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README.html
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<body>
<h1 id="another-awesome-dataset-list">Another Awesome Dataset List</h1>
<p><a href="https://github.com/sindresorhus/awesome"><img src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg" alt="Awesome" /></a></p>
<p><span class="emoji" data-emoji="sparkling_heart">💖</span>:</p>
<ul>
<li>AI开发者神器! 谷歌重磅推出数据集搜索 Dataset Search: <a href="https://mp.weixin.qq.com/s/ErbwXAz-_AJrmUGMHZIcwg">https://mp.weixin.qq.com/s/ErbwXAz-_AJrmUGMHZIcwg</a></li>
<li>Making it easier to discover datasets: <a href="https://www.blog.google/products/search/making-it-easier-discover-datasets/">https://www.blog.google/products/search/making-it-easier-discover-datasets/</a></li>
</ul>
<blockquote>
<p>Please <strong>cite related paper</strong> if you <strong>use their dataset</strong> <span class="emoji" data-emoji="smile">😄</span></p>
</blockquote>
<ul>
<li><a href="#another-awesome-dataset-list">Another Awesome Dataset List</a>
<ul>
<li><a href="#saliency">Saliency</a>
<ul>
<li><a href="#rgb-saliency-detection">RGB-Saliency Detection</a>
<ul>
<li><a href="#msramsra10kmsra-b">MSRA(MSRA10K/MSRA-B)</a></li>
<li><a href="#sed12">SED1/2</a></li>
<li><a href="#asdmsra1000msra1kneed-some-images">ASD(MSRA1000/MSRA1K)[need some images]</a></li>
<li><a href="#dut-omron">DUT-OMRON</a></li>
<li><a href="#duts">DUTS</a></li>
<li><a href="#hku-isneed-some-iamges">HKU-IS[need some iamges]</a></li>
<li><a href="#sod">SOD</a></li>
<li><a href="#icoseg">iCoSeg</a></li>
<li><a href="#infraredneed-help">Infrared[need help]</a></li>
<li><a href="#imgsal">ImgSal</a></li>
<li><a href="#ecssdcssd">ECSSD/CSSD</a></li>
<li><a href="#thur15k">THUR15K</a></li>
<li><a href="#bruce-aneed-help">Bruce-A[need help]</a></li>
<li><a href="#judd-aneed-help">Judd-A[need help]</a></li>
<li><a href="#pascal-s">PASCAL-S</a></li>
<li><a href="#ucsbneed-help">UCSB[need help]</a></li>
<li><a href="#osieneed-help">OSIE[need help]</a></li>
<li><a href="#acsd">ACSD</a></li>
</ul></li>
<li><a href="#other-special-sod-datasets">Other Special SOD Datasets</a>
<ul>
<li><a href="#xpie">XPIE</a></li>
<li><a href="#soc">SOC</a></li>
<li><a href="#sosmosneed-some-images">SOS/MOS[need some images]</a></li>
<li><a href="#ilsoneed-some-images">ILSO[need some images]</a></li>
<li><a href="#hs-sod">HS-SOD</a></li>
</ul></li>
<li><a href="#video-saliency-detection">Video Saliency Detection</a>
<ul>
<li><a href="#rsdpku-rsd">RSD(PKU-RSD)</a></li>
<li><a href="#stcneed-help">STC[need help]</a></li>
</ul></li>
<li><a href="#rgbd-saliency-detection">RGBD-Saliency Detection</a>
<ul>
<li><a href="#sip">SIP</a></li>
<li><a href="#nlprrgbd1000">NLPR/RGBD1000</a></li>
<li><a href="#nju4002000">NJU400/2000</a></li>
<li><a href="#stereossb">STEREO/SSB</a></li>
<li><a href="#lfsdnead-img">LFSD[nead img]</a></li>
<li><a href="#rgbd135des">RGBD135/DES</a></li>
<li><a href="#dut-rgbd">DUT-RGBD</a></li>
<li><a href="#ssd100">SSD100</a></li>
</ul></li>
<li><a href="#rgbt-saliency-detection-need-more-information">RGBT-Saliency Detection [need more information...]</a>
<ul>
<li><a href="#vt1000-dataset">VT1000 Dataset</a></li>
<li><a href="#vt821-dataset">VT821 Dataset</a></li>
</ul></li>
<li><a href="#high-resolution-saliency-detection">High-Resolution Saliency Detection</a>
<ul>
<li><a href="#hrsoddavis-s">HRSOD/DAVIS-S</a></li>
</ul></li>
<li><a href="#other-saliency-dataset">Other Saliency Dataset</a>
<ul>
<li><a href="#kaist-salient-pedestrian-dataset">KAIST Salient Pedestrian Dataset</a></li>
</ul></li>
</ul></li>
<li><a href="#segmentation">Segmentation</a>
<ul>
<li><a href="#generalneed-help">General[need help]</a>
<ul>
<li><a href="#davis">DAVIS</a></li>
<li><a href="#anyu">aNYU</a></li>
</ul></li>
<li><a href="#about-person">About Person</a>
<ul>
<li><a href="#supervisely人像数据集">Supervisely人像数据集</a></li>
<li><a href="#clothing-parsing">Clothing Parsing</a></li>
<li><a href="#humanparsing-dataset">HumanParsing-Dataset</a></li>
<li><a href="#look-into-person-lip">Look into Person (LIP)</a></li>
<li><a href="#taobao-commodity-dataset">Taobao Commodity Dataset</a></li>
<li><a href="#object-extraction-dataset">Object Extraction Dataset</a></li>
<li><a href="#clothing-co-parsing-ccp-dataset">Clothing Co-Parsing (CCP) Dataset</a></li>
<li><a href="#baidu-people-segmentation-datasetneed-help">Baidu People segmentation dataset[need help]</a></li>
</ul></li>
</ul></li>
<li><a href="#matting">Matting</a>
<ul>
<li><a href="#alphamattingcom">alphamatting.com</a></li>
<li><a href="#composition-1k-deep-image-matting">Composition-1k: Deep Image Matting</a></li>
<li><a href="#semantic-human-matting">Semantic Human Matting</a></li>
<li><a href="#matting-human-datasets">Matting-Human-Datasets</a></li>
<li><a href="#pfcn">PFCN</a></li>
<li><a href="#deep-automatic-portrait-matting">Deep Automatic Portrait Matting</a></li>
</ul></li>
<li><a href="#other">Other</a>
<ul>
<li><a href="#large-scale-fashion-deepfashion-database">Large-scale Fashion (DeepFashion) Database</a></li>
<li><a href="#ml-image">ML-Image</a></li>
</ul></li>
<li><a href="#need-your-help">need your help...</a></li>
<li><a href="#reference">Reference</a>
<ul>
<li><a href="#salient-object-detection-a-survey">Salient Object Detection: A Survey</a></li>
<li><a href="#review-of-visual-saliency-detection-with-comprehensive-information">Review of Visual Saliency Detection with Comprehensive Information</a></li>
<li><a href="#salient-object-detection-in-the-deep-learning-era-an-in-depth-survey">Salient Object Detection in the Deep Learning Era: An In-Depth Survey</a></li>
</ul></li>
<li><a href="#more">More</a>
<ul>
<li><a href="#similiar-projects">Similiar Projects</a></li>
<li><a href="#research-institutes">Research Institutes</a></li>
<li><a href="#resource-websites">Resource Websites</a></li>
</ul></li>
<li><a href="#about">About</a></li>
</ul></li>
</ul>
<h2 id="saliency">Saliency</h2>
<h3 id="rgb-saliency-detection">RGB-Saliency Detection</h3>
