Compared to basic level recogni tion 64257negrained categorization can be more challenging as there are in general less data and fewer discriminative features This necessitates the use of stronger prior for fea ture selection In this work we include ID: 7278 Download Pdf
Compared to basic level recogni tion 64257negrained categorization can be more challenging as there are in general less data and fewer discriminative features This necessitates the use of stronger prior for fea ture selection In this work we include
Stateoftheart approaches achieve remarkable performance when training data is plentiful but they are typically tied to 64258at 2D represen tations that model objects as a collection of unconnected views limiting their ability to generalize across vi
stanfordedu Abstract In this paper we study the problem of 64257negrained im age categorization The goal of our method is to explore 64257ne image statistics and identify the discriminative image patches for recognition We achieve this goal by combin
San Jose CA cwahcsucsdedu wedianbhardwajrpiramuthunsundaresan ebaycom Abstract With the rapid proliferation of smartphones and tablet computers search has moved beyond text to other modali ties like images and voice For many applications like Fash i
San Jose CA cwahcsucsdedu wedianbhardwajrpiramuthunsundaresan ebaycom Abstract With the rapid proliferation of smartphones and tablet computers search has moved beyond text to other modali ties like images and voice For many applications like Fash i
However in some cases it is possible to utilize device resources more e64259ciently by running kernels concurrently This raises questions about load balancing and resource allocