Related references
Note: Only part of the references are listed.
Article
Computer Science, Artificial Intelligence
Yongsheng Dong et al.
Summary: Semantic segmentation is fundamental for autonomous driving, but current approaches lack a good trade-off between accuracy and latency. In this study, the authors propose a real-time high-resolution refinement co-supervision network (RCNet), which outperforms seven representative segmentation methods by using a context refinement module and a boundary co-supervision mechanism.
IET COMPUTER VISION
(2023)
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Summary: In this paper, a multi-stage context refinement network (MCRNet) is proposed for semantic segmentation. By constructing the Lowest-resolution Chain Context Aggregation (LCCA) module and the High-resolution Context Attention Refinement (HCAR) module, MCRNet can encode rich semantic information while preserving spatial details, resulting in improved image segmentation performance.
Article
Computer Science, Artificial Intelligence
Yongsheng Dong et al.
Summary: This paper proposes a field-matching attention network (FMANet) for object detection, which normalizes the receptive fields between features at different stages to the same scale and captures spatial information and details using spatial and channel attention mechanisms. Experimental results show that FMANet achieves competitive performance in object detection.
Article
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Yongsheng Dong et al.
Summary: This paper proposes an efficient and compact interactive dual-branch network (CIDNet) for real-time semantic segmentation. A compact detail branch and a semantic branch are constructed, and a detail-semantic interactive module is used to fuse specific stages of the two branches. Finally, a dual-branch contextual attention fusion module is proposed to predict the final segmentation result.
COMPLEX & INTELLIGENT SYSTEMS
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Guangwei Gao et al.
Summary: In this article, a Fast Bilateral Symmetrical Network (FBSNet) is proposed for real-time semantic segmentation. FBSNet alleviates the challenges of calculation burden and redundant parameters by employing a symmetrical encoder-decoder structure with semantic information branch and spatial detail branch. Experimental results show that FBSNet can strike a good balance between accuracy and efficiency.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Artificial Intelligence
Kitsuchart Pasupa et al.
Summary: Evaluation of car damages after an accident is crucial in the car insurance industry. This study compared five deep learning algorithms for semantic segmentation of car parts, with HTC and ResNet-50 showing the best performance for instance segmentation across various types of cars. Additionally, GCNet demonstrated the highest robustness under different weather and lighting conditions.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yongsheng Dong et al.
Summary: In this paper, we propose a CartoonLossGAN based on cartoon loss for generating cartoon-style images. By reusing the encoder part of the discriminator and introducing a new loss function, the network can learn the smooth surface and coloring process of cartoon images, resulting in high-quality cartoon-style images. Furthermore, an initialization strategy is proposed to simplify and stabilize the model training.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Hongfeng You et al.
Summary: This paper proposes a simple and efficient dual-rotation network (DR-Net) to enhance the quality of both local and global feature maps for medical image segmentation with small samples. Experimental results show that the DR-Net method achieves higher segmentation accuracy on both the CHAOS and BraTS datasets compared to state-of-the-art methods.
COMPLEX & INTELLIGENT SYSTEMS
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Article
Computer Science, Artificial Intelligence
Yanfei Su et al.
Summary: This study introduces a large-scale point cloud benchmark dataset for building facade semantic segmentation and proposes a new attention module, DLA, to enhance the learning of local information from point clouds. The experimental results show that the proposed DLA-Net outperforms state-of-the-art methods for semantic segmentation.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Jie Jiang et al.
Summary: Recent studies have shown that utilizing contextual information can improve semantic segmentation and address the inconsistency in parsing predictions for large objects and ignorance of small objects. However, existing methods overlook the fact that different pixels may require different levels of context. In this paper, we propose a novel global-guided selective context network (GSCNet) that adaptively selects contextual information for scene parsing, achieving state-of-the-art performance on various challenging datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Civil
Hai Wang et al.
Summary: Considerable progress has been made in semantic segmentation of images in favorable environments in recent years, but the environmental perception of autonomous driving under adverse weather conditions remains challenging. This paper aims to explore image segmentation in low-light scenarios to expand the application range of autonomous vehicles. We propose a novel nighttime segmentation framework and demonstrate its effectiveness through experiments.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Pengcheng Li et al.
Summary: An accurate tooth identification and delineation method is proposed for dental CBCT images. A semantic graph-based approach is used to model the spatial associations between teeth and achieve precise delineation. The method demonstrates superior performance compared to other state-of-the-art approaches.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Computer Science, Artificial Intelligence
Zechao Li et al.
Summary: This study proposes a novel Context-based Tandem Network (CTNet) that explores spatial and channel contextual information for semantic segmentation. The CTNet demonstrates superior performance by adaptively integrating the results of two context modules, leading to improved learning representations.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Shubhankar Borse et al.
Summary: This paper presents a novel framework for integrating semantic and instance contexts for panoptic segmentation. By introducing a panoptic relational attention module, the framework is able to capture relations between semantic classes and instances as well as relations between these categories and spatial features. Evaluation on multiple panoptic segmentation benchmarks demonstrates considerable improvements achieved by this framework.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Article
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Summary: In this article, a multitask Res-U-Net model with an attention mechanism is proposed for the extraction of building roofs and whole building shapes from remote sensing images. An offset vector method is used to detect the footprints of high-rise buildings based on the boundaries of the corresponding building roofs and shapes. The online food delivery data is also utilized to parse the POI name of each building footprint. The experimental results show that the proposed model achieves the best performance among all baseline models in terms of building roof and shape segmentation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Jiangyun Li et al.
Summary: The encoder-decoder architecture is widely used in lightweight semantic segmentation networks, but it has limited performance compared to the Dilated-FCN model. To address the limitations in global contexts and noisy low-level features, a Global Enhancement Method and a Local Refinement Module are proposed and integrated into a Context Fusion Block, resulting in the Attention guided Global enhancement and Local refinement Network (AGLN).
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Geochemistry & Geophysics
Yuxing Zhao et al.
Summary: Distributed acoustic sensing (DAS) is a promising technology that offers advantages in well coverage, sampling density, and tolerance to harsh environments. However, the low signal-to-noise ratio (SNR) and various types of noise in vertical seismic profile (VSP) data obtained using DAS pose challenges in data interpretation. To address this, we propose a DAS VSP data denoiser based on convolutional neural network (CNN) that can effectively suppress multiple types of common noise in a convenient and efficient manner.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Quan Tang et al.
Summary: The paper proposes a novel Chained Context Aggregation Module (CAM) to enrich feature representation by capturing multi-scale contexts, and constructs the Chained Context Aggregation Network (CANet) which achieves state-of-the-art or competitive performance on six challenging datasets.
IMAGE AND VISION COMPUTING
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Qiule Sun et al.
Summary: This study introduces a new second-order encoding network (SoENet) to enhance the capability of context modeling by capturing second-order statistics in individual feature subspaces. Experimental results show that SoENet significantly outperforms other methods on several commonly used benchmarks and is competitive.
Article
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Jian Ji et al.
Summary: This article proposes a new semantic segmentation method that enhances the model's ability to locate object boundaries by introducing cascaded CRFs into the decoder and fusing the output with the last decoder's output, resulting in more accurate semantic segmentation results.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
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Yao Chen et al.
Summary: This paper introduces a novel method for heart sound segmentation based on convolutional long short-term memory (CLSTM) which directly uses audio recording as input, improving robustness and adaptability in processing HSS tasks. The algorithm does not require feature extraction in advance and demonstrates outstanding performance on real-world PCG datasets.
COMPLEX & INTELLIGENT SYSTEMS
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Shiyu Liu et al.
Summary: This paper introduces the Built-in Depth-Semantic Coupled Encoding (BDSCE) module to improve scene parsing performance by effectively utilizing the correlation between depth and semantic information. The proposed module greatly enhances scene parsing results, especially in categories with clear depth distinction. Furthermore, the module is extended to other urban scene semantic reasoning tasks with successful outcomes.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jingyuan Li et al.
Summary: The study proposed a DSC-Net model and an image preprocessing method, demonstrating improved performance in thymoma segmentation, enhancing accuracy and efficiency. Experimental results showed the robustness and stability of the DSC-Net model in segmentation of different patients and different types of thymoma classified by the WHO histological classification criteria.
Article
Computer Science, Artificial Intelligence
Zhen Zhou et al.
Summary: This paper introduces a self-attention feature fusion network for semantic segmentation, which improves performance by introducing vertical and horizontal compression attention modules and unequal channel pyramid pooling modules. The proposed model achieves high performance on the PASCAL VOC2012 and Cityscapes datasets.
Article
Computer Science, Information Systems
Shu Yang et al.
Summary: The paper introduces a novel attribute-aware feature encoding (AFE) module to a multi-task network for object recognition and segmentation, aiming to improve both semantic tasks by regularizing feature encoding with auxiliary attribute learning.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Guang Feng et al.
Summary: This study proposes an encoder fusion network (EFN) for multimodal feature learning between language and vision, utilizing a co-attention mechanism for parallel updates of multimodal features, and introducing a boundary enhancement module (BEM) to enhance the network's focus on fine structures. Experiment results on four benchmark datasets show that the approach achieves state-of-the-art performance without post-processing.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Article
Computer Science, Artificial Intelligence
Yingfeng Cai et al.
Summary: This study proposes a model for refining target main body segmentation based on the phenomenon of potential relevance among targets, achieving outstanding performance in semantic segmentation testing compared to other state-of-the-art models.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
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Qichuan Geng et al.
Summary: The proposed GPSNet method achieves good performance in semantic segmentation tasks by dynamically selecting receptive fields and aggregating dense semantic context information.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Geochemistry & Geophysics
Lei Ding et al.
Summary: Two proposed modules, Patch Attention Module (PAM) and Attention Embedding Module (AEM), enhance feature representation in remote sensing images by bridging the gap between high-level and low-level features. Experimental results show that integrating these modules into a baseline fully convolutional network greatly improves performance and outperforms other attention-based methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
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Henghui Ding et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
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Proceedings Paper
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2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
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Review
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