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Yuichi Kimura - IEEE Xplore Author Profile

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Author details

Yuichi Kimura

Also published under: Y. Kimura

Publications

70

Citations

335

Publications by Year

19962024

Co-Authors:

A. AkiyamaM. AndoIbuki AsahinaYusuke AsanumaRidho Chayono

Show All Co-Authors (53)

Yuichi Kimura

Also published under: Y. Kimura

Affiliation

Graduate School of Science and Engineering

Saitama University

Saitama, Japan

Publication Topics

Microstrip Antenna,
Dielectric Substrate,
Feed Antenna,
Design Frequency,
Substrate Thickness,
Ground Plane,
Thin Substrate,
Antenna Design,
Cross-polarization,
Radiation Pattern,
Resonance Frequency,
Center Frequency

Biography

Yuichi Kimura (S'99–M'01) was born in Saitama, Japan, on November 18, 1973. He received the B.S., M.S., and D.E. degrees in electrical and electronic engineering from the Tokyo Institute of Technology, Tokyo, Japan, in 1996, 1998, and 2001, respectively.,He is currently a Research Associate at Saitama University, Saitama, Japan. His research interests include slotted waveguide array antennas and microstrip antennas.,Dr. Kimura is a Member of the Institute of Electronics, Information and Communication Engineers (IEICE), Japan.(Based on document published on 8 August 2005).

Publications

70

Citations

335

Publications by Year

19962024

Co-Authors:

A. Akiyama
M. Ando
Ibuki Asahina
Yusuke Asanuma
Ridho Chayono

Show All Co-Authors (53)

Author's Published Works

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Showing 1-25 of 40 results

Journals (26)

Conferences (14)

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College of Computer Science, Nankai University, Tianjin, China(7)
College of Artificial Intelligence, Nankai University, Tianjin, China(5)
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore(3)
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China(3)
TKLNDST, College of Computer Science, Nankai University, Nankai, China(3)
IEEE Transactions on Pattern Analysis and Machine Intelligence(13)
IEEE Transactions on Image Processing(3)
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Convolutional Neural Network(22)
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Results

Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution

Ao Li;Le Zhang;Yun Liu;Ce Zhu

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 4 | Journal Article |

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Transformer-based methods have exhibited remarkable potential in single image super-resolution (SISR) by effectively extracting long-range dependencies. However, most of the current research in this area has prioritized the design of transformer blocks to capture global information, while overlooking the importance of incorporating high-frequency priors, which we believe could be beneficial. In ou...Show More

Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution

Ao Li;Le Zhang;Yun Liu;Ce Zhu

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 4 | Journal Article |

PSRR-MaxpoolNMS++: Fast Non-Maximum Suppression With Discretization and Pooling

Tianyi Zhang;Chunyun Chen;Yun Liu;Xue Geng;Mohamed M. Sabry Aly;Jie Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 2 | Journal Article |

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Non-maximum suppression (NMS) is an essential post-processing step for object detection. The de-facto standard for NMS, namely GreedyNMS, is not parallelizable and could thus be the performance bottleneck in object detection pipelines. MaxpoolNMS is introduced as a fast and parallelizable alternative to GreedyNMS. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of...Show More

PSRR-MaxpoolNMS++: Fast Non-Maximum Suppression With Discretization and Pooling

Tianyi Zhang;Chunyun Chen;Yun Liu;Xue Geng;Mohamed M. Sabry Aly;Jie Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 2 | Journal Article |

Rethinking 3-D LiDAR Point Cloud Segmentation

Shijie Li;Yun Liu;Juergen Gall

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 3 | Journal Article |

Cited by: Papers (131)

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Many point-based semantic segmentation methods have been designed for indoor scenarios, but they struggle if they are applied to point clouds that are captured by a light detection and ranging (LiDAR) sensor in an outdoor environment. In order to make these methods more efficient and robust such that they can handle LiDAR data, we introduce the general concept of reformulating 3-D point-based oper...Show More