<h4 id="msramsra10kmsra-b">MSRA(MSRA10K/MSRA-B)</h4>
<p><img src="https://mmcheng.net/wp-content/uploads/2014/07/MSRA10K.jpg" alt="img" /></p>
<ul>
<li>论文: <a href="http://mmlab.ie.cuhk.edu.hk/2007/CVPR07_detect.pdf">T. Liu, J. Sun, N. Zheng, X. Tang, and H.-Y. Shum, "Learningto detect a salient object, " inCVPR, 2007, pp.1–8</a></li>
<li>主页: 南开大学媒体计算实验室: <a href="https://mmcheng.net/zh/msra10k/">https://mmcheng.net/zh/msra10k/</a></li>
<li>下载:
<ul>
<li>MSRA10K(formally named as THUS10000; <a href="http://mftp.mmcheng.net/Data/MSRA10K_Imgs_GT.zip">195MB</a>: images + binary masks):
<ul>
<li>Pixel accurate salient object labeling for <strong>10000 images</strong> from MSRA dataset.</li>
<li>Please cite our paper [https://mmcheng.net/SalObj/] if you use it.</li>
<li>Saliency maps and salient object region segmentation for other 20+ alternative methods are also available (<a href="http://pan.baidu.com/s/1dEaQqlF#path=%252FShare%252FSalObjRes">百度网盘</a>).</li>
</ul></li>
<li>MSRA-B (<a href="http://mftp.mmcheng.net/Data/MSRA-B.zip">111MB</a>: images + binary masks):
<ul>
<li>Pixel accurate salient object labeling for <strong>5000 images</strong> from MSRA-B dataset.</li>
<li>Please cite the corresponding paper [https://mmcheng.net/drfi/] if you use it.</li>
</ul></li>
</ul></li>
</ul>
<blockquote>
<p>我们通过检测输入图像中的显着对象来研究视觉注意力. 我们将显着对象检测表示为图像分割问题, 我们将显着对象与图像背景分开. 我们提出了一系列新颖的特征, 包括多尺度对比度, 中心环绕直方图和颜色空间分布, 以在本地, 区域和全局描述显着对象. 学习条件随机场以有效地组合这些特征以用于显着对象检测. 我们还构建了一个<strong>包含由多个用户标记的数以万计的完全标记图像的图像数据库</strong>. 据我们所知, 它是第一个用于视觉注意算法定量评估的大型图像数据库. 我们在此图像数据库上验证了我们的方法, 该数据库在本文中是公开的.</p>
<p>人们可能对图像中的显着对象有不同的看法. 为了解决"给定图像中可能是什么样的显着对象"的问题, 我们通过在多个用户的图像中标记"基础事实"显着对象来进行投票策略. 在本文中, 我们关注图像中单个显着对象的情况.</p>
<p>显著性对象表示. 通常, 我们<strong>将给定对象表示为给定image I中的二元mask</strong> $A={a_x}$. 对于每个像素x, $a_x∈{1, 0}$是二进制标签, 以指示像素是否属于显着对象.<strong>为了标记和评估, 我们要求用户绘制一个矩形来指定一个显着对象. 我们的检测算法也输出一个矩形.</strong></p>
<p>图像来源. 我们收集了一个非常大的图像数据库, 其中130, 099个来自各种来源的高质量图像, 主要来自图像论坛和图像搜索引擎. 然后我们手动选择60, 000多个图像, 每个图像包含一个显着对象或一个独特的前景对象. 我们进一步选择了20, 840张图片进行标记. 在选择过程中, 我们<strong>排除了包含非常大的显着对象的任何图像</strong>, 从而可以更准确地评估检测的性能.</p>
<p>标记一致性. 对于每个要标记的图像, 我们请用户绘制一个矩形, 该矩形包围图像中最大的对象根据他/她自己的理解. 由不同用户标记的矩形通常不相同. 为了减少标签的不一致性, 我们从多个用户绘制的矩形中选择一个"真实"标签.</p>
</blockquote>
<h4 id="sed12">SED1/2</h4>
<ul>
<li>单目标</li>
</ul>
<p><img src="./assets/2018-12-29-18-38-59.png" alt="img" /></p>
<ul>
<li>双目标</li>
</ul>
<p><img src="./assets/2018-12-29-18-39-30.png" alt="img" /></p>
<ul>
<li>真值</li>
</ul>
<p>给出的是每个图像由三个不同的人类对象分割的结果.</p>
<p><img src="./assets/2018-12-29-18-40-17.png" alt="img" /></p>
<ul>
<li><a href="https://arxiv.org/abs/1501.02741">A. Borji, M.-M. Cheng, H. Jiang, and J. Li, "Salient objectdetection: A benchmark, "IEEE TIP, vol.24, no.12, pp.5706–5722, 2015.</a></li>
<li><a href="http://www.wisdom.weizmann.ac.il/~meirav/Segmentation_Alpert_Galun_Brandt_Basri.pdf">Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration</a></li>
