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Article
Computer Science, Artificial Intelligence
Aojun Gong et al.
Summary: This paper proposes an unsupervised method for RGB-T image saliency detection by constructing a graph model and fusing algorithms such as classification distance and sparse constraint to enhance detection accuracy.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jun Wang et al.
Summary: High-level semantic features and low-level detail features play important roles in salient object detection in fully convolutional neural networks (FCNs). This paper proposes a residual attention learning strategy and a multistage refinement mechanism to gradually improve the coarse prediction. Through integrating low-level detailed features and high-level semantic features, and employing a residual attention mechanism module to enhance feature maps, the proposed method significantly outperforms 15 state-of-the-art methods in various evaluation metrics on benchmark datasets.
APPLIED INTELLIGENCE
(2022)
Article
Engineering, Electrical & Electronic
Jie Wang et al.
Summary: This paper proposes a Cross-Guided Fusion Network (CGFNet) for RGB-T salient object detection, which combines RGB and thermal infrared (T) images to improve the detection performance of salient objects by leveraging the unique characteristics of different modalities.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Wujie Zhou et al.
Summary: ECFFNet is a RGB-T feature fusion network that achieves accurate detection of salient objects by combining thermal images and RGB images. It outperforms state-of-the-art methods by implementing cross-modality fusion, bilateral reversal fusion, and multilevel consistent fusion.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Wei Gao et al.
Summary: The use of complementary information, such as depth or thermal information, has shown benefits in salient object detection. However, the current approaches for RGB-D or RGB-T salient object detection problems are solved independently and they directly extract and fuse raw features. This work proposes a unified end-to-end framework that can simultaneously analyze RCB-D and RGB-T salient object detection tasks, and it introduces a multi-stage and multi-scale fusion network to effectively handle multi-modal features.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Fushuo Huo et al.
Summary: The study proposed an efficient encoder-decoder network named Context-guided Stacked Refinement Network (CSRNet), which reduces computational cost using a lightweight backbone and efficient decoder parts, fuses RGB and T modalities through the Context-guided Cross Modality Fusion (CCMF) module, and refines features progressively via the Stacked Refinement Network (SRN).
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Liming Huang et al.
Summary: RGB and thermal infrared (RGBT) image saliency detection is a new direction in computer vision. This paper proposes an unsupervised RGBT saliency detection method based on multi-graph fusion and learning, which improves detection performance by fusing and learning from multi-modal images, and the effectiveness of the method is validated in experiments.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Xin Deng et al.
Summary: In this paper, a novel deep convolutional neural network is proposed to address general multi-modal image restoration and fusion problems, drawing inspirations from a new multi-modal convolutional sparse coding model. The proposed CU-Net architecture automatically separates common and unique information, consisting of three modules: unique feature extraction, common feature preservation, and image reconstruction. Extensive numerical results validate the effectiveness of the method on various tasks such as RGB-guided depth image super-resolution and multi-focus image fusion.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Jun Jiao et al.
Summary: In this paper, a novel non-local duplicate pooling (NLDP) network is proposed to address internal discontinuities in salient object detection. By removing early convolutional layers and introducing a duplicate pooling module (DPM), richer saliency maps are generated. A non-local module (NLM) is used to enhance long-range dependencies and improve internal continuities between saliency maps. Comprehensive experiments demonstrate the effectiveness and speed of the proposed method compared to existing salient object detection methods.
APPLIED INTELLIGENCE
(2021)
Article
Engineering, Electrical & Electronic
Qiang Zhang et al.
Summary: This article explores RGB-T saliency detection and introduces a novel deep feature fusion network that outperforms other state-of-the-art methods by integrating multi-modality and multi-level feature fusion modules.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Xuehao Wang et al.
Summary: The paper proposes a simple yet effective scheme to measure the quality of depth (D) in advance, employing a multi-scale methodology for D quality assessments and combining RGB and D saliency cues to guide selective RGBD fusion, resulting in steady performance improvements.
Article
Computer Science, Artificial Intelligence
Deng-Ping Fan et al.
Summary: This article makes contributions to RGB-D SOD by collecting a new SIP dataset, conducting a large-scale benchmark comparing contemporary methods, and proposing the D(3)Net model. D(3)Net outperforms prior contenders and can efficiently extract salient object masks for real scenes.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Wei Ji et al.
Summary: The proposed Depth Calibration and Fusion (DCF) framework addresses the challenges in Salient Object Detection by calibrating depth bias and fusing features from RGB and depth modalities, achieving superior performance compared to state-of-the-art methods. The depth calibration strategy can also be used as a preprocessing step to improve existing RGB-D SOD models.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Article
Engineering, Electrical & Electronic
Qinling Guo et al.
Summary: The paper introduces a two-stage fusion network (TSFNet) for integrating RGB and thermal information for RGB-T SOD. The network includes a feature-wise fusion module and a bilateral auxiliary fusion module, with multiple supervision used to further enhance SOD performance.
IEEE SIGNAL PROCESSING LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Zuyao Chen et al.
Summary: This paper proposes a novel network named DPANet, which addresses the two main challenges in RGB-D salient object detection: integrating cross-modal data complementarity and preventing contamination effects from unreliable depth maps. By introducing depth potentiality perception and gated multi-modality attention module, the proposed approach effectively models the potential of depth maps and integrates cross-modal complementarity.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Zhengzheng Tu et al.
Summary: In this study, a multi-interactive dual-decoder is proposed to mine and model the multi-type interactions for accurate RGBT SOD. The method performs well on public datasets and can handle challenging scenarios even in the presence of invalid modality.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Wen-Da Jin et al.
Summary: Our proposed Complementary Depth Network (CDNet) effectively utilizes saliency-informative depth features for RGB-D salient object detection, by selecting saliency-informative depth maps as training targets and estimating meaningful depth maps using RGB features. Additionally, a dynamic scheme is introduced to fuse depth features from original and estimated depth maps with adaptive weights, and a two-stage cross-modal feature fusion scheme is designed to integrate depth features with RGB ones for improved performance.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Hong-Bo Bi et al.
Summary: This paper systematically studies the role of spatial and temporal attention mechanism in video salient object detection, proposing a two-stage spatial-temporal attention network called STA-Net. By utilizing Multi-Scale-Spatial-Attention and Pyramid-Saliency-Shift-Aware modules, the network efficiently exploits multi-scale saliency information and dynamic object information, achieving compelling performance in video salient object detection task.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Gongyang Li et al.
Summary: In this study, a novel RGB-D Salient Object Detection method, HAINet, is proposed to mitigate distractions in depth maps and highlight salient objects in RGB images through three key stages.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Information Systems
Zhengzheng Tu et al.
IEEE TRANSACTIONS ON MULTIMEDIA
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Miao Zhang et al.
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2020)
Article
Engineering, Electrical & Electronic
Liming Huang et al.
IEEE SIGNAL PROCESSING LETTERS
(2020)
Article
Computer Science, Artificial Intelligence
Qiang Zhang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Zhengzheng Tu et al.
2019 2ND IEEE CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2019)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Deng-Ping Fan et al.
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Yongri Piao et al.
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019)
(2019)
Article
Computer Science, Artificial Intelligence
Liang-Chieh Chen et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2018)
Article
Computer Science, Artificial Intelligence
Ya'nan Guo et al.
Article
Computer Science, Artificial Intelligence
Liangqiong Qu et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Clement Godard et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Deng-Ping Fan et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Ran Margolin et al.
2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2014)
Proceedings Paper
Computer Science, Artificial Intelligence
Houwen Peng et al.
COMPUTER VISION - ECCV 2014, PT III
(2014)
Proceedings Paper
Computer Science, Artificial Intelligence
Radhakrishna Achanta et al.
CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4
(2009)
