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| <html> | |
| <head> | |
| <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/vis/4.16.1/vis.css" type="text/css" /> | |
| <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/vis/4.16.1/vis-network.min.js"> </script> | |
| <center> | |
| <h1></h1> | |
| </center> | |
| <!-- <link rel="stylesheet" href="../node_modules/vis/dist/vis.min.css" type="text/css" /> | |
| <script type="text/javascript" src="../node_modules/vis/dist/vis.js"> </script>--> | |
| <style type="text/css"> | |
| #mynetwork { | |
| width: 500px; | |
| height: 500px; | |
| background-color: #ffffff; | |
| border: 1px solid lightgray; | |
| position: relative; | |
| float: left; | |
| } | |
| #loadingBar { | |
| position:absolute; | |
| top:0px; | |
| left:0px; | |
| width: 500px; | |
| height: 500px; | |
| background-color:rgba(200,200,200,0.8); | |
| -webkit-transition: all 0.5s ease; | |
| -moz-transition: all 0.5s ease; | |
| -ms-transition: all 0.5s ease; | |
| -o-transition: all 0.5s ease; | |
| transition: all 0.5s ease; | |
| opacity:1; | |
| } | |
| #bar { | |
| position:absolute; | |
| top:0px; | |
| left:0px; | |
| width:20px; | |
| height:20px; | |
| margin:auto auto auto auto; | |
| border-radius:11px; | |
| border:2px solid rgba(30,30,30,0.05); | |
| background: rgb(0, 173, 246); /* Old browsers */ | |
| box-shadow: 2px 0px 4px rgba(0,0,0,0.4); | |
| } | |
| #border { | |
| position:absolute; | |
| top:10px; | |
| left:10px; | |
| width:500px; | |
| height:23px; | |
| margin:auto auto auto auto; | |
| box-shadow: 0px 0px 4px rgba(0,0,0,0.2); | |
| border-radius:10px; | |
| } | |
| #text { | |
| position:absolute; | |
| top:8px; | |
| left:530px; | |
| width:30px; | |
| height:50px; | |
| margin:auto auto auto auto; | |
| font-size:22px; | |
| color: #000000; | |
| } | |
| div.outerBorder { | |
| position:relative; | |
| top:400px; | |
| width:600px; | |
| height:44px; | |
| margin:auto auto auto auto; | |
| border:8px solid rgba(0,0,0,0.1); | |
| background: rgb(252,252,252); /* Old browsers */ | |
| background: -moz-linear-gradient(top, rgba(252,252,252,1) 0%, rgba(237,237,237,1) 100%); /* FF3.6+ */ | |
| background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,rgba(252,252,252,1)), color-stop(100%,rgba(237,237,237,1))); /* Chrome,Safari4+ */ | |
| background: -webkit-linear-gradient(top, rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* Chrome10+,Safari5.1+ */ | |
| background: -o-linear-gradient(top, rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* Opera 11.10+ */ | |
| background: -ms-linear-gradient(top, rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* IE10+ */ | |
| background: linear-gradient(to bottom, rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* W3C */ | |
| filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#fcfcfc', endColorstr='#ededed',GradientType=0 ); /* IE6-9 */ | |
| border-radius:72px; | |
| box-shadow: 0px 0px 10px rgba(0,0,0,0.2); | |
| } | |
| #config { | |
| float: left; | |
| width: 400px; | |
| height: 600px; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div id = "mynetwork"></div> | |
| <div id="loadingBar"> | |
| <div class="outerBorder"> | |
| <div id="text">0%</div> | |
| <div id="border"> | |
| <div id="bar"></div> | |
| </div> | |
| </div> | |
| </div> | |
| <div id = "config"></div> | |
| <script type="text/javascript"> | |
| // initialize global variables. | |
| var edges; | |
| var nodes; | |
| var network; | |
| var container; | |
| var options, data; | |
| // This method is responsible for drawing the graph, returns the drawn network | |
| function drawGraph() { | |
| var container = document.getElementById('mynetwork'); | |
| // parsing and collecting nodes and edges from the python | |
