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本文提出了多个基于半监督学习的自动视频标注方法。通过对几种常见的半监督学习方法,如自训练、互训练以及Co一EM等方法的分析,针对它们(主要是自训练和互训练方法)在视频标注应用中的局限,在提高分类的准确性和模型更新等方面做了深入研究,提出了相应的改进措施。
-This paper presents a number of semi-supervised learning-based automatic video annotation methods. Through several comm
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进行半监督转倒式训练
通过半监督学习进行分类-for semi-supervised maching learning,the paper is 《Large Scale Transductive SVMs》
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基于时空域稀疏性表征的人的行为识别和理解的算法研究的论文,采用了非监督学习的方法,具有很*价值。-When the airspace based on sparse representation of human behavior recognition and understanding of the algorithm research paper, using non-supervised learning method, has great reference value.
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模式识别的内容,包括模式识别的基本概念、模式识别方法及应用。具体的内容包括:正则化网络、Bayes决策理论、分类器组合、统计学习理论、概率密度估计、非监督学习方法-Pattern recognition, including the basic concepts of pattern recognition, pattern recognition methods and applications.Specific content, including: Regularization Netwo
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CVPR2012_oral
Weakly Supervised Structured Output Learning for Semantic Segmentation-We address the problem of weakly supervised semantic
segmentation. The training images are labeled only by the
classes they contain, not by their location in t
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用来图像检索的,SSMetric半监督学习距离图像检索Used for image retrieval, SSMetric a semi-supervised learning distance image retrieval
-Used for image retrieval, SSMetric a semi-supervised learning distance image retrieval
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semi-supervised learning 半监督局部线性嵌入-semi-supervised learning
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基于图的半监督学习算法,matlab代码-Semi-supervised learning algorithm based on graph, matlab code. . . .
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A Comparative Evaluation of Deep Belief Nets in Semi-supervised Learning
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在机器学习领域,支持向量机SVM(Support Vector Machine)是一个有监督的学习模型,通常用来进行模式识别、分类、以及回归分析-In the field of machine learning, support vector machine SVM (Support Vector Machine) is a supervised learning model, typically used for pattern recognition, classification, and
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In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a
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对图像做spca有监督的主成分分析降维,并通过RBF神经网络学习得到一个模型,并投影到原图来降噪-supervised manifold learning
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Feature Learning based Deep Supervised Hashing with Pairwise Labels 这篇论文的代码,用于图片检索-Feature Learning based Deep Supervised Hashing with Pairwise Labels
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人脸识别是一个有监督学习过程,首先利用训练集构造一个人脸模型,然后将测试集与训练集进行匹配,找到与之对应的训练集头像。最容易的方式是直接利用欧式距离计算测试集的每一幅图像与训练集的每一幅图像的距离,然后选择距离最近的图像作为识别的结果。这种直接计算距离的方式直观,但是有一个非常大的缺陷—计算量太大。如果每幅图像大小为100*100,训练集大小1000,则识别测试集中的一幅图像就需要1000*100*100的计算量,当测试集很大时,识别速度非常缓慢。(Face recognition is a s
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人脸识别是一个有监督学习过程,首先利用训练集构造一个人脸模型,然后将测试集与训练集进行匹配,找到与之对应的训练集头像。最容易的方式是直接利用欧式距离计算测试集的每一幅图像与训练集的每一幅图像的距离,然后选择距离最近的图像作为识别的结果。(Face recognition is a supervised learning process. Firstly, a face model is constructed by training set, and then the test set is m
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We develop a new edge detection algorithm, holistically-nested edge detection (HED), which performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automat
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This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual(effective visual representation Specifically we use unsupervised motion-based segmentation on videos to obtain segments)
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半监督学习(Semi-Supervised Learning,SSL)是模式识别和机器学习领域研究的重点问题,是监督学习与无监督学习相结合的一种学习方法。半监督学习使用大量的未标记数据,以及同时使用标记数据,来进行模式识别工作。当使用半监督学习时,将会要求尽量少的人员来从事工作,同时,又能够带来比较高的准确性
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OpenPose人体姿态识别项目是美国卡耐基梅隆大学(CMU)基于卷积神经网络和监督学习并以caffe为框架开发的开源库。可以实现人体动作、面部表情、手指运动等姿态估计。适用于单人和多人,具有极好的鲁棒性。是世界上首个基于深度学习的实时多人二维姿态估计应用,基于它的实例如雨后春笋般涌现。人体姿态估计技术在体育健身、动作采集、3D试衣、舆情监测等领域具有广阔的应用前景,人们更加熟悉的应用就是抖音尬舞机(OpenPost Human Attitude Recognition Project is a
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