places.csail.mit.edu places.csail.mit.edu

places.csail.mit.edu

MIT Places Database for Scene Recognition

By MIT Computer Science and Artificial Intelligence Laboratory. Places205-GoogLeNet is available to download in the Places CNNs. Register to download data and submit prediction results at here. The leaderboard of Places Database is at here. An image dataset which contains 2,448,873 images from 205 scene categories. Convolutional neural networks trained on Places. Input a picture of a place or scene and see how our Places-CNN predicts it. A visualization of units’ connection for CNNs.

http://places.csail.mit.edu/

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MIT Places Database for Scene Recognition | places.csail.mit.edu Reviews
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By MIT Computer Science and Artificial Intelligence Laboratory. Places205-GoogLeNet is available to download in the Places CNNs. Register to download data and submit prediction results at here. The leaderboard of Places Database is at here. An image dataset which contains 2,448,873 images from 205 scene categories. Convolutional neural networks trained on Places. Input a picture of a place or scene and see how our Places-CNN predicts it. A visualization of units’ connection for CNNs.
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3 download
4 places cnns
5 scene recognition demo
6 visualization
7 announcement
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MIT Places Database for Scene Recognition | places.csail.mit.edu Reviews

https://places.csail.mit.edu

By MIT Computer Science and Artificial Intelligence Laboratory. Places205-GoogLeNet is available to download in the Places CNNs. Register to download data and submit prediction results at here. The leaderboard of Places Database is at here. An image dataset which contains 2,448,873 images from 205 scene categories. Convolutional neural networks trained on Places. Input a picture of a place or scene and see how our Places-CNN predicts it. A visualization of units’ connection for CNNs.

INTERNAL PAGES

places.csail.mit.edu places.csail.mit.edu
1

Places Database

http://places.csail.mit.edu/user/leaderboard.php

Leaderboard of Places Database. Top1 accuracy and Top5 accuracy on the test set of Places205:. Qian Zhang(Beijing Samsung Telecom R&D Center). Limin Wang,Sheng Guo,Weilin Huang,Yu Qiao. Hussein Al-barazanchi, Hussam Qassim, Dr. Abhishek Verma (CSUF). Liwei Wang(UIUC),Chen-Yu Lee(UCSD). Indian Institute of Science, Bangalore. Dougal - baseline 1. You could choose to include or hide from the learderboard in the User Settings.

2

Places205 dataset

http://places.csail.mit.edu/downloadData.html

MIT Computer Science and Artificial Intelligence Laboratory. Places205: We release 2.5 million images from 205 scene categories to the public. To use the dataset. Page generated 2015-05-12 00:16:05 EDT, by jemdoc.

3

Visualization of Places-CNN and ImageNet CNN

http://places.csail.mit.edu/visualizationCNN.html

Visualization of Places-CNN and ImageNet CNN. MIT Computer Science and Artificial Intelligence Laboratory. A tool to visualize the connections among units and layers in the CNNs. The object/concept segmentation results for each unit at different layers for Places-CNN and ImageNet-CNN. Units’ receptive fields and the most activated image crops. The receptive field and the top ranked activation patterns within the receptive field of each unit at different layers for Places-CNN and ImageNet-CNN.

4

MIT Scene recognition demo

http://places.csail.mit.edu/demo.html

MIT Scene Recognition Demo. This demo identifies if the image is an indoor or an outdoor place, and suggests the five most likely place categories representing the image, using Places-CNN (see project page. It is made for pictures of environments, places, views on a scene and a space (as opposed to picture of an object). You also could upload image using mobile phone. Upload .jpg or jpeg image only. The heatmap is generated by the CAM.

5

Places Database

http://places.csail.mit.edu/user

You have to register to use the dataset. Before we are able to offer you access to the database, you have agreed the following license agreement. The citation for the use of the MIT Places-205 Dataset and/or Places205-CNN is:. B Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning Deep Features for Scene Recognition using Places Database. Advances in Neural Information Processing Systems 27 (NIPS), 2014.

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Antonio Torralba

http://web.mit.edu/torralba/www

Computer Science and Artificial Intelligence Laboratory. Dept of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Address: 32 Vassar Street, Cambridge, MA 02139. My research is in the areas of computer vision, machine learning and human visual perception. I am interested in scene and object recognition, among other things. A scene parsing challenge is being held jointly with ILSVRC'16. Aligning books and movies. Places2 scene classification challenge. For more details o...

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AN UNCANNY VALLEY, IN REVERSE — PINKNANTUCKET PRESS

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LQ's wiki

http://lqkhoo.com/wiki

From LQ's wiki. Hi, I'm Li! I'm most interested in working with technologies that can tractably and massively scale, such as applications of machine learning to large, high dimensional, sparse, or heterogeneous data. I hold a Master of Engineering in Computer Science with first class honours from University College London. Since August 2015. I have experience with datasets and methods related to search and advertising, semantic computation, and NLP - corpora ( mined Twitter data, BNC. What I'm working on.