| nodes = new vis.DataSet([{"authors": "Z Zou, Z Shi, Y Guo, J Ye", "cited_by": 629, "cited_by_url": "https://scholar.google.com/scholar?cites=1195754158484278880\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "1195754158484278880", "label": "Object detection in 20 years: A survey", "shape": "dot", "size": 10, "title": "Object detection in 20 years: A survey", "url": "https://arxiv.org/abs/1905.05055", "year": "2019"}, {"authors": "L Liu, W Ouyang, X Wang, P Fieguth, J Chen\u2026", "cited_by": 1384, "cited_by_url": "https://scholar.google.com/scholar?cites=2846334625875347669\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "2846334625875347669", "label": "Deep learning for generic object detection: A survey", "shape": "dot", "size": 10, "title": "Deep learning for generic object detection: A survey", "url": "https://link.springer.com/article/10.1007/s11263-019-01247-4", "year": "2020"}, {"authors": "P Zhou, B Ni, C Geng, J Hu\u2026", "cited_by": 250, "cited_by_url": "https://scholar.google.com/scholar?cites=14842674417993046386\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "14842674417993046386", "label": "Scale-transferrable object detection", "shape": "dot", "size": 10, "title": "Scale-transferrable object detection", "url": "http://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_Scale-Transferrable_Object_Detection_CVPR_2018_paper.html", "year": "2018"}, {"authors": "P Hu, D Ramanan", "cited_by": 730, "cited_by_url": "https://scholar.google.com/scholar?cites=5935623751055355825\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "5935623751055355825", "label": "Finding tiny faces", "shape": "dot", "size": 10, "title": "Finding tiny faces", "url": "http://openaccess.thecvf.com/content_cvpr_2017/html/Hu_Finding_Tiny_Faces_CVPR_2017_paper.html", "year": "2017"}, {"authors": "J Gu, Z Wang, J Kuen, L Ma, A Shahroudy, B Shuai\u2026", "cited_by": 2608, "cited_by_url": "https://scholar.google.com/scholar?cites=6545447824154534971\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "6545447824154534971", "label": "Recent advances in convolutional neural networks", "shape": "dot", "size": 10, "title": "Recent advances in convolutional neural networks", "url": "https://www.sciencedirect.com/science/article/pii/S0031320317304120", "year": "2018"}, {"authors": "L Jiao, F Zhang, F Liu, S Yang, L Li, Z Feng\u2026", "cited_by": 533, "cited_by_url": "https://scholar.google.com/scholar?cites=5669364687087032958\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "5669364687087032958", "label": "A survey of deep learning-based object detection", "shape": "dot", "size": 10, "title": "A survey of deep learning-based object detection", "url": "https://ieeexplore.ieee.org/abstract/document/8825470/", "year": "2019"}, {"authors": "G Ghiasi, TY Lin, QV Le", "cited_by": 735, "cited_by_url": "https://scholar.google.com/scholar?cites=17367410418412289203\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "17367410418412289203", "label": "Nas-fpn: Learning scalable feature pyramid architecture for object detection", "shape": "dot", "size": 10, "title": "Nas-fpn: Learning scalable feature pyramid architecture for object detection", "url": "http://openaccess.thecvf.com/content_CVPR_2019/html/Ghiasi_NAS-FPN_Learning_Scalable_Feature_Pyramid_Architecture_for_Object_Detection_CVPR_2019_paper.html", "year": "2019"}, {"authors": "Q Zhao, T Sheng, Y Wang, Z Tang, Y Chen\u2026", "cited_by": 461, "cited_by_url": "https://scholar.google.com/scholar?cites=13359963855282714219\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "13359963855282714219", "label": "M2det: A single-shot object detector based on multi-level feature pyramid network", "shape": "dot", "size": 10, "title": "M2det: A single-shot object detector based on multi-level feature pyramid network", "url": "https://ojs.aaai.org/index.php/AAAI/article/view/4962", "year": "2019"}, {"authors": "X Zhu, H Hu, S Lin, J Dai", "cited_by": 804, "cited_by_url": "https://scholar.google.com/scholar?cites=7168214980579319728\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "7168214980579319728", "label": "Deformable convnets v2: More deformable, better results", "shape": "dot", "size": 10, "title": "Deformable convnets v2: More deformable, better