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<h1 id="Memory-Guided-Video-Detection-CVPR19"><a href="#Memory-Guided-Video-Detection-CVPR19" class="headerlink" title="Memory-Guided-Video-Detection-CVPR19"></a>Memory-Guided-Video-Detection-CVPR19</h1><ul>
<li>Looking Fast and Slow: Memory-Guided Mobile Video Object Detection</li>
<li>Paper: <a href="https://arxiv.org/abs/1903.10172" target="_blank" rel="noopener">https://arxiv.org/abs/1903.10172</a></li>
<li>Code: <a href="https://github.com/tensorflow/models/tree/master/research" target="_blank" rel="noopener">https://github.com/tensorflow/models/tree/master/research</a></li>
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<h2 id="Overview"><a href="#Overview" class="headerlink" title="Overview"></a>Overview</h2><ol>
<li>提出了memory-guided的多特征网络提取的网络结构，使用快/慢特征提取网络分别提取不同帧特征，可以减少冗余计算。</li>
<li>使用Double Q-learning强化学习策略来执行memory更新，进行特征提取网络的选择。</li>
<li>在移动设备上可以实现高精度，高速度（70fps）。</li>
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<h1 id="Bottleneck-LSTM-Video-Object-Detection-CVPR18"><a href="#Bottleneck-LSTM-Video-Object-Detection-CVPR18" class="headerlink" title="Bottleneck-LSTM-Video-Object-Detection-CVPR18"></a>Bottleneck-LSTM-Video-Object-Detection-CVPR18</h1><ul>
<li>Bottleneck-LSTM：Mobile Video Object Detection with Temporally-Aware Feature </li>
<li>Paper: <a href="https://arxiv.org/abs/1711.06368" target="_blank" rel="noopener">https://arxiv.org/abs/1711.06368</a></li>
<li>Code: <a href="https://github.com/tensorflow/models/tree/master/research" target="_blank" rel="noopener">https://github.com/tensorflow/models/tree/master/research</a></li>
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<h2 id="Overview"><a href="#Overview" class="headerlink" title="Overview"></a>Overview</h2><ol>
<li>构建Conv Bottleneck-LSTM，并将Bottleneck-LSTM与one stage检测相结合。其中，Bottleneck-LSTM借鉴了深度可分离卷积和bottleneck。</li>
<li>在video detection中的速度，相比现存检测算快，FPS为15（个人感觉比较慢）。</li>
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<a class="post-title-link" href="/2019/07/10/object-detection-summary/" itemprop="url">Object Detection Summary</a></h1>
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<h1 id="目标检测（待完善）"><a href="#目标检测（待完善）" class="headerlink" title="目标检测（待完善）"></a>目标检测（待完善）</h1><h2 id="简述"><a href="#简述" class="headerlink" title="简述"></a>简述</h2><ol>
<li><strong>one-stage</strong></li>
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<li>主要思想：均匀地在图片的不同位置进行密集抽样，抽样时可以采用不同尺度和长宽比，然后利用CNN提取特征后直接进行分类与回归，整个过程只需要一步</li>
<li>代表：Yolo，SSD</li>
<li>特点：速度快，准确率一般没有two-stage高</li>
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<li><strong>two-stage</strong></li>
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<li>主要思想：一阶段生成proposals，二阶段对这些proposals进行分类和回归</li>
<li>代表：R-CNN，Fast R-CNN, Faster R-CNN</li>
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