliency Detection" ICCV-2021. Arxiv Page

2. Overview

2.1. Introduction

RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing works often focus on learning a shared representation through various fusion strategies, with few methods explicitly considering how to preserve modality-specific characteristics. In this paper, taking a new perspective, we propose a specificitypreserving network (SP-Net) for RGB-D saliency detection, which benefits saliency detection performance by exploring both the shared information and modality-specific properties (e.g., specificity). Specifically, two modality-specific networks and a shared learning network are adopted to generate individual and shared saliency maps. A crossenhanced integration module (CIM) is proposed to fuse cross-modal features in the shared learning network, which are then propagated to the next layer for integrating cross-level information. Besides, we propose a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder, which can provide rich complementary multi-modal information to boost the saliency detection performance. Further, a skip connection is used to combine hierarchical features between the encoder and decoder layers. Experiments on six benchmark datasets demonstrate that our SP-Net outperforms other state-of-the-art methods.

2.2. Framework Overview


Figure 1: The overall architecture of the proposed SP-Net.

2.3. Quantitative Results


2.4. Qualitative Results


Figure 2: Visual comparisons of our method and eight state-of-the-art methods.

3. Proposed Baseline

3.1. Training/Testing

The training and testing experiments are conducted using PyTorch with one NVIDIA Tesla V100 GPU with 32 GB memor.

  1. Configuring your environment (Prerequisites):

    • Installing necessary packages: pip install -r requirements.txt.
  2. Downloading necessary data:

  3. Train Configuration:

    • After you download training dataset, just run train.py to train our model.
  4. Test Configuration:

    • After you download all the pre-trained model and testing dataset, just run test_produce_maps.py to generate the final prediction map, then run test_evaluation_maps.py to obtain the final quantitative results.

    • You can also download predicted saliency maps (download link (Google Drive)) and move it into ./Predict_maps/, then then run test_evaluation_maps.py.

3.2 Evaluating your trained model:

Our evaluation is implemented by python, please refer to test_evaluation_maps.py

4. Citation

Please cite our paper if you find the work useful, thanks!

@inproceedings{zhouiccv2021,
	title={Specificity-preserving RGB-D Saliency Detection},
	author={Zhou, Tao and Fu, Huazhu and Chen, Geng and Zhou, Yi and Fan, Deng-Ping and Shao, Ling},
	booktitle={International Conference on Computer Vision (ICCV)},
	year={2021},
}

@inproceedings{zhoucvmj2022,
	title={Specificity-preserving RGB-D Saliency Detection},
	author={Zhou, Tao and Fan, Deng-Ping and Chen, Geng and Zhou, Yi and Fu, Huazhu},
	booktitle={Computational Visual Media},
	year={2022},
}

back to top

Owner
Tao Zhou
Research scientist.
Tao Zhou
Implementation of Barlow Twins paper

barlowtwins PyTorch Implementation of Barlow Twins paper: Barlow Twins: Self-Supervised Learning via Redundancy Reduction This is currently a work in

IgorSusmelj 86 Dec 20, 2022
MRI reconstruction (e.g., QSM) using deep learning methods

deepMRI: Deep learning methods for MRI Authors: Yang Gao, Hongfu Sun This repo is devloped based on Pytorch (1.8 or later) and matlab (R2019a or later

Hongfu Sun 17 Dec 18, 2022
In the case of your data having only 1 channel while want to use timm models

timm_custom Description In the case of your data having only 1 channel while want to use timm models (with or without pretrained weights), run the fol

2 Nov 26, 2021
Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"

The Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more" Arxiv preprint Louay Hazami   ·   Rayhane Mama   ·   Ragavan Thurairatn

Rayhane Mama 144 Dec 23, 2022
Official implementation for ICDAR 2021 paper "Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer"

Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer Description Convert offline handwritten mathematical expressi

Wenqi Zhao 87 Dec 27, 2022
Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.

counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code

Networks Learning 11 Dec 09, 2022
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dea

MIC-DKFZ 1.2k Jan 04, 2023
Code for "Multi-Time Attention Networks for Irregularly Sampled Time Series", ICLR 2021.

Multi-Time Attention Networks (mTANs) This repository contains the PyTorch implementation for the paper Multi-Time Attention Networks for Irregularly

The Laboratory for Robust and Efficient Machine Learning 68 Dec 17, 2022
Discord bot for notifying on github events

Git-Observer Discord bot for notifying on github events ⚠️ This bot is meant to write messages to only one channel (implementing this for multiple pro

ilu_vatar_ 0 Apr 19, 2022
SEC'21: Sparse Bitmap Compression for Memory-Efficient Training onthe Edge

Training Deep Learning Models on The Edge Training on the Edge enables continuous learning from new data for deployed neural networks on memory-constr

Brown University Scale Lab 4 Nov 18, 2022
This is just a funny project that we want to see AutoEncoder (AE) can actually work to enhance the features we want

Funny_muscle_enhancer :) 1.Discription: This is just a funny project that we want to see AutoEncoder (AE) can actually work on the some features. We w

Jing-Yao Chen (Jacob) 8 Oct 01, 2022
The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

Representative Batch Normalization (RBN) with Feature Calibration The official implementation of the CVPR2021 oral paper: Representative Batch Normali

Open source projects of ShangHua-Gao 76 Nov 09, 2022
PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models

Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th

Rithesh Kumar 135 Oct 27, 2022
Code base of object detection

rmdet code base of object detection. 环境安装: 1. 安装conda python环境 - `conda create -n xxx python=3.7/3.8` - `conda activate xxx` 2. 运行脚本,自动安装pytorch1

3 Mar 08, 2022
Implementation of Feedback Transformer in Pytorch

Feedback Transformer - Pytorch Simple implementation of Feedback Transformer in Pytorch. They improve on Transformer-XL by having each token have acce

Phil Wang 93 Oct 04, 2022
🔥 Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python

Cogitare is a Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python. A friendly interface for beginners and a powerful too

Cogitare - Modern and Easy Deep Learning with Python 76 Sep 30, 2022
LSTM and QRNN Language Model Toolkit for PyTorch

LSTM and QRNN Language Model Toolkit This repository contains the code used for two Salesforce Research papers: Regularizing and Optimizing LSTM Langu

Salesforce 1.9k Jan 08, 2023
Includes PyTorch -> Keras model porting code for ConvNeXt family of models with fine-tuning and inference notebooks.

ConvNeXt-TF This repository provides TensorFlow / Keras implementations of different ConvNeXt [1] variants. It also provides the TensorFlow / Keras mo

Sayak Paul 87 Dec 06, 2022
This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data.

This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data.

212 Dec 25, 2022