results", "url": "http://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Deformable_ConvNets_V2_More_Deformable_Better_Results_CVPR_2019_paper.html", "year": "2019"}, {"authors": "J Pang, K Chen, J Shi, H Feng\u2026", "cited_by": 660, "cited_by_url": "https://scholar.google.com/scholar?cites=13559955005752027345\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "13559955005752027345", "label": "Libra r-cnn: Towards balanced learning for object detection", "shape": "dot", "size": 10, "title": "Libra r-cnn: Towards balanced learning for object detection", "url": "http://openaccess.thecvf.com/content_CVPR_2019/html/Pang_Libra_R-CNN_Towards_Balanced_Learning_for_Object_Detection_CVPR_2019_paper.html", "year": "2019"}, {"authors": "X Wu, D Sahoo, SCH Hoi", "cited_by": 295, "cited_by_url": "https://scholar.google.com/scholar?cites=11408603882397693774\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "11408603882397693774", "label": "Recent advances in deep learning for object detection", "shape": "dot", "size": 10, "title": "Recent advances in deep learning for object detection", "url": "https://www.sciencedirect.com/science/article/pii/S0925231220301430", "year": "2020"}, {"authors": "ZQ Zhao, P Zheng, S Xu, X Wu", "cited_by": 2316, "cited_by_url": "https://scholar.google.com/scholar?cites=13346469121565650680\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "13346469121565650680", "label": "Object detection with deep learning: A review", "shape": "dot", "size": 10, "title": "Object detection with deep learning: A review", "url": "https://ieeexplore.ieee.org/abstract/document/8627998/", "year": "2019"}, {"authors": "L Alzubaidi, J Zhang, AJ Humaidi, A Al-Dujaili\u2026", "cited_by": 278, "cited_by_url": "https://scholar.google.com/scholar?cites=8136644755673586417\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "8136644755673586417", "label": "Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions", "shape": "dot", "size": 10, "title": "Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions", "url": "https://link.springer.com/article/10.1186/s40537-021-00444-8", "year": "2021"}, {"authors": "J Wang, K Sun, T Cheng, B Jiang\u2026", "cited_by": 918, "cited_by_url": "https://scholar.google.com/scholar?cites=15505711795824202735\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "15505711795824202735", "label": "Deep high-resolution representation learning for visual recognition", "shape": "dot", "size": 10, "title": "Deep high-resolution representation learning for visual recognition", "url": "https://ieeexplore.ieee.org/abstract/document/9052469/", "year": "2020"}, {"authors": "T Hoeser, C Kuenzer", "cited_by": 113, "cited_by_url": "https://scholar.google.com/scholar?cites=16601259166110264431\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "16601259166110264431", "label": "Object detection and image segmentation with deep learning on earth observation data: A review-part i: Evolution and recent trends", "shape": "dot", "size": 10, "title": "Object detection and image segmentation with deep learning on earth observation data: A review-part i: Evolution and recent trends", "url": "https://www.mdpi.com/723500", "year": "2020"}, {"authors": "K Tong, Y Wu, F Zhou", "cited_by": 114, "cited_by_url": "https://scholar.google.com/scholar?cites=17642928594905005472\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "17642928594905005472", "label": "Recent advances in small object detection based on deep learning: A review", "shape": "dot", "size": 10, "title": "Recent advances in small object detection based on deep learning: A review", "url": "https://www.sciencedirect.com/science/article/pii/S0262885620300421", "year": "2020"}, {"authors": "P Adarsh, P Rathi, M Kumar", "cited_by": 104, "cited_by_url": "https://scholar.google.com/scholar?cites=12435210238474843309\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "12435210238474843309", "label": "YOLO v3-Tiny: Object Detection and Recognition using one stage improved model", "shape": "dot", "size": 10, "title": "YOLO v3-Tiny: Object Detection