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Attention-guided Multi-modality Interaction Network for RGB-D Salient Object Detection

Authors:

Haojie LiAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 3

Article No.: 68, Pages 1 - 22

https://doi.org/10.1145/3624747

Published: 23 October 2023 Publication History

Abstract

The past decade has witnessed great progress in RGB-D salient object detection (SOD). However, there are two bottlenecks that limit its further development. The first one is low-quality depth maps. Most existing methods directly use raw depth maps to perform detection, but low-quality depth images can bring negative impacts to the detection performance. Hence, it is not desirable to utilize depth maps indiscriminately. The other one is how to effectively predict salient maps with clear boundary and complete salient region. To address these problems, an Attention-Guided Multi-Modality Interaction Network (AMINet) is proposed. First, we propose a new quality enhancement strategy for unreliable depth images, named Depth Enhancement Module (DEM). With respect to the second issue, we propose Cross-Modality Attention Module (CMAM) to rapidly locate salient region. The Boundary-Aware Module (BAM) is designed to utilize high-level feature to guide the low-level feature generation in a top-down way to make up for the dilution of the boundary. To further improve the accuracy, we propose Atrous Refined Block (ARB) to adaptively compensate for the shortcoming of atrous convolution. By integrating these interactive modules, features from depth and RGB streams can be refined efficiently, which consequently boosts the detection performance. Experimental results demonstrate the proposed AMINet exceeds state-of-the-art (SOTA) methods on several public RGB-D datasets.

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Index Terms

Attention-guided Multi-modality Interaction Network for RGB-D Salient Object Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 3

March 2024

665 pages

EISSN:1551-6865

DOI:10.1145/3613614

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 23 October 2023

Online AM: 16 September 2023

Accepted: 12 September 2023

Revised: 29 August 2023

Received: 03 February 2023

Published in TOMM Volume 20, Issue 3

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LiaoNing Revitalization Talents Program
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Innovative Talents Program for Liaoning Universities

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Fu LLiu YZhang YLi M(2024)Network Information Security Monitoring Under Artificial Intelligence EnvironmentInternational Journal of Information Security and Privacy10.4018/IJISP.34503818:1(1-25)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.4018/IJISP.345038
Liu BLi HWang ZXue T(2024)Transparent Depth Completion Using Segmentation FeaturesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/369497820:12(1-19)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3694978
Liao GGao W(2024)Rethinking Feature Mining for Light Field Salient Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3676967Online publication date: 8-Jul-2024
https://doi.org/10.1145/3676967
Gao HSu YWang FLi H(2024)Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365647620:7(1-24)Online publication date: 15-May-2024
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Wang FSu YWang RSun JSun FLi H(2024)Cross-Modal and Cross-Level Attention Interaction Network for Salient Object DetectionIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33338275:6(2907-2920)Online publication date: Jun-2024
https://doi.org/10.1109/TAI.2023.3333827
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