Rethinking 3-D LiDAR Point Cloud Segmentation

Shijie Li;Yun Liu;Juergen Gall

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 3 | Journal Article |

Global and Compact Video Context Embedding for Video Semantic Segmentation

Lei Sun;Yun Liu;Guolei Sun;Min Wu;Zhijie Xu;Kaiwei Wang;Luc van Gool

Year: 2024 | Volume: 12 | Journal Article |

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Intuitively, global video context could benefit video semantic segmentation (VSS) if it is designed to simultaneously model global temporal and spatial dependencies for a holistic understanding of the semantic scenes in a video clip. However, we found that the existing VSS approaches focus only on modeling local video context. This paper attempts to bridge this gap by learning global video context...Show More

Global and Compact Video Context Embedding for Video Semantic Segmentation

Lei Sun;Yun Liu;Guolei Sun;Min Wu;Zhijie Xu;Kaiwei Wang;Luc van Gool

Year: 2024 | Volume: 12 | Journal Article |

Learning Local and Global Temporal Contexts for Video Semantic Segmentation

Guolei Sun;Yun Liu;Henghui Ding;Min Wu;Luc Van Gool

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 10 | Journal Article |

Cited by: Papers (1)

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Contextual information plays a core role for video semantic segmentation (VSS). This paper summarizes contexts for VSS in two-fold: local temporal contexts (LTC) which define the contexts from neighboring frames, and global temporal contexts (GTC) which represent the contexts from the whole video. As for LTC, it includes static and motional contexts, corresponding to static and moving content in n...Show More

Learning Local and Global Temporal Contexts for Video Semantic Segmentation

Guolei Sun;Yun Liu;Henghui Ding;Min Wu;Luc Van Gool

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 10 | Journal Article |

Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots

Youqi Liao;Shuhao Kang;Jianping Li;Yang Liu;Yun Liu;Zhen Dong;Bisheng Yang;Xieyuanli Chen

IEEE Robotics and Automation Letters

Year: 2024 | Volume: 9, Issue: 4 | Journal Article |

Cited by: Papers (1)

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Precise and rapid delineation of sharp boundaries and robust semantics is essential for numerous downstream robotic tasks, such as robot grasping and manipulation, real-time semantic mapping, and online sensor calibration performed on edge computing units. Although boundary detection and semantic segmentation are complementary tasks, most studies focus on lightweight models for semantic segmentati...Show More

Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots

Youqi Liao;Shuhao Kang;Jianping Li;Yang Liu;Yun Liu;Zhen Dong;Bisheng Yang;Xieyuanli Chen

IEEE Robotics and Automation Letters

Year: 2024 | Volume: 9, Issue: 4 | Journal Article |

Revisiting Computer-Aided Tuberculosis Diagnosis

Yun Liu;Yu-Huan Wu;Shi-Chen Zhang;Li Liu;Min Wu;Ming-Ming Cheng

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 4 | Journal Article |

Cited by: Papers (7)

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Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. Recently, computer-aided tuberculosis diagnosis (CTD) using deep learning has shown promise, but progress is hindered by limited training data. To address this, w...Show More

Revisiting Computer-Aided Tuberculosis Diagnosis

Yun Liu;Yu-Huan Wu;Shi-Chen Zhang;Li Liu;Min Wu;Ming-Ming Cheng

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 4 | Journal Article |

Boosting Salient Object Detection With Transformer-Based Asymmetric Bilateral U-Net

Yu Qiu;Yun Liu;Le Zhang;Haotian Lu;Jing Xu

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 4 | Journal Article |

Cited by: Papers (15)

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Existing salient object detection (SOD) methods mainly rely on U-shaped convolution neural networks (CNNs) with skip connections to combine the global contexts and local spatial details that are crucial for locating salient objects and refining object details, respectively. Despite great successes, the ability of CNNs in learning global contexts is limited. Recently, the vision transformer has ach...Show More

Boosting Salient Object Detection With Transformer-Based Asymmetric Bilateral U-Net

Yu Qiu;Yun Liu;Le Zhang;Haotian Lu;Jing Xu

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 4 | Journal Article |

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation

Yu Qiu;Yun Liu;Shijie Li;Jing Xu

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 6 | Journal Article |

Cited by: Papers (5)

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The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected area segmentation from computed tomography (CT) image, has attracted much attention by serving as an adjunct to increase the accuracy of COVID-19 screening and clinical diagnosis. Although lesion segmentation i...Show More