and Recognition using one stage improved model", "url": "https://ieeexplore.ieee.org/abstract/document/9074315/", "year": "2020"}, {"authors": "P Bateni, R Goyal, V Masrani\u2026", "cited_by": 70, "cited_by_url": "https://scholar.google.com/scholar?cites=10179566061976639313\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "10179566061976639313", "label": "Improved few-shot visual classification", "shape": "dot", "size": 10, "title": "Improved few-shot visual classification", "url": "http://openaccess.thecvf.com/content_CVPR_2020/html/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.html", "year": "2020"}, {"authors": "A Kumar, TK Marks, W Mou, Y Wang\u2026", "cited_by": 59, "cited_by_url": "https://scholar.google.com/scholar?cites=10496106229213355393\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "10496106229213355393", "label": "LUVLi Face Alignment: Estimating Landmarks\u0027 Location, Uncertainty, and Visibility Likelihood", "shape": "dot", "size": 10, "title": "LUVLi Face Alignment: Estimating Landmarks\u0027 Location, Uncertainty, and Visibility Likelihood", "url": "http://openaccess.thecvf.com/content_CVPR_2020/html/Kumar_LUVLi_Face_Alignment_Estimating_Landmarks_Location_Uncertainty_and_Visibility_Likelihood_CVPR_2020_paper.html", "year": "2020"}, {"authors": "JP Queralta, J Taipalmaa, BC Pullinen, VK Sarker\u2026", "cited_by": 65, "cited_by_url": "https://scholar.google.com/scholar?cites=5559195703472982509\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "5559195703472982509", "label": "Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision", "shape": "dot", "size": 10, "title": "Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision", "url": "https://ieeexplore.ieee.org/abstract/document/9220149/", "year": "2020"}, {"authors": "E Meijering", "cited_by": 51, "cited_by_url": "https://scholar.google.com/scholar?cites=6929006081707924223\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "6929006081707924223", "label": "A bird\u0027s-eye view of deep learning in bioimage analysis", "shape": "dot", "size": 10, "title": "A bird\u0027s-eye view of deep learning in bioimage analysis", "url": "https://www.sciencedirect.com/science/article/pii/S2001037020303561", "year": "2020"}, {"authors": "RI Hasan, SM Yusuf, L Alzubaidi", "cited_by": 46, "cited_by_url": "https://scholar.google.com/scholar?cites=2994117983880350781\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "2994117983880350781", "label": "Review of the state of the art of deep learning for plant diseases: a broad analysis and discussion", "shape": "dot", "size": 10, "title": "Review of the state of the art of deep learning for plant diseases: a broad analysis and discussion", "url": "https://www.mdpi.com/845654", "year": "2020"}, {"authors": "K Oksuz, BC Cam, S Kalkan\u2026", "cited_by": 165, "cited_by_url": "https://scholar.google.com/scholar?cites=8762454778937977659\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "8762454778937977659", "label": "Imbalance problems in object detection: A review", "shape": "dot", "size": 10, "title": "Imbalance problems in object detection: A review", "url": "https://ieeexplore.ieee.org/abstract/document/9042296/", "year": "2020"}, {"authors": "G Ghiasi, Y Cui, A Srinivas, R Qian\u2026", "cited_by": 164, "cited_by_url": "https://scholar.google.com/scholar?cites=2321980635951558135\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "2321980635951558135", "label": "Simple copy-paste is a strong data augmentation method for instance segmentation", "shape": "dot", "size": 10, "title": "Simple copy-paste is a strong data augmentation method for instance segmentation", "url": "http://openaccess.thecvf.com/content/CVPR2021/html/Ghiasi_Simple_Copy-Paste_Is_a_Strong_Data_Augmentation_Method_for_Instance_CVPR_2021_paper.html", "year": "2021"}, {"authors": "S Qiao, LC Chen, A Yuille", "cited_by": 212, "cited_by_url": "https://scholar.google.com/scholar?cites=3466946125865762121\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "3466946125865762121", "label": "Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution", "shape": "dot", "size": 