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MAYA CHAMI - MA DIGITAL ARTS: June 2015

http://mayachamidigitalarts.blogspot.com/2015_06_01_archive.html

Dedicated originally to the MA in Digital Arts at Camberwell College of Arts, UAL (2009-2011). Starting October 2011, I will be using this same blog to continue posting about projects in progress. Thursday, 25 June 2015. The latest news from Research at Google. Inceptionism: Going Deeper into Neural Networks. Wednesday, June 17, 2015. Posted by Alexander Mordvintsev, Software Engineer, Christopher Olah, Software Engineering Intern and Mike Tyka, Software Engineer. One way to visualize what goes on is to ...

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CVPR 2015深度学习回顾:ConvNet、Caffe、Torch及其他-CSDN.NET

http://www.csdn.net/article/2015-08-06/2825395

本文是vision.ai的Co-Founder,前MIT研究人员T. Malisiewicz针对CVPR'15尤其是Deep Learning的综述文章,谈到了ConvNet的Baseline,Caffe和Torch之间的分歧,ArXiv论文热,以及百度的ImageNet违规事件等。 CVPR可谓计算机视觉领域的奥运会,这是vision.ai的Co-Founder,前MIT研究人员T. Malisiewicz针对CVPR'15尤其是Deep Learning的综述文章,谈到了ConvNet的Baseline,Caffe和Torch之间的分歧,ArXiv论文热,以及百度的ImageNet违规事件等。 原文标题为 Deep down the rabbit hole: CVPR 2015 and beyond。 数据集通常是一件大事 请下载我们的数据 数据集依旧是件大事 但是我们抱歉告诉你,你所在大学的计算资源达不到要求 但幸运的是,我们 X 公司总在招聘,所以来加入我们吧,让我们一起推动研究的向前发展。 如果你想要查看个人文献,我建议Andrej Karpathy的 CVPR 2015文献在线导航工具.

people.csail.mit.edu people.csail.mit.edu

Spatial envelope

http://people.csail.mit.edu/torralba/code/spatialenvelope

Modeling the shape of the scene: a holistic representation of the spatial envelope. Aude Oliva, Antonio Torralba. International Journal of Computer Vision. Vol 42(3): 145-175, 2001. PDF. This material is based upon work supported by the National Science Foundation under CAREER Grant No. 0546262. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. ParamnumberBlocks = 4;.

multimediacommons.wordpress.com multimediacommons.wordpress.com

Additional Feature Corpora | The Multimedia Commons Initiative

https://multimediacommons.wordpress.com/other-feature-corpora

The Multimedia Commons Initiative. A research community around the YFCC100M dataset. YLI-GEO: Placing Task Subcorpus. YLI-MED: Event Detection Subcorpus. Tag and Caption Prediction Challenge. The Multimedia Commons resources include several sets of computed features contributed by researchers at collaborating institutions (in addition to the YLI Feature Corpus. Extracted from the images in the Yahoo Flickr Creative Commons 100 Million (YFCC100M) dataset. Features and Annotation Sets Hosted on Other Sites.

vision.stanford.edu vision.stanford.edu

Stanford University CS231n: Convolutional Neural Networks for Visual Recognition

http://vision.stanford.edu/teaching/cs231n/project.html

CS231n: Convolutional Neural Networks for Visual Recognition. Update: Winter Quater 2015 projects. Have now been posted! The Course Project is an opportunity for you to apply what you have learned in class to a problem of your interest. There are two project options you can pick from:. Option 1: Your own project (Encouraged). Your are encouraged to select a topic and work on your own project. Potential projects usually fall into these two tracks:. IEEE Conference on Computer Vision and Pattern Recognition.

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Inceptionalism – Astounding Images Produced by Neural Network | Ian Q. Rowan

https://ianqrowan.wordpress.com/2015/06/21/inceptionalism-astounding-images-produced-by-neural-network

About Ian Q. Rowan. Ian Q Rowan Photography. Ian Q Rowan Videography. Ian Q Rowan Writing. Writings, photography and film. Stay updated via RSS. The Narwhal’s Wake IndieGoGo Campaign Fiscally Sponsored by The Redford Center. Inceptionalism – Astounding Images Produced by Neural Network. When In Rome…. 15 Days of NarNar the Narwhal: Day 8. The Narwhal's Wake - Feature Documentary Film. Inceptionalism – Astounding Images Produced by Neural Network. Posted: June 21, 2015 in Collage. Until it gives the class...

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Places

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Places.com -- Day Trip Planner Mobile Web App

First Time Logging In. Click Slide-Bar On Right To Open Web App. Welcome to Places.com. Comprehensive Day Trip Travel Planner for iPhone and Android. Places.com is a free full featured Global Positioning System (GPS) travel companion for iPhone and Android devices. It is a web application (app), so it runs on your mobile browser, with no further downloading and installation required. Visit http:/ places.com on your iPhone. Mobile browser and start using this free no-installation web app today! Feature re...

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MIT Places Database for Scene Recognition

By MIT Computer Science and Artificial Intelligence Laboratory. Places205-GoogLeNet is available to download in the Places CNNs. Register to download data and submit prediction results at here. The leaderboard of Places Database is at here. An image dataset which contains 2,448,873 images from 205 scene categories. Convolutional neural networks trained on Places. Input a picture of a place or scene and see how our Places-CNN predicts it. A visualization of units’ connection for CNNs.

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