根据关键词查询论文
<h5>简要描述</h5>
<ul>
<li>根据关键词查询论文，可模糊查询</li>
</ul>
<h5>请求URL</h5>
<ul>
<li><code>http://xx.cxx/searchPaperByKeyword</code></li>
</ul>
<h5>请求方式</h5>
<ul>
<li>POST </li>
</ul>
<h5>参数</h5>
<table>
<thead>
<tr>
<th style="text-align: left;">参数名</th>
<th style="text-align: left;">必选</th>
<th style="text-align: left;">类型</th>
<th>说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">searchKeyword</td>
<td style="text-align: left;">是</td>
<td style="text-align: left;">string</td>
<td>关键词</td>
</tr>
</tbody>
</table>
<p>请求示例：{"searchKeyword":"Shape matching"}</p>
<h5>返回示例</h5>
<pre><code>[
{
"releasetime": "09 October 2018",
"typeandyear": "ECCV 2018",
"link": "https://doi.org/10.1007/978-3-030-01216-8_15",
"abstractcontext": "We present a new deep learning approach for matching deformable shapes by introducing Shape Deformation Networks which jointly encode 3D shapes and correspondences. This is achieved by factoring the surface representation into (i) a template, that parameterizes the surface, and (ii) a learnt global feature vector that parameterizes the transformation of the template into the input surface. By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template. We show that these correspondences can be improved by an additional step which improves the shape feature by minimizing the Chamfer distance between the input and transformed template. We demonstrate that our simple approach improves on state-of-the-art results on the difficult FAUST-inter challenge, with an average correspondence error of 2.88 cm. We show, on the TOSCA dataset, that our method is robust to many types of perturbations, and generalizes to non-human shapes. This robustness allows it to perform well on real unclean, meshes from the SCAPE dataset.",
"id": 7,
"keyword": "3D deep learning,Computational geometry,Shape matching",
"title": "3D-CODED: 3D Correspondences by Deep Deformation"
},
{
"releasetime": "06 October 2018",
"typeandyear": "ECCV 2018",
"link": "https://doi.org/10.1007/978-3-030-01228-1_46",
"abstractcontext": "We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.",
"id": 175,
"keyword": "Shape matching,Cross-modal recognition and retrieval",
"title": "Deep Shape Matching"
},</code></pre>
<h5>返回参数说明</h5>
<table>
<thead>
<tr>
<th style="text-align: left;">参数名</th>
<th style="text-align: left;">类型</th>
<th>说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">releasetime</td>
<td style="text-align: left;">String</td>
<td>论文的最后更新时间</td>
</tr>
<tr>
<td style="text-align: left;">typeandyear</td>
<td style="text-align: left;">String</td>
<td>论文的类型和发布年份</td>
</tr>
<tr>
<td style="text-align: left;">link</td>
<td style="text-align: left;">String</td>
<td>论文的链接</td>
</tr>
<tr>
<td style="text-align: left;">abstractcontext</td>
<td style="text-align: left;">String</td>
<td>论文的摘要</td>
</tr>
<tr>
<td style="text-align: left;">id</td>
<td style="text-align: left;">int</td>
<td>论文的id</td>
</tr>
<tr>
<td style="text-align: left;">keyword</td>
<td style="text-align: left;">String</td>
<td>论文的关键词集合</td>
</tr>
<tr>
<td style="text-align: left;">title</td>
<td style="text-align: left;">String</td>
<td>论文的标题</td>
</tr>
</tbody>
</table>
<h5>备注</h5>
<ul>
<li>更多返回错误代码请看首页的错误代码描述</li>
</ul>
<p>欢迎使用ShowDoc！</p>