<li>项目: <a href="http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB/index.html">http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB/index.html</a></li>
<li>下载: <a href="http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB/dl.html">http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB/dl.html</a></li>
</ul>
<blockquote>
<p>这项工作的目的是为图像分割研究提供经验和科学依据. 评估分割算法产生的结果具有挑战性, 因为很难提出提供基础真实分割的规范测试集. 这部分是因为在日常复杂图像中手动描绘片段可能是费力的. 此外, 人们往往倾向于将语义考虑纳入其分段中, 这超出了数据驱动的分割算法的范围. 因此, 许多现有算法仅显示很少的分割结果. 为了评估由不同算法产生的分割, 我们编制了一个数据库, 目前<strong>包含200个灰度图像以及真实标注分割</strong>. 该数据库专门设计用于避免潜在的模糊, 仅通过仅通过强度, 纹理或其他低水平线索合并清晰描绘前景中与其周围环境不同的一个或两个物体的图像. 通过要求人类对象手动地将灰度图像(还提供颜色源)分成两个或三个类别来获得地面真实分割, 其中<strong>每个图像由三个不同的人类对象分割</strong>. 通过评估其与真实分割的一致性及其碎片量来评估分割. 与此数据库评估一起, 我们提供了用于评估给定分割算法的代码. 这样, 不同的分割算法可能具有可比较的结果以获得更多细节, 请参阅"评估测试"部分.</p>
</blockquote>
<h4 id="asdmsra1000msra1kneed-some-images">ASD(MSRA1000/MSRA1K)[need some images]</h4>
<ul>
<li>论文:<a href="https://www.researchgate.net/publication/224312323_A_two-stage_approach_to_saliency_detection_in_images">A two-stage approach to saliency detection inimages</a></li>
<li>相关:
<ul>
<li>T. Liu, J. Sun, N.-N. Zheng, X. Tang, and H.-Y. Shum, "<a href="http://research.microsoft.com/en-us/um/people/jiansun/salientobject/salient_object.htm">Learning to detect a salient object</a>, " in <em>Proc. IEEE Conf. Comput. Vis. Pattern Recognit.</em>, 2007, pp.1–8.</li>
<li>R. Achanta, S. Hemami, F. Estrada, and S. Süsstrunk, "<a href="http://ivrlwww.epfl.ch/supplementary_material/RK_CVPR09/">Frequency-tuned salient region detection</a>, " in <em>Proc. IEEE Conf. Comput. Vis. Pattern Recognit.</em>, 2009, pp.1597–1604.</li>
</ul></li>
<li>下载: <a href="http://download.csdn.net/detail/wanyq07/9839322">http://download.csdn.net/detail/wanyq07/9839322</a>
<ul>
<li>关于下载的说明: 因为基于MSRA的图片数据集, 在孙剑走了之后, MARA上就没了他的页面, 相关的资源也就找不到了. CSDN一篇博客有分享. 原图下载地址:<a href="http://download.csdn.net/detail/tuconghuan/8357509">MSRA图像数据集(1000幅含真实标注)</a>. 上面下载到的标注图尺寸被统一改为512*512, 所以这里在给个地址:<a href="http://download.csdn.net/detail/zzb4702/9559378">ASD尺寸一致</a></li>
</ul></li>
</ul>
<blockquote>
<p>ASD contains 1, 000 images with pixel-wise ground-truths. The images are selected from the MSRA-A dataset, where only the bounding boxes around salient regions are provided. The accurate salient masks in ASD are created based on object contours.</p>
<p>这个数据集包含有1000张图(MSRA1000)这个数据库来自于 该数据库的说明以及一些算法(IT, MZ, GB, SR, AC, IG ) 的结果可以在<a href="http://ivrlwww.epfl.ch/supplementary_material/RK_CVPR09/index.html">Frequency-tuned Salient Region Detection</a> (FT算法 => 这里改进的数据集叫做ACSD, 相关可见<a href="#ACSD">ACSD</a>部分)下载, 此外其中还包含了这1000张测试图的真值图.</p>
</blockquote>
<h4 id="dut-omron">DUT-OMRON</h4>
<p><img src="assets/2019-03-22-18-45-56.png" alt="img" /></p>
<ul>
<li>论文: C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang, "<a href="http://saliencydetection.net/dut-omron/">Saliency detection via graph-based manifold ranking</a>, " in <em>Proc. IEEE Conf. Comput. Vis. Pattern Recognit.</em>, 2013, pp.3166–3173.</li>
