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<a class="article-title" href="/2019/04/06/c-c-ForInterview/">C/C++ 面试基础知识总结</a>
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<p>C/C++ 面试基础知识总结，只为复习<br>
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<li>图像语义分割(Semantic Segmentation)和物体检测ObjectDetection是计算机视觉领域非常经典的两个重要应用。在语义分割领域，FCN是代表性的算法；在物体检测领域，代表性的算法是Faster R-CNN。很自然的会想到，结合FCN和Faster R-CNN不仅可以是模型同时具有物体检测和语义分割两个功能，还可以是两个功能互相辅助，共同提高模型精度，这便是Mask R-CNN的提出动机。
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<a class="article-title" href="/2019/03/12/FCN/">图像语义分割网络FCN/U-Net网络解析</a>
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<a href="/2019/03/12/FCN/" class="archive-article-date">
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<li>图像语义分割(Semantic Segmentation)是图像处理和是机器视觉技术中关于图像理解的重要一环，也是 AI 领域中一个重要的分支。语义分割即是对图像中每一个像素点进行分类，确定每个点的类别（如属于背景、人或车等），从而进行区域划分。目前，语义分割已经被广泛应用于自动驾驶、无人机落点判定等场景中。
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<a class="article-title" href="/2019/03/06/decisionTree/">decisionTree</a>
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<p>决策树 decision tree 主要的优点是模型具有可读性，分类速度快。决策树学习通常包括三个步骤：特征选择、决策树的生成的决策树的剪枝<br>
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<a class="article-title" href="/2019/03/04/SVM/">支持向量机SVM</a>
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<p>支持向量机（supporr vector machine，SVM）是一种<strong>二类分类</strong>模型<br>该模型的学习策略是定义在<strong>特征空间上的间隔最大</strong>的线性分类器，<strong>间隔最大</strong>使它有区别于感知机<br>支持向量机还包括<strong>核技巧</strong>，这使它成为实质上的<strong>非线性</strong>分类器。<br>支持向量机的<strong>学习算法</strong>是求解<strong>凸二次规划</strong>的最优化算法<br>
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<p>2分类算法-逻辑回归 | 回归问题——线性回归<br>
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<li>对于老程序员来说，比起晦涩复杂的数学公式或者文本描述，也许直接看源码能够更好的帮助我们理解各种网络内部卷积模块。</li>
<li>我会定期阅读CVPR,ICCV等人工智能和计算机视觉相关的论文。这是紧跟着最新研究的唯一方法。作为一名计算机视觉算法研究者，当阅读科研文本或者公式的时候，我常常一时难以理解，但是我发现直接用代码来理解原理要容易得多。</li>
<li>这篇文章中，用Keras 实现代码来描述最新架构的重要卷积模块
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<p>基于深度学习中的目标检测场景下的目标<strong>多尺度变化问题</strong>，一直是该领域研究的核心问题。解决了多尺度问题(速度和准确度)，就能够写出一篇好的paper。下面介绍业界已经提出的多尺度问题解决方案<br>
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