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Papers in the main technical program must describe high-quality, original research. Topics of interest include the following aspects of computer vision and pattern recognition (in alphabetical order): 3D from a single image and shape-from-x 3D shape modeling and processing Action and behavior recognition Adversarial learning Biometrics Body gestures and pose Computational photography Datasets and evaluation Detection and localization in 2D and/or 3D Efficient training and inference methods Explainable AI for CV Faces Fairness, accountability, transparency, and ethics in vision First person (Egocentric) vision Image and video manipulation detection Image and video retrieval Image and video synthesis Low-level and physics-based vision Low-shot learning Machine learning architectures and formulations Medical, biological, and cell microscopy Motion and tracking Neural generative models Optimization and learning methods Recognition and classification Representation learning Scene analysis and understanding Scene text and document understanding Segmentation, grouping, and shape Semi / Weak / Self / Unsupervised lLearning Stereo, 3D from multiview and other sensors Transfer learning Video analysis and understanding Vision + language Vision + other modalities Vision applications and systems Vision for and autonomous vehicles Vision for robotics and embodied vision Visual reasoning and logical representation All submissions will be handled electronically. In addition to the main technical program, the conference will include Tutorials, Workshops, Demonstrations, and Exhibits. Submit proposals to the appropriate chair.</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2023-12-10</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 = [ ['1998', 18.8], ['2000', 43.6], ['2002', 7.5], ['2004', 7.4], ['2006', 4.4] ]; var ticks = [ ['1998', '1998'], ['2000', '2000'], ['2002', '2002'], ['2004', '2004'], ['2006', '2006'] ]; $.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>2006</td><td>900</td><td>40</td><td>4.4%</td></tr> <tr class="even"> <td>2004</td><td>555</td><td>41</td><td>7.4%</td></tr> <tr class="odd"> <td>2002</td><td>600</td><td>45</td><td>7.5%</td></tr> <tr class="even"> <td>2000</td><td>266</td><td>116</td><td>43.6%</td></tr> <tr class="odd"> <td>1998</td><td>223</td><td>42</td><td>18.8%</td></tr> </tbody> </table><div class="keys" style="display:none" title="/conference/413"><span>2864</span><span>1877</span><span>1876</span><span>1875</span><span>1874</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>2010</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Graph+Cut+Based+Inference+with+Co-occurrence+Statistics">Graph Cut Based Inference with Co-occurrence Statistics</a></td></tr> <tr class="even"> <td>2008</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Learning+Spatial+Context%3A+Using+Stuff+to+Find+Things">Learning Spatial Context: Using Stuff to Find Things</a></td></tr> <tr class="odd"> <td>2006</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Learning+to+Combine+Bottom-Up+and+Top-Down+Segmentation">Learning to Combine Bottom-Up and Top-Down Segmentation</a></td></tr> <tr class="even"> <td>2004</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=High+Accuracy+Optical+Flow+Estimation+Based+on+a+Theory+for+Warping">High Accuracy Optical Flow Estimation Based on a Theory for Warping</a></td></tr> <tr class="odd"> <td>2004</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=A+Boosted+Particle+Filter%3A+Multitarget+Detection+and+Tracking">A Boosted Particle Filter: Multitarget Detection and Tracking</a></td></tr> <tr class="even"> <td>2002</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Object+Recognition+as+Machine+Translation%3A+Learning+a+Lexicon+for+a+Fixed+Image+Vocabulary">Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary</a></td></tr> <tr class="odd"> <td>2002</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Multi-camera+Scene+Reconstruction+via+Graph+Cuts">Multi-camera Scene Reconstruction via Graph Cuts</a></td></tr> <tr class="even"> <td>2002</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Increasing+Space-Time+Resolution+in+Video">Increasing Space-Time Resolution in Video</a></td></tr> <tr class="odd"> <td>2002</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=A+Variational+Approach+to+Recovering+a+Manifold+from+Sample+Points">A Variational Approach to Recovering a Manifold from Sample Points</a></td></tr> <tr class="even"> <td>2002</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=3D+Statistical+Shape+Models+Using+Direct+Optimisation+of+Description+Length">3D Statistical Shape Models Using Direct Optimisation of Description Length</a></td></tr> </tbody> </table><div class="pager">Go to page: <ul id="yw6" class="yiiPager"><li class="first hidden"><a href="/conference/413"><< First</a></li> <li class="previous hidden"><a href="/conference/413">< Previous</a></li> <li class="page selected"><a href="/conference/413">1</a></li> <li class="page"><a href="/conference/413?page=2">2</a></li> <li class="next"><a href="/conference/413?page=2">Next ></a></li> <li class="last"><a href="/conference/413?page=2">Last >></a></li></ul></div><div class="keys" style="display:none" title="/conference/413"><span>431</span><span>430</span><span>425</span><span>421</span><span>422</span><span>419</span><span>420</span><span>423</span><span>427</span><span>429</span></div> </div></div> </div> <div class="portlet" id="yw7"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw8" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw8_c0">CCF</th><th id="yw8_c1">CORE</th><th id="yw8_c2">QUALIS</th><th id="yw8_c3">Short</th><th id="yw8_c4">Full Name</th><th id="yw8_c5">Submission</th><th id="yw8_c6">Notification</th><th id="yw8_c7">Conference</th></tr> </thead> <tbody> <tr class="odd"> <td><span 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(652)</a></td><td>2018</td></tr> <tr class="even"> <td><a href="/researcher/18149">Xipeng Lin (331)</a></td><td>2018</td></tr> <tr class="odd"> <td><a href="/researcher/5465">Yawei Luo (204)</a></td><td>2018</td></tr> <tr class="even"> <td><a href="/researcher/4145">Ling Ding (337)</a></td><td>2018</td></tr> <tr class="odd"> <td><a href="/researcher/12427">Sun Jin (211)</a></td><td>2018</td></tr> <tr class="even"> <td><a href="/researcher/12419">Bo Zhang (7)</a></td><td>2018</td></tr> <tr class="odd"> <td><a href="/researcher/11567">Yan Zhenguo (291)</a></td><td>2018</td></tr> <tr class="even"> <td><a href="/researcher/11436">Jianyuan Guo (5)</a></td><td>2018</td></tr> <tr class="odd"> <td><a href="/researcher/5226">Shenghua Wei (647)</a></td><td>2018</td></tr> <tr class="even"> <td><a href="/researcher/8488">Yuanyuan Huang (37)</a></td><td>2018</td></tr> <tr class="odd"> <td><a href="/researcher/1334">Feng Gao (6)</a></td><td>2012</td></tr> </tbody> </table><div class="keys" style="display:none" title="/conference/413"><span>68563</span><span>47283</span><span>63800</span><span>61088</span><span>56514</span><span>23757</span><span>40919</span><span>49089</span><span>38331</span><span>34560</span><span>25434</span><span>29361</span><span>45582</span><span>45306</span><span>42257</span><span>39350</span><span>29053</span><span>16787</span><span>29486</span><span>29361</span><span>29336</span><span>29322</span><span>29286</span><span>29258</span><span>26495</span><span>29234</span><span>29189</span><span>29174</span><span>29042</span><span>28977</span><span>28971</span><span>28862</span><span>28750</span><span>28401</span><span>28617</span><span>28393</span><span>24552</span><span>27914</span><span>27242</span><span>9815</span><span>14270</span><span>18149</span><span>5465</span><span>4145</span><span>12427</span><span>12419</span><span>11567</span><span>11436</span><span>5226</span><span>8488</span><span>1334</span></div> </div> <div class="portlet" 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