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diff --git a/2018/08/14/Generative-Adversarial-Nets/index.html b/2018/08/14/Generative-Adversarial-Nets/index.html
@@ -89,7 +89,7 @@
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 <meta name="twitter:title" content="Generative Adversarial Nets">
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@@ -755,7 +755,7 @@ <h3 id="generative-adversarial-networks代码整理">Generative Adversarial Netw
 
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diff --git a/2018/08/14/How-to-use-hexo/index.html b/2018/08/14/How-to-use-hexo/index.html
@@ -89,7 +89,7 @@
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 <meta name="twitter:title" content="How to use hexo?">
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@@ -785,7 +785,7 @@ <h2 id="hexo-资源">Hexo 资源</h2>
 
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diff --git a/2018/08/14/Keras-Tutorial/index.html b/2018/08/14/Keras-Tutorial/index.html
@@ -89,7 +89,7 @@
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@@ -765,7 +765,7 @@ <h2 id="目录">目录</h2>
 
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diff --git a/2018/08/17/Pedestrian-Detection-Sources/index.html b/2018/08/17/Pedestrian-Detection-Sources/index.html
@@ -124,7 +124,7 @@
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+<meta property="og:updated_time" content="2022-05-30T14:16:36.000Z">
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 <meta name="twitter:title" content="行人检测（Pedestrian Detection）论文整理">
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@@ -1234,7 +1234,7 @@ <h2 id="性能比较">性能比较</h2>
 
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diff --git a/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/index.html b/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/index.html
@@ -81,13 +81,13 @@
 
 
 
-<meta name="description" content="前言 CornerNet: Detection Objects as Paired Keypoints　这篇论文发表在ECCV2018，本人感觉非常有意思，所以和大家分享一下。 Arxiv: https://arxiv.org/abs/1808.01244 Github: https://github.com/umich-vl/">
+<meta name="description" content="前言 CornerNet: Detection Objects as Paired Keypoints　这篇论文发表在ECCV2018，本人感觉非常有意思，所以和大家分享一下。 Arxiv: https://arxiv.org/abs/1808.01244 Github: https://github.com/umich-vl/">
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-<meta property="og:description" content="前言 CornerNet: Detection Objects as Paired Keypoints　这篇论文发表在ECCV2018，本人感觉非常有意思，所以和大家分享一下。 Arxiv: https://arxiv.org/abs/1808.01244 Github: https://github.com/umich-vl/">
+<meta property="og:description" content="前言 CornerNet: Detection Objects as Paired Keypoints　这篇论文发表在ECCV2018，本人感觉非常有意思，所以和大家分享一下。 Arxiv: https://arxiv.org/abs/1808.01244 Github: https://github.com/umich-vl/">
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@@ -97,10 +97,10 @@
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-<meta property="og:updated_time" content="2021-11-02T15:53:44.528Z">
+<meta property="og:updated_time" content="2022-05-30T14:16:36.000Z">
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 <meta name="twitter:title" content="CornerNet: Detection Objects as Paired Keypoints">
-<meta name="twitter:description" content="前言 CornerNet: Detection Objects as Paired Keypoints　这篇论文发表在ECCV2018，本人感觉非常有意思，所以和大家分享一下。 Arxiv: https://arxiv.org/abs/1808.01244 Github: https://github.com/umich-vl/">
+<meta name="twitter:description" content="前言 CornerNet: Detection Objects as Paired Keypoints　这篇论文发表在ECCV2018，本人感觉非常有意思，所以和大家分享一下。 Arxiv: https://arxiv.org/abs/1808.01244 Github: https://github.com/umich-vl/">
 <meta name="twitter:image" content="https://www.starlg.cn/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig1.png">
 
 
@@ -454,17 +454,17 @@ <h1 class="post-title" itemprop="name headline">CornerNet: Detection Objects as
 
 
 
-        <h3 id="前言">前言</h3>
-<p>CornerNet: Detection Objects as Paired Keypoints　这篇论文发表在ECCV2018，本人感觉非常有意思，所以和大家分享一下。</p>
-<p>Arxiv: https://arxiv.org/abs/1808.01244 Github: https://github.com/umich-vl/</p>
-<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig1.png" title="We detect an object as a pair of bounding box corners grouped together.">
-<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig2.png" title="Often there is no local evidence to determine the location of a bounding box corner. We address this issue by proposing a new type of pooling layer.">
-<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig3.png" title="Corner pooling: for each channel, we take the maximum values (red dots) in two directions (red lines), each from a separate feature map, and add the two maximums together (blue dot).">
-<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig4.png" title="Overview of CornerNet. The backbone network is followed by two prediction modules, one for the top-left corners and the other for the bottom-right corners. Using the predictions from both modules, we locate and group the corners.">
-<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig5.png" title="“Ground-truth” heatmaps for training.">
-<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig6.png" title="The top-left corner pooling layer can be implemented very efficiently. We scan from left to right for the horizontal max-pooling and from bottom to top for the vertical max-pooling. We then add two max-pooled feature maps.">
-<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig7.png" title="The prediction module starts with a modified residual block, in which we replace the first convolution module with our corner pooling module. The modified residual block is then followed by a convolution module. We have multiple branches for predict- ing the heatmaps, embeddings and offsets">
-<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig8.png" title="Example bounding box predictions overlaid on predicted heatmaps of corners.">
+        <h3 id="前言">前言</h3>
+<p>CornerNet: Detection Objects as Paired Keypoints　这篇论文发表在ECCV2018，本人感觉非常有意思，所以和大家分享一下。</p>
+<p>Arxiv: https://arxiv.org/abs/1808.01244 Github: https://github.com/umich-vl/</p>
+<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig1.png" title="We detect an object as a pair of bounding box corners grouped together.">
+<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig2.png" title="Often there is no local evidence to determine the location of a bounding box corner. We address this issue by proposing a new type of pooling layer.">
+<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig3.png" title="Corner pooling: for each channel, we take the maximum values (red dots) in two directions (red lines), each from a separate feature map, and add the two maximums together (blue dot).">
+<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig4.png" title="Overview of CornerNet. The backbone network is followed by two prediction modules, one for the top-left corners and the other for the bottom-right corners. Using the predictions from both modules, we locate and group the corners.">
+<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig5.png" title="“Ground-truth” heatmaps for training.">
+<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig6.png" title="The top-left corner pooling layer can be implemented very efficiently. We scan from left to right for the horizontal max-pooling and from bottom to top for the vertical max-pooling. We then add two max-pooled feature maps.">
+<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig7.png" title="The prediction module starts with a modified residual block, in which we replace the first convolution module with our corner pooling module. The modified residual block is then followed by a convolution module. We have multiple branches for predict- ing the heatmaps, embeddings and offsets">
+<img src="/2018/09/02/CornerNet-Detection-Objects-as-Paired-Keypoints/Fig8.png" title="Example bounding box predictions overlaid on predicted heatmaps of corners.">
 
 
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@@ -747,7 +747,7 @@ <h3 id="前言">前言</h3>
 
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diff --git a/2018/09/05/MLIA-Logistic-Regression/index.html b/2018/09/05/MLIA-Logistic-Regression/index.html
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diff --git a/2018/09/09/Conda-Tutorials/index.html b/2018/09/09/Conda-Tutorials/index.html
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diff --git a/2018/10/10/docker/index.html b/2018/10/10/docker/index.html
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