<li>项目: <a href="http://saliencydetection.net/dut-omron/#outline-container-org0e04792">http://saliencydetection.net/dut-omron/#outline-container-org0e04792</a></li>
<li>下载: <a href="http://saliencydetection.net/dut-omron/download/DUT-OMRON-image.zip">http://saliencydetection.net/dut-omron/download/DUT-OMRON-image.zip</a></li>
</ul>
<blockquote>
<p>数据库包括从超过140, 000张图像中手动选择的5, 168个高质量图像. 我们将图像的大小调整为宽为400或高为400像素, 其中另一条边小于400. 我们数据库的图像具有一个或多个显着对象和相对复杂的背景. 我们共有25名参与者, 用于汇总真值, 每个图像有五个参与者标签. 他们都有正常或矫正到正常的视力并且意识到我们实验的目标. 我们为提出的数据库构建像素方面的真实标注, 边界框, 和眼睛固定标注真值.</p>
<p>我们的数据集是唯一一个具有眼睛固定, 边界框和像素方面的大规模真实标注的数据集. 与ASD和MSRA数据集以及其他一些眼睛固定数据集(即MIT和NUSEF数据集)相比, 数据集中的图像更加困难, 因此更具挑战性, 并为相关的显着性研究提供了更多的改进空间.</p>
</blockquote>
<h4 id="duts">DUTS</h4>
<ul>
<li>项目: <a href="http://saliencydetection.net/duts/">http://saliencydetection.net/duts/</a></li>
</ul>
<blockquote>
<p>...we contribute a large scale data set named DUTS, <strong>containing 10, 553 training images and 5, 019 test images</strong>. All training images are collected from the ImageNet DET training/val sets, while test images are collected from the ImageNet DET test set and the SUN data set.</p>
<p>Both the training and test set contain very challenging scenarios for saliency detection. Accurate pixel-level ground truths are manually annotated by 50 subjects.</p>
<p>To our knowledge, DUTS is currently <strong>the largest saliency detection benchmark</strong> with the explicit training/test evaluation protocol.</p>
<p>For fair comparison in the future research, the training set of DUTS serves as a good candidate for learning DNNs, while the test set and other public data sets can be used for evaluation.</p>
</blockquote>
<h4 id="hku-isneed-some-iamges">HKU-IS[need some iamges]</h4>
<ul>
<li>项目: <a href="https://i.cs.hku.hk/~gbli/deep_saliency.html">https://i.cs.hku.hk/~gbli/deep_saliency.html</a></li>
<li>论文: <a href="http://i.cs.hku.hk/~yzyu/publication/mdfsaliency-cvpr15.pdf">Visual Saliency Based on Multiscale Deep Features</a></li>
<li>下载:
<ul>
<li><a href="https://drive.google.com/open?id=0BxNhBO0S5JCRQ1N6V25VeVh6cHc&authuser=0">Google Drive</a></li>
<li><a href="http://pan.baidu.com/s/1c0EpNfM">Baidu Yun</a></li>
</ul></li>
</ul>
<blockquote>
<p>数据集包含4447个具有显着对象的像素注释的图像</p>
<p>视觉显着性是包括计算机视觉在内的认知和计算科学中的一个基本问题. 在本文中, 我们发现可以从使用深度卷积神经网络(CNN)提取的多尺度特征中学习高质量的视觉显着性模型. 视觉识别任务的成功. 为了学习这样的显着性模型, 我们引入了一种神经网络结构, 它在CNN顶部具有完全连接的层, 负责三个不同尺度的特征提取. 然后, 我们提出一种改进方法来增强我们的显着性结果的空间一致性. 最后, 针对不同级别的图像分割计算的聚合多个显着性图可以进一步提高性能, 从而产生比由单个分割产生的显着性图更好的显着性图. 为了促进对视觉显着性模型的进一步研究和评估, <strong>我们还构建了一个新的大型数据库, 包括4447个具有挑战性的图像及其像素显着性注释</strong>.</p>
</blockquote>
<h4 id="sod">SOD</h4>
<p><img src="assets/2019-03-22-18-46-40.png" alt="img" /></p>
<ul>
<li>项目: <a href="http://elderlab.yorku.ca/SOD/">http://elderlab.yorku.ca/SOD/</a></li>
<li>下载
<ul>
<li>官方: <a href="http://elderlab.yorku.ca/SOD/SOD.zip">http://elderlab.yorku.ca/SOD/SOD.zip</a></li>
<li>百度云: <a href="https://pan.baidu.com/s/1IMElTPwD4yTo2TMSRU-keQ">https://pan.baidu.com/s/1IMElTPwD4yTo2TMSRU-keQ</a></li>