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<!DOCTYPE html>
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194
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1
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<p>摘抄自：<a href="https://zhuanlan.zhihu.com/p/41825737" target="_blank" rel="noopener">CornerNet：目标检测算法新思路</a></p>
<p>个人观点：CornerNet创新来自于多人姿态估计的Bottom-Up思路，预测corner的heatmps,根据Embeddings vector对corner进行分组，其主干网络也来自于姿态估计的Hourglass Network。模型的源码在github已经公布，可以放心大胆的研究测试。</p>
<p>CV的很多任务之间是相通的，CVPR2018 best paper [8]也印证这一观点，在不同的子领域寻找相似性，迁移不同领域的算法，是CV行业一个趋势。</p>
<p>多人姿态估计的Hourglass Network算法也不断改进中，其实论文模型的推断速率受限于Hourglass Network的特征提取，有志青年也可以沿着这个思路取得更好的性能。</p>
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2
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<p>发现好的代码收藏到这里，不断更新。。。。。。</p>
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<p>本文主要推荐一些博客链接和工具链接，以便查找，如果需要详细说明，会单开一篇来介绍相应的工具。</p>
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<a class="post-title-link" href="/2019/06/13/focal-loss-in-rpn/" itemprop="url">Focal Loss in RPN</a></h1>
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<h3 id="参考文献"><a href="#参考文献" class="headerlink" title="参考文献"></a>参考文献</h3><ol>
<li><a href="https://link.springer.com/chapter/10.1007/978-3-030-03335-4_32" target="_blank" rel="noopener">Focal Loss for Region Proposal Network</a></li>
<li><a href="https://github.com/unsky/focal-loss" target="_blank" rel="noopener">focal-loss</a></li>
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