Clothing parsing, also known as clothing image segmentation, is the problem of assigning a clothing category label to each pixel in clothing images. To address the lack of positional and global prior in existing cloth...Clothing parsing, also known as clothing image segmentation, is the problem of assigning a clothing category label to each pixel in clothing images. To address the lack of positional and global prior in existing clothing parsing algorithms, this paper proposes an enhanced positional attention module(EPAM) to collect positional information in the vertical direction of each pixel, and an efficient global prior module(GPM) to aggregate contextual information from different sub-regions. The EPAM and GPM based residual network(EG-ResNet) could effectively exploit the intrinsic features of clothing images while capturing information between different scales and sub-regions. Experimental results show that the proposed EG-ResNet achieves promising performance in clothing parsing of the colorful fashion parsing dataset(CFPD)(51.12% of mean Intersection over Union(mIoU) and 92.79% of pixel-wise accuracy(PA)) compared with other state-of-the-art methods.展开更多
This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel an...This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.展开更多
Sparse rewards pose significant challenges in deep reinforcement learning as agents struggle to learn from experiences with limited reward signals.Hindsight experience replay(HER)addresses this problem by creating“sm...Sparse rewards pose significant challenges in deep reinforcement learning as agents struggle to learn from experiences with limited reward signals.Hindsight experience replay(HER)addresses this problem by creating“small goals”within a hierarchical decision model.However,HER does not consider the value of different episodes for agent learning.In this paper,we propose SPAHER,a framework for prioritizing hindsight experiences based on spatial position attention.SPAHER allows the agent to prioritize more valuable experiences in a manipulation task.It achieves this by calculating transition and trajectory spatial position functions to determine the value of each episode for experience replays.We evaluate SPAHER on eight robot manipulation tasks in the Fetch and Hand environments provided by OpenAI Gym.Simulation results show that our method improves the final mean success rate by an average of 3.63%compared to HER,especially in challenging Hand environments.Notably,these improvements are achieved without any increase in computation time.展开更多
In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of ex...In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information(CSI)feedback methods.Specifically,a bidirectional position attention module(BPAM)was designed in the BPANet to improve the network performance.The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information,thereby enhancing the feature representation of the CSI matrix.Furthermore,channel attention is decomposed into two one-dimensional(1D)feature encoding processes effectively reducing computational costs.Simulation results demonstrate that,compared with the existing representative method complex input lightweight neural network(CLNet),BPANet reduces computational complexity by an average of 19.4%and improves accuracy by an average of 7.1%.Additionally,it performs better in terms of running time delay and cosine similarity.展开更多
基金National Natural Science Foundation of China (No.62006039)Shanghai Special Fund for Software and Integrated Circuit Industry Development,China (No.180330)。
文摘Clothing parsing, also known as clothing image segmentation, is the problem of assigning a clothing category label to each pixel in clothing images. To address the lack of positional and global prior in existing clothing parsing algorithms, this paper proposes an enhanced positional attention module(EPAM) to collect positional information in the vertical direction of each pixel, and an efficient global prior module(GPM) to aggregate contextual information from different sub-regions. The EPAM and GPM based residual network(EG-ResNet) could effectively exploit the intrinsic features of clothing images while capturing information between different scales and sub-regions. Experimental results show that the proposed EG-ResNet achieves promising performance in clothing parsing of the colorful fashion parsing dataset(CFPD)(51.12% of mean Intersection over Union(mIoU) and 92.79% of pixel-wise accuracy(PA)) compared with other state-of-the-art methods.
基金support from the Ministry of Science and Tech-nology of the:People's Republic of China(Grant No.2021 YFB2600804)the Open Research Project Programme of the State Key Labor atory of Interet of Things for Smart City(University of Macao)(Grant No.SKL-IoTSC(UM)-2021-2023/ORPF/A19/2022)the General Research Fund(GRF)project(Grant No.15214722)from Research Grants Council(RGC)of Hong Kong Special Administrative Re gion Government of China are gratefully acknowledged.
文摘This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.
基金supported by the Natural Science Foundation of Shaanxi Province,China(No.2022JQ-661)the Project of Science and Technology Development Plan in Hangzhou,China(No.202202B38)the Xidian-FIAS International Joint Research Center,China.
文摘Sparse rewards pose significant challenges in deep reinforcement learning as agents struggle to learn from experiences with limited reward signals.Hindsight experience replay(HER)addresses this problem by creating“small goals”within a hierarchical decision model.However,HER does not consider the value of different episodes for agent learning.In this paper,we propose SPAHER,a framework for prioritizing hindsight experiences based on spatial position attention.SPAHER allows the agent to prioritize more valuable experiences in a manipulation task.It achieves this by calculating transition and trajectory spatial position functions to determine the value of each episode for experience replays.We evaluate SPAHER on eight robot manipulation tasks in the Fetch and Hand environments provided by OpenAI Gym.Simulation results show that our method improves the final mean success rate by an average of 3.63%compared to HER,especially in challenging Hand environments.Notably,these improvements are achieved without any increase in computation time.
基金supported by the National Natural Science Foundation of China(12005108)the Shandong Provincial Natural Science Foundation Youth Project(ZR2020QF016)the National Natural Science Foundation of China(U2006222)。
文摘In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information(CSI)feedback methods.Specifically,a bidirectional position attention module(BPAM)was designed in the BPANet to improve the network performance.The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information,thereby enhancing the feature representation of the CSI matrix.Furthermore,channel attention is decomposed into two one-dimensional(1D)feature encoding processes effectively reducing computational costs.Simulation results demonstrate that,compared with the existing representative method complex input lightweight neural network(CLNet),BPANet reduces computational complexity by an average of 19.4%and improves accuracy by an average of 7.1%.Additionally,it performs better in terms of running time delay and cosine similarity.
