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
Wafer Fault Detection using MlOps Integration

Wafer Fault Detection using MlOps Integration This is an end to end machine learning project with MlOps integration for predicting the quality of wafe

Sethu Sai Medamallela 0 Mar 11, 2022
Python library for science observations from the James Webb Space Telescope

JWST Calibration Pipeline JWST requires Python 3.7 or above and a C compiler for dependencies. Linux and MacOS platforms are tested and supported. Win

Space Telescope Science Institute 386 Dec 30, 2022
Code for the paper "Combining Textual Features for the Detection of Hateful and Offensive Language"

The repository provides the source code for the paper "Combining Textual Features for the Detection of Hateful and Offensive Language" submitted to HA

Sherzod Hakimov 3 Aug 04, 2022
unet for image segmentation

Implementation of deep learning framework -- Unet, using Keras The architecture was inspired by U-Net: Convolutional Networks for Biomedical Image Seg

zhixuhao 4.1k Dec 31, 2022
BanditPAM: Almost Linear-Time k-Medoids Clustering

BanditPAM: Almost Linear-Time k-Medoids Clustering This repo contains a high-performance implementation of BanditPAM from BanditPAM: Almost Linear-Tim

254 Dec 12, 2022
A PyTorch Implementation of SphereFace.

SphereFace A PyTorch Implementation of SphereFace. The code can be trained on CASIA-Webface and the best accuracy on LFW is 99.22%. SphereFace: Deep H

carwin 685 Dec 09, 2022
The 7th edition of NTIRE: New Trends in Image Restoration and Enhancement workshop will be held on June 2022 in conjunction with CVPR 2022.

NTIRE 2022 - Image Inpainting Challenge Important dates 2022.02.01: Release of train data (input and output images) and validation data (only input) 2

Andrés Romero 37 Nov 27, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
(NeurIPS '21 Spotlight) IQ-Learn: Inverse Q-Learning for Imitation

Inverse Q-Learning (IQ-Learn) Official code base for IQ-Learn: Inverse soft-Q Learning for Imitation, NeurIPS '21 Spotlight IQ-Learn is an easy-to-use

Divyansh Garg 102 Dec 20, 2022
Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Causality In Traffic Accident (Under Construction) Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020) Overview Data Prepa

Tackgeun 21 Nov 20, 2022
Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors

-IEEE-TIM-2021-1-Shallow-CNN-for-HAR [IEEE TIM 2021-1] Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors All

Wenbo Huang 1 May 17, 2022
Telegram chatbot created with deep learning model (LSTM) and telebot library.

Telegram chatbot Telegram chatbot created with deep learning model (LSTM) and telebot library. Description This program will allow you to create very

1 Jan 04, 2022
This repository contains all data used for writing a research paper Multiple Object Trackers in OpenCV: A Benchmark, presented in ISIE 2021 conference in Kyoto, Japan.

OpenCV-Multiple-Object-Tracking Python is version 3.6.7 to install opencv: pip uninstall opecv-python pip uninstall opencv-contrib-python pip install

6 Dec 19, 2021
AI-generated-characters for Learning and Wellbeing

AI-generated-characters for Learning and Wellbeing Click here for the full project page. This repository contains the source code for the paper AI-gen

MIT Media Lab 214 Jan 01, 2023
a spacial-temporal pattern detection system for home automation

Argos a spacial-temporal pattern detection system for home automation. Based on OpenCV and Tensorflow, can run on raspberry pi and notify HomeAssistan

Angad Singh 133 Jan 05, 2023
Single Image Random Dot Stereogram for Tensorflow

TensorFlow-SIRDS Single Image Random Dot Stereogram for Tensorflow SIRDS is a means to present 3D data in a 2D image. It allows for scientific data di

Greg Peatfield 5 Aug 10, 2022
STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack This is the official implementation of the paper: STMTrack: Template-free Visual Tracking with Space-time Memory Networks. Setup Prepare Anac

Zhihong Fu 62 Dec 21, 2022
pip install python-office

🍬 python for office 👉 http://www.python4office.cn/ 👈 🌎 English Documentation 📚 简介 Python-office 是一个 Python 自动化办公第三方库,能解决大部分自动化办公的问题。而且每个功能只需一行代码,

程序员晚枫 272 Dec 29, 2022
CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

Frederick Wang 3 Apr 26, 2022
Reproduced Code for Image Forgery Detection papers.

Image Forgery Detection With over 4.5 billion active internet users, the amount of multimedia content being shared every day has surpassed everyone’s

Umar Masud 15 Dec 06, 2022