10, "title": "Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution", "url": "http://openaccess.thecvf.com/content/CVPR2021/html/Qiao_DetectoRS_Detecting_Objects_With_Recursive_Feature_Pyramid_and_Switchable_Atrous_CVPR_2021_paper.html", "year": "2021"}, {"authors": "L Xiang, G Ding, J Han", "cited_by": 57, "cited_by_url": "https://scholar.google.com/scholar?cites=10072224309423455263\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "10072224309423455263", "label": "Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification", "shape": "dot", "size": 10, "title": "Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification", "url": "https://link.springer.com/chapter/10.1007/978-3-030-58558-7_15", "year": "2020"}, {"authors": "ND Nguyen, T Do, TD Ngo, DD Le", "cited_by": 62, "cited_by_url": "https://scholar.google.com/scholar?cites=9316980701966772208\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "9316980701966772208", "label": "An evaluation of deep learning methods for small object detection", "shape": "dot", "size": 10, "title": "An evaluation of deep learning methods for small object detection", "url": "https://www.hindawi.com/journals/jece/2020/3189691/", "year": "2020"}, {"authors": "E Ben-Baruch, T Ridnik, N Zamir, A Noy\u2026", "cited_by": 54, "cited_by_url": "https://scholar.google.com/scholar?cites=14439068122437787179\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "14439068122437787179", "label": "Asymmetric loss for multi-label classification", "shape": "dot", "size": 10, "title": "Asymmetric loss for multi-label classification", "url": "https://arxiv.org/abs/2009.14119", "year": "2020"}, {"authors": "Y Liu, P Sun, N Wergeles, Y Shang", "cited_by": 51, "cited_by_url": "https://scholar.google.com/scholar?cites=17430652832439326106\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en", "id": "17430652832439326106", "label": "A survey and performance evaluation of deep learning methods for small object detection", "shape": "dot", "size": 10, "title": "A 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| // adding nodes and edges to the graph | |
| data = {nodes: nodes, edges: edges}; | |
| var options = { | |
| "configure": { | |
| "enabled": true, | |
| "filter": [ | |
| "physics" | |
| ] | |
| }, | |
| "edges": { | |
| "color": { | |
| "inherit": true | |
| }, | |
| "smooth": { | |
| "enabled": false, | |
| "type": "continuous" | |
| } | |
| }, | |
| "interaction": { | |
| "dragNodes": true, | |
| "hideEdgesOnDrag": false, | |
| "hideNodesOnDrag": false | |
| }, | |
| "physics": { | |
| "enabled": true, | |
| "stabilization": { | |
| "enabled": true, | |
| "fit": true, | |
| "iterations": 1000, | |
| "onlyDynamicEdges": false, | |
| "updateInterval": 50 | |
| } | |
| } | |
| }; | |
| // if this network requires displaying the configure window, | |
| // put it in its div | |
| options.configure["container"] = document.getElementById("config"); | |
| network = new vis.Network(container, data, options); | |
| network.on("stabilizationProgress", function(params) { | |
| document.getElementById('loadingBar').removeAttribute("style"); | |
| var maxWidth = 496; | |
| var minWidth = 20; | |
| var widthFactor = params.iterations/params.total; | |
| var width = Math.max(minWidth,maxWidth * widthFactor); | |
| document.getElementById('bar').style.width = width + 'px'; | |
| document.getElementById('text').innerHTML = Math.round(widthFactor*100) + '%'; | |
| }); | |
| network.once("stabilizationIterationsDone", function() { | |
| document.getElementById('text').innerHTML = '100%'; | |
| document.getElementById('bar').style.width = '496px'; | |
| document.getElementById('loadingBar').style.opacity = 0; | |
| // really clean the dom element | |
| setTimeout(function () {document.getElementById('loadingBar').style.display = 'none';}, 500); | |
| }); | |
| return network; | |
| } | |
| drawGraph(); | |
| </script> | |
| </body> | |
| </html> |