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visible image fusion","volume":"35","author":"Huang","year":"2025","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"403","DOI":"10.3390\/biomimetics8050403","article-title":"Robotic grasp detection network based on improved deformable convolution and spatial feature center mechanism","volume":"8","author":"Zou","year":"2023","journal-title":"Biomimetics"},{"key":"ref4","series-title":"Proceedings of the 2024 21st International Conference on Ubiquitous Robots (UR)","article-title":"Safety-optimized strategy for grasp detection in high-clutter scenarios","author":"Li","year":"2024 Jun 24\u201327"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/70.954765","article-title":"Computation of 3-D form-closure grasps","volume":"17","author":"Ding","year":"2001","journal-title":"IEEE Trans Robot Autom"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"10832","DOI":"10.1109\/TNNLS.2023.3244186","article-title":"A novel robotic pushing and grasping method based on vision transformer and convolution","volume":"35","author":"Yu","year":"2024","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1089\/big.2016.0042","article-title":"Recent data sets on object manipulation: a survey","volume":"4","author":"Huang","year":"2016","journal-title":"Big Data"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1109\/TNNLS.2021.3106299","article-title":"A survey on brain effective connectivity network learning","volume":"34","author":"Ji","year":"2023","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref9","series-title":"Proceedings of the 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)","article-title":"Grasping of unknown objects on a planar surface using a single depth image","author":"Suzuki","year":"2016 Jul 12\u201315"},{"key":"ref10","series-title":"Proceedings of the 5th IEEE-RAS International Conference on Humanoid Robots, 2005","article-title":"A shape matching algorithm for synthesizing humanlike enveloping grasps","author":"Li","year":"2005 Dec 5"},{"key":"ref11","series-title":"Proceedings of the 2012 IEEE International Conference on Robotics and Automation","article-title":"Template-based learning of grasp selection","author":"Herzog","year":"2012 May 14\u201318"},{"key":"ref12","series-title":"Proceedings of the 2011 IEEE International Conference on Robotics and Automation","article-title":"Efficient grasping from RGBD images: learning using a new rectangle representation","author":"Jiang","year":"2011 May 9\u201313"},{"key":"ref13","series-title":"Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA)","article-title":"A hybrid deep architecture for robotic grasp detection","author":"Guo","year":"2017 May 29\u2013Jun 3"},{"key":"ref14","series-title":"Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","article-title":"Fully convolutional grasp detection network with oriented anchor box","author":"Zhou","year":"2018 Oct 1\u20135"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/TRO.2013.2289018","article-title":"Data-driven grasp synthesis\u2014a survey","volume":"30","author":"Bohg","year":"2014","journal-title":"IEEE Trans Robot"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"7039","DOI":"10.1109\/TSMC.2024.3446841","article-title":"Robotic grasp detection using structure prior attention and multiscale features","volume":"54","author":"Chen","year":"2024","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1177\/0278364914549607","article-title":"Deep learning for detecting robotic grasps","volume":"34","author":"Lenz","year":"2015","journal-title":"Int J Robot Res"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref19","unstructured":"Cao H, Chen G, Li Z, Lin J, Knoll A. Lightweight convolutional neural network with Gaussian-based grasping representation for robotic grasping detection. arXiv:2101.10226. 2021."},{"key":"ref20","doi-asserted-by":"crossref","first-page":"244","DOI":"10.26599\/TST.2023.9010003","article-title":"Grasp detection with hierarchical multi-scale feature fusion and inverted shuffle residual","volume":"29","author":"Geng","year":"2024","journal-title":"Tsinghua Sci Technol"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"8293","DOI":"10.1109\/TNNLS.2022.3226772","article-title":"PHNNs: lightweight neural networks via parameterized hyper complex convolutions","volume":"35","author":"Grassucci","year":"2022","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1109\/TVCG.2015.2513408","article-title":"Pose estimation for augmented reality: a hands-on survey","volume":"22","author":"Marchand","year":"2016","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/0278364919859066","article-title":"Learning robust, real-time, reactive robotic