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Topics of interest include all aspects of computer vision and pattern recognition including, but not limited to: 3D from multi-view and sensors 3D from single images Adversarial attack and defense Autonomous driving Biometrics Computational imaging Computer vision for social good Computer vision theory Datasets and evaluation Deep learning architectures and techniques Document analysis and understanding Efficient and scalable vision Embodied vision: Active agents, simulation Explainable computer vision Humans: Face, body, pose, gesture, movement Image and video synthesis and generation Low-level vision Machine learning (other than deep learning) Medical and biological vision, cell microscopy Multimodal learning Optimization methods (other than deep learning) Photogrammetry and remote sensing Physics-based vision and shape-from-X Recognition: Categorization, detection, retrieval Representation learning Robotics Scene analysis and understanding Segmentation, grouping and shape analysis Self-& semi-& meta-& unsupervised learning Transfer/ low-shot/ continual/ long-tail learning Transparency, fairness, accountability, privacy and ethics in vision Video: Action and event understanding Video: Low-level analysis, motion, and tracking Vision + graphics Vision, language, and reasoning Vision applications and systems</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-08-18</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-signal"></i> Acceptance Ratio</div> </div> <div class="portlet-content"> <div class="row-fluid"> <div class="span8"> <div id="placeholder" style="height:240px"></div> <script type="text/javascript"> $(function() { var data = [ ['1997', 11.4], ['1998', 9.3], ['2000', 14.2], ['2001', 8.5], ['2003', 6.6], ['2004', 6.2], ['2005', 6.4], ['2006', 4.8], ['2007', 4.8], ['2008', 4], ['2009', 4.2] ]; var ticks = [ ['1997', '1997'], ['1998', '1998'], ['2000', '2000'], ['2001', '2001'], ['2003', '2003'], ['2004', '2004'], ['2005', '2005'], ['2006', '2006'], ['2007', '2007'], ['2008', '2008'], ['2009', '2009'] ]; $.plot("#placeholder", [ data ], { series: { bars: { show: true, barWidth: 0.6, align: "center" } }, xaxis: { ticks: ticks, tickLength: 0 }, yaxis: { max: 100, tickFormatter: function (v, axis) { return v + "%"; } } }); }); </script> </div> <div class="span4"> <div style="height:240px;overflow:scroll"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw3_c0">Year</th><th id="yw3_c1">Submitted</th><th id="yw3_c2">Accepted</th><th id="yw3_c3">Accepted(%)</th></tr> </thead> <tbody> <tr class="odd"> <td>2009</td><td>1464</td><td>61</td><td>4.2%</td></tr> <tr class="even"> <td>2008</td><td>1593</td><td>63</td><td>4%</td></tr> <tr class="odd"> <td>2007</td><td>1250</td><td>60</td><td>4.8%</td></tr> <tr class="even"> <td>2006</td><td>1131</td><td>54</td><td>4.8%</td></tr> <tr class="odd"> <td>2005</td><td>1160</td><td>74</td><td>6.4%</td></tr> <tr class="even"> <td>2004</td><td>873</td><td>54</td><td>6.2%</td></tr> <tr class="odd"> <td>2003</td><td>905</td><td>60</td><td>6.6%</td></tr> <tr class="even"> <td>2001</td><td>920</td><td>78</td><td>8.5%</td></tr> <tr class="odd"> <td>2000</td><td>466</td><td>66</td><td>14.2%</td></tr> <tr class="even"> <td>1998</td><td>453</td><td>42</td><td>9.3%</td></tr> <tr class="odd"> <td>1997</td><td>544</td><td>62</td><td>11.4%</td></tr> </tbody> </table><div class="keys" style="display:none" title="/conference/407"><span>2873</span><span>2872</span><span>2871</span><span>2870</span><span>2869</span><span>2868</span><span>1872</span><span>1871</span><span>1870</span><span>1869</span><span>1868</span></div> </div></div> </div> </div> </div> </div> <div class="portlet" id="yw4"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-thumbs-up"></i> Best Papers</div> </div> <div class="portlet-content"> <div id="yw5" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw5_c0">Year</th><th id="yw5_c1">Best Papers</th></tr> </thead> <tbody> <tr class="odd"> <td>2023</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Planning-oriented+Autonomous+Driving">Planning-oriented Autonomous Driving</a></td></tr> <tr class="even"> <td>2023</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Visual+Programming%3A+Compositional+visual+reasoning+without+training">Visual Programming: Compositional visual reasoning without training</a></td></tr> <tr class="odd"> <td>2022</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Learning+to+Solve+Hard+Minimal+Problems">Learning to Solve Hard Minimal Problems</a></td></tr> <tr