[Awesome@CVPR2013] Scene-SIRFs, Sketch Tokens, Detecting 100,000 object classes, and more - Selamat datang di situs media global terbaru Xivanki, Pada halaman ini kami menyajikan informasi tentang [Awesome@CVPR2013] Scene-SIRFs, Sketch Tokens, Detecting 100,000 object classes, and more !! Semoga tulisan dengan kategori
best paper !!
caroline pantofaru !!
conference !!
cvpr !!
cvpr 2013 !!
fei-fei li !!
jay yagnik !!
jia deng !!
jon barron !!
joseph lim !!
noah snavely !!
papers !!
scene understanding !!
silvio savarese !!
sirfs !!
sketch tokens !! ini bermanfaat bagi anda. Silahkan sebarluaskan postingan [Awesome@CVPR2013] Scene-SIRFs, Sketch Tokens, Detecting 100,000 object classes, and more ini ke social media anda, Semoga rezeki berlimpah ikut dimudahkan Allah bagi anda, Lebih jelas infonya lansung dibawah -->
I promised to blog about some more exciting papers at CVPR 2013, so here is a short list of a few papers which stood out. This list also include this year's award winning paper: Fast, Accurate Detection of 100,000 Object Classes on a Single Machine. Congrats Google Research on the excellent paper!
This paper uses ideas from Abhinav Gupta's work on 3D scene understanding as well as Ali Farhadi's work on visual phrases; however, it also uses RGB-D input data (like many other CVPR 2013 papers).
W. Choi, Y. -W. Chao, C. Pantofaru, S. Savarese. "Understanding Indoor Scenes Using 3D Geometric Phrases" in CVPR, 2013. [pdf]
This paper shows a uses the crowd to learn which parts of birds are useful for fine-grained categorization. If you work on fine-grained categorization or run experiments with MTurk, then you gotta check this out!
Fine-Grained Crowdsourcing for Fine-Grained Recognition. Jia Deng, Jonathan Krause, Li Fei-Fei. CVPR, 2013. [ pdf ]
This paper won the best paper award. Congrats Google Research!
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine. Thomas Dean, Mark Ruzon, Mark Segal, Jon Shlens, Sudheendra Vijayanarasimhan, Jay Yagnik. CVPR, 2013 [pdf]
The following is the Scene-SIRFs paper, which I thought was one of the best papers at this year's CVPR. The ideas to to decompose an input image into intrinsic images using Barron's algorithm which was initially shown to work on objects, but now is being applied to realistic scenes.
Intrinsic Scene Properties from a Single RGB-D Image. Jonathan T. Barron, Jitendra Malik. CVPR, 2013 [pdf]
This is a graph-based localization paper which uses a sort of "Visual Memex" to solve the problem.
Graph-Based Discriminative Learning for Location Recognition. Song Cao, Noah Snavely. CVPR, 2013. [pdf]
This paper provides an exciting new way of localizing contours in images which is orders of magnitude faster than the gPb. There is code available, so the impact is likely to be high.
Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection. Joseph J. Lim, C. Lawrence Zitnick, and Piotr Dollar. CVPR 2013. [ pdf ] [code@github]
Demikian info [Awesome@CVPR2013] Scene-SIRFs, Sketch Tokens, Detecting 100,000 object classes, and more, Semoga dengan adanya postingan ini, Anda sudah benar benar menemukan informasi yang memang sedang anda butuhkan saat ini. Bagikan informasi [Awesome@CVPR2013] Scene-SIRFs, Sketch Tokens, Detecting 100,000 object classes, and more ini untuk orang orang terdekat anda, Bagikan infonya melalui fasilitas layanan Share Facebook maupun Twitter yang tersedia di situs ini.