grasping","volume":"39","author":"Morrison","year":"2020","journal-title":"Int J Robot Res"},{"key":"ref24","series-title":"Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","article-title":"Antipodal robotic grasping using generative residual convolutional neural network","author":"Kumra","year":"2021 Oct 24\u2013Jan 24"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"5238","DOI":"10.1109\/LRA.2022.3145064","article-title":"SE-ResUNet: a novel robotic grasp detection method","volume":"7","author":"Yu","year":"2022","journal-title":"IEEE Robot Autom Lett"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"4653","DOI":"10.3390\/app14114653","article-title":"ODGNet: robotic grasp detection network based on omni-dimensional dynamic convolution","volume":"14","author":"Kuang","year":"2024","journal-title":"Appl Sci"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"6696","DOI":"10.1109\/TII.2024.3353841","article-title":"Light-weight convolutional neural networks for generative robotic grasping","volume":"20","author":"Fu","year":"2024","journal-title":"IEEE Trans Ind Inform"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1109\/TMECH.2022.3209488","article-title":"CGNet: robotic grasp detection in heavily cluttered scenes","volume":"28","author":"Yu","year":"2023","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"7958","DOI":"10.3390\/s24247958","article-title":"Cascaded feature fusion grasping network for real-time robotic systems","volume":"24","author":"Li","year":"2024","journal-title":"Sensors"},{"key":"ref30","series-title":"Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 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Sens"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"8170","DOI":"10.1109\/LRA.2022.3187261","article-title":"When transformer meets robotic grasping: exploits context for efficient grasp detection","volume":"7","author":"Wang","year":"2022","journal-title":"IEEE Robot Autom Lett"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"109014","DOI":"10.1016\/j.compag.2024.109014","article-title":"End-to-end lightweight transformer-based neural network for grasp detection towards fruit robotic handling","volume":"221","author":"Guo","year":"2024","journal-title":"Comput Electron Agric"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"39206","DOI":"10.1109\/JSEN.2024.3449946","article-title":"MCT-grasp: a novel grasp detection using multimodal embedding and convolutional modulation transformer","volume":"24","author":"Yang","year":"2024","journal-title":"IEEE Sens J"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"4702","DOI":"10.1109\/LRA.2024.3381091","article-title":"DSNet: double strand robotic grasp detection network based on cross attention","volume":"9","author":"Zhang","year":"2024","journal-title":"IEEE Robot Autom Lett"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"e3","DOI":"10.23915\/distill.00003","article-title":"Deconvolution and checkerboard artifacts","volume":"1","author":"Odena","year":"2016","journal-title":"Distill"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"129866","DOI":"10.1016\/j.neucom.2025.129866","article-title":"SCSA: exploring the synergistic effects between spatial and channel attention","volume":"634","author":"Si","year":"2025","journal-title":"Neurocomputing"},{"key":"ref43","series-title":"Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Xception: deep learning with depthwise separable 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ELA: efficient local attention for deep convolutional neural networks. arXiv:2403.01123. 2024.","DOI":"10.1007\/s11554-025-01719-6"},{"key":"ref46","unstructured":"Mehta S, Rastegari M. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv:2110.02178. 2021."},{"key":"ref47","first-page":"11863","article-title":"SimAM: a simple, parameter-free attention module for convolutional neural networks","volume":"139","author":"Yang","year":"2021","journal-title":"Proc Mach Learn Res"},{"key":"ref48","series-title":"Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"EMCAD: efficient multi-scale convolutional attention decoding for medical image segmentation","author":"Rahman","year":"2024 Jun 16\u201322"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/LSP.2021.3138351","article-title":"Learning a contrast enhancer for intensity correction of remotely sensed images","volume":"29","author":"Huang","year":"2021","journal-title":"IEEE Signal Process Lett"},{"key":"ref50","series-title":"Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","article-title":"Jacquard: a large scale dataset for robotic grasp detection","author":"Depierre","year":"2018 Oct 1\u20135"},{"key":"ref51","unstructured":"Kingma DP, Ba J. 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