class="even"> <td>2021</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=GIRAFFE%3A+Representing+Scenes+as+Compositional+Generative+Neural+Feature+Fields">GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields</a></td></tr> <tr class="odd"> <td>2020</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Unsupervised+Learning+of+Probably+Symmetric+Deformable+3D+Objects+from+Images+in+the+Wild">Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild</a></td></tr> <tr class="even"> <td>2019</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=A+Theory+of+Fermat+Paths+for+Non-Line-of-Sight+Shape+Reconstruction">A Theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction</a></td></tr> <tr class="odd"> <td>2018</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Taskonomy%3A+Disentangling+Task+Transfer+Learning">Taskonomy: Disentangling Task Transfer Learning</a></td></tr> <tr class="even"> <td>2017</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Learning+from+Simulated+and+Unsupervised+Images+through+Adversarial+Training">Learning from Simulated and Unsupervised Images through Adversarial Training</a></td></tr> <tr class="odd"> <td>2017</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Densely+Connected+Convolutional+Networks">Densely Connected Convolutional Networks</a></td></tr> <tr class="even"> <td>2016</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Deep+Residual+Learning+for+Image+Recognition">Deep Residual Learning for Image Recognition</a></td></tr> </tbody> </table><div class="pager">Go to page: <ul id="yw6" class="yiiPager"><li class="first hidden"><a href="/conference/407"><< First</a></li> <li class="previous hidden"><a href="/conference/407">< Previous</a></li> <li class="page selected"><a href="/conference/407">1</a></li> <li class="page"><a href="/conference/407?page=2">2</a></li> <li class="page"><a 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title="/conference/407"><span>2741</span><span>2448</span><span>4159</span><span>3009</span><span>9</span><span>4387</span><span>1578</span><span>4446</span><span>2785</span><span>2956</span></div> </div></div> </div> </div> <div class="portlet" id="yw10"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-file"></i> Related Journals</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw11" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw11_c0">CCF</th><th id="yw11_c1">Full Name</th><th id="yw11_c2">Impact Factor</th><th id="yw11_c3">Publisher</th><th id="yw11_c4">ISSN</th></tr> </thead> <tbody> <tr class="odd"> <td></td><td><a href="/journal/752">Production Planning & Control</a></td><td>6.100</td><td>Taylor & Francis</td><td>0953-7287</td></tr> <tr class="even"> <td></td><td><a href="/journal/1029">Materials 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title="/conference/407"><span>71551</span><span>57563</span><span>65886</span><span>70932</span><span>70858</span><span>20923</span><span>70336</span><span>69212</span><span>69558</span><span>69109</span><span>68877</span><span>14875</span><span>68660</span><span>65796</span><span>61029</span><span>13746</span><span>67607</span><span>67537</span><span>67249</span><span>61481</span><span>66695</span><span>64396</span><span>23963</span><span>49906</span><span>65194</span><span>65134</span><span>51275</span><span>65125</span><span>62816</span><span>64853</span><span>64581</span><span>64417</span><span>9255</span><span>26161</span><span>57234</span><span>46132</span><span>63203</span><span>63025</span><span>52942</span><span>62340</span><span>26254</span><span>61156</span><span>25008</span><span>61590</span><span>11251</span><span>45836</span><span>61519</span><span>35747</span><span>12497</span><span>59797</span><span>60040</span><span>59396</span><span>17167</span><span>6187</span><span>60633</span><span>49593</span><span>60545</span><span>56673</span><span>53509</span><span>60101</span><span>60007</span><span>59843</span><span>59839</span><span>48004</span><span>59470</span><span>59395</span><span>58807</span><span>58641</span><span>58237</span><span>57902</span><span>57853</span><span>49089</span><span>57753</span><span>57541</span><span>56970</span><span>26772</span><span>56726</span><span>56514</span><span>11384</span><span>24302</span><span>56250</span><span>43937</span><span>53119</span><span>28774</span><span>54384</span><span>54523</span><span>54438</span><span>23757</span><span>54001</span><span>53842</span><span>53679</span><span>51404</span><span>53556</span><span>53506</span><span>53250</span><span>52602</span><span>38628</span><span>36210</span><span>53099</span><span>34199</span></div> 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