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation

Yu Qiu;Yun Liu;Shijie Li;Jing Xu

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 6 | Journal Article |

Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution

Ao Li;Le Zhang;Yun Liu;Ce Zhu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (29)

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Transformer-based methods have exhibited remarkable potential in single image super-resolution (SISR) by effectively extracting long-range dependencies. However, most of the current research in this area has prioritized the design of transformer blocks to capture global information, while overlooking the importance of incorporating high-frequency priors, which we believe could be beneficial. In ou...Show More

Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution

Ao Li;Le Zhang;Yun Liu;Ce Zhu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Indiscernible Object Counting in Underwater Scenes

Guolei Sun;Zhaochong An;Yun Liu;Ce Liu;Christos Sakaridis;Deng-Ping Fan;Luc Van Gool

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (12)

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Recently, indiscernible scene understanding has attracted a lot of attention in the vision community. We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundings. Due to a lack of appropriate IOC datasets, we present a large-scale dataset...Show More

Indiscernible Object Counting in Underwater Scenes

Guolei Sun;Zhaochong An;Yun Liu;Ce Liu;Christos Sakaridis;Deng-Ping Fan;Luc Van Gool

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

LSDIR: A Large Scale Dataset for Image Restoration

Yawei Li;Kai Zhang;Jingyun Liang;Jiezhang Cao;Ce Liu;Rui Gong;Yulun Zhang;Hao Tang;Yun Liu;Denis Demandolx;Rakesh Ranjan;Radu Timofte;Luc Van Gool

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Cited by: Papers (37)

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The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks. The datasets used to train these networks ‘only’ contain some thousands of images, which is still incomparable with the large scale datasets for other vision tasks such as visual recognition and object detection. The small...Show More

LSDIR: A Large Scale Dataset for Image Restoration

Yawei Li;Kai Zhang;Jingyun Liang;Jiezhang Cao;Ce Liu;Rui Gong;Yulun Zhang;Hao Tang;Yun Liu;Denis Demandolx;Rakesh Ranjan;Radu Timofte;Luc Van Gool

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

P2T: Pyramid Pooling Transformer for Scene Understanding

Yu-Huan Wu;Yun Liu;Xin Zhan;Ming-Ming Cheng

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 11 | Journal Article |

Cited by: Papers (123)

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Recently, the vision transformer has achieved great success by pushing the state-of-the-art of various vision tasks. One of the most challenging problems in the vision transformer is that the large sequence length of image tokens leads to high computational cost (quadratic complexity). A popular solution to this problem is to use a single pooling operation to reduce the sequence length. This paper...Show More

P2T: Pyramid Pooling Transformer for Scene Understanding

Yu-Huan Wu;Yun Liu;Xin Zhan;Ming-Ming Cheng

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 11 | Journal Article |

A2SPPNet: Attentive Atrous Spatial Pyramid Pooling Network for Salient Object Detection

Yu Qiu;Yun Liu;Yanan Chen;Jianwen Zhang;Jinchao Zhu;Jing Xu

IEEE Transactions on Multimedia

Year: 2023 | Volume: 25 | Journal Article |

Cited by: Papers (19)

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Recent progress in salient object detection (SOD) mainly depends on the Atrous Spatial Pyramid Pooling (ASPP) module for multi-scale learning. Intuitively, different input images, different pixels, and different network layers may have different preferences for various feature scales. However, ASPP treats all feature scales as equally important by a simple sum operation. To this end, we propose At...Show More

A2SPPNet: Attentive Atrous Spatial Pyramid Pooling Network for Salient Object Detection

Yu Qiu;Yun Liu;Yanan Chen;Jianwen Zhang;Jinchao Zhu;Jing Xu

IEEE Transactions on Multimedia

Year: 2023 | Volume: 25 | Journal Article |

MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation

Shijie Li;Yazan Abu Farha;Yun Liu;Ming-Ming Cheng;Juergen Gall

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 6 | Journal Article |

Cited by: Papers (140)

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With the success of deep learning in classifying short trimmed videos, more attention has been focused on temporally segmenting and classifying activities in long untrimmed videos. State-of-the-art approaches for action segmentation utilize several layers of temporal convolution and temporal pooling. Despite the capabilities of these approaches in capturing temporal dependencies, their predictions...Show More

MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation

Shijie Li;Yazan Abu Farha;Yun Liu;Ming-Ming Cheng;Juergen Gall

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 6 | Journal Article |

Coarse-to-Fine Feature Mining for Video Semantic Segmentation

Guolei Sun;Yun Liu;Henghui Ding;Thomas Probst;Luc Van Gool

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (33)

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The contextual information plays a core role in semantic segmentation. As for video semantic segmentation, the contexts include static contexts and motional contexts, corresponding to static content and moving content in a video clip, respectively. The static contexts are well exploited in image semantic segmentation by learning multi-scale and global/long-range features. The motional contexts are...Show More

Coarse-to-Fine Feature Mining for Video Semantic Segmentation

Guolei Sun;Yun Liu;Henghui Ding;Thomas Probst;Luc Van Gool

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

EDN: Salient Object Detection via Extremely-Downsampled Network

Yu-Huan Wu;Yun Liu;Le Zhang;Ming-Ming Cheng;Bo Ren

IEEE Transactions on Image Processing

Year: 2022 | Volume: 31 | Journal Article |

Cited by: Papers (114)

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Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features collaborate in locating salient objects and discovering fine details, respectively. However, most efforts are devoted to low-level feature learning by fusing multi-scale features or enhancing boundary representations. High-level features, which although have long...Show More

EDN: Salient Object Detection via Extremely-Downsampled Network

Yu-Huan Wu;Yun Liu;Le Zhang;Ming-Ming Cheng;Bo Ren

IEEE Transactions on Image Processing

Year: 2022 | Volume: 31 | Journal Article |

MobileSal: Extremely Efficient RGB-D Salient Object Detection

Yu-Huan Wu;Yun Liu;Jun Xu;Jia-Wang Bian;Yu-Chao Gu;Ming-Ming Cheng

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 12 | Journal Article |

Cited by: Papers (80)

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The high computational cost of neural networks has prevented recent successes in RGB-D salient object detection (SOD) from benefiting real-world applications. Hence, this article introduces a novel network, MobileSal, which focuses on efficient RGB-D SOD using mobile networks for deep feature extraction. However, mobile networks are less powerful in feature representation than cumbersome networks....Show More

MobileSal: Extremely Efficient RGB-D Salient Object Detection

Yu-Huan Wu;Yun Liu;Jun Xu;Jia-Wang Bian;Yu-Chao Gu;Ming-Ming Cheng

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 12 | Journal Article |

Multi-Scale Interaction for Real-Time LiDAR Data Segmentation on an Embedded Platform

Shijie Li;Xieyuanli Chen;Yun Liu;Dengxin Dai;Cyrill Stachniss;Juergen Gall

IEEE Robotics and Automation Letters

Year: 2022 | Volume: 7, Issue: 2 | Journal Article |

Cited by: Papers (48)

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Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles and robots, which are usually equipped with an embedded platform and have limited computational resources. Approaches that operate directly on the point cloud use complex spatial aggregation operations, which are very expensive and difficult to deploy on embedded platforms. As an alternative, projection-base...Show More

Multi-Scale Interaction for Real-Time LiDAR Data Segmentation on an Embedded Platform

Shijie Li;Xieyuanli Chen;Yun Liu;Dengxin Dai;Cyrill Stachniss;Juergen Gall

IEEE Robotics and Automation Letters

Year: 2022 | Volume: 7, Issue: 2 | Journal Article |

DNA: Deeply Supervised Nonlinear Aggregation for Salient Object Detection

Yun Liu;Ming-Ming Cheng;Xin-Yu Zhang;Guang-Yu Nie;Meng Wang

IEEE Transactions on Cybernetics

Year: 2022 | Volume: 52, Issue: 7 | Journal Article |

Cited by: Papers (59)

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Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multiscale convolutional features in convolutional neural networks (CNNs). Many popular methods impose deep supervision to perform side-output predictions that are linearly aggregated for final saliency prediction. In this article, we theoretically and experimentally demonstrate that linear aggregati...Show More

DNA: Deeply Supervised Nonlinear Aggregation for Salient Object Detection

Yun Liu;Ming-Ming Cheng;Xin-Yu Zhang;Guang-Yu Nie;Meng Wang

IEEE Transactions on Cybernetics

Year: 2022 | Volume: 52, Issue: 7 | Journal Article |

Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation

Yun Liu;Yu-Huan Wu;Peisong Wen;Yujun Shi;Yu Qiu;Ming-Ming Cheng

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 3 | Journal Article |

Cited by: Papers (92)

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Weakly supervised semantic instance segmentation with only image-level supervision, instead of relying on expensive pixel-wise masks or bounding box annotations, is an important problem to alleviate the data-hungry nature of deep learning. In this article, we tackle this challenging problem by aggregating the image-level information of all training images into a large knowledge graph and exploitin...Show More

Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation

Yun Liu;Yu-Huan Wu;Peisong Wen;Yujun Shi;Yu Qiu;Ming-Ming Cheng

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 3 | Journal Article |

DOTS: Decoupling Operation and Topology in Differentiable Architecture Search

Yu-Chao Gu;Li-Juan Wang;Yun Liu;Yi Yang;Yu-Huan Wu;Shao-Ping Lu;Ming-Ming Cheng

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (27)

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Differentiable Architecture Search (DARTS) has attracted extensive attention due to its efficiency in searching for cell structures. DARTS mainly focuses on the operation search and derives the cell topology from the operation weights. However, the operation weights can not indicate the importance of cell topology and result in poor topology rating correctness. To tackle this, we propose to Decoup...Show More

DOTS: Decoupling Operation and Topology in Differentiable Architecture Search

Yu-Chao Gu;Li-Juan Wang;Yun Liu;Yi Yang;Yu-Huan Wu;Shao-Ping Lu;Ming-Ming Cheng

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Regularized Densely-Connected Pyramid Network for Salient Instance Segmentation

Yu-Huan Wu;Yun Liu;Le Zhang;Wang Gao;Ming-Ming Cheng

IEEE Transactions on Image Processing

Year: 2021 | Volume: 30 | Journal Article |

Cited by: Papers (25)

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Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance. To better use the rich feature hierarchies in deep networks and enhance the s...Show More

Regularized Densely-Connected Pyramid Network for Salient Instance Segmentation

Yu-Huan Wu;Yun Liu;Le Zhang;Wang Gao;Ming-Ming Cheng

IEEE Transactions on Image Processing

Year: 2021 | Volume: 30 | Journal Article |

SAMNet: Stereoscopically Attentive Multi-Scale Network for Lightweight Salient Object Detection

Yun Liu;Xin-Yu Zhang;Jia-Wang Bian;Le Zhang;Ming-Ming Cheng

IEEE Transactions on Image Processing

Year: 2021 | Volume: 30 | Journal Article |

Cited by: Papers (161)

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Recent progress on salient object detection (SOD) mostly benefits from the explosive development of Convolutional Neural Networks (CNNs). However, much of the improvement comes with the larger network size and heavier computation overhead, which, in our view, is not mobile-friendly and thus difficult to deploy in practice. To promote more practical SOD systems, we introduce a novel Stereoscopicall...Show More

SAMNet: Stereoscopically Attentive Multi-Scale Network for Lightweight Salient Object Detection

Yun Liu;Xin-Yu Zhang;Jia-Wang Bian;Le Zhang;Ming-Ming Cheng

IEEE Transactions on Image Processing

Year: 2021 | Volume: 30 | Journal Article |

Lightweight Salient Object Detection via Hierarchical Visual Perception Learning

Yun Liu;Yu-Chao Gu;Xin-Yu Zhang;Weiwei Wang;Ming-Ming Cheng

IEEE Transactions on Cybernetics

Year: 2021 | Volume: 51, Issue: 9 | Journal Article |

Cited by: Papers (86)

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Recently, salient object detection (SOD) has witnessed vast progress with the rapid development of convolutional neural networks (CNNs). However, the improvement of SOD accuracy comes with the increase in network depth and width, resulting in large network size and heavy computational overhead. This prevents state-of-the-art SOD methods from being deployed into practical platforms, especially mobi...Show More

Lightweight Salient Object Detection via Hierarchical Visual Perception Learning

Yun Liu;Yu-Chao Gu;Xin-Yu Zhang;Weiwei Wang;Ming-Ming Cheng

IEEE Transactions on Cybernetics

Year: 2021 | Volume: 51, Issue: 9 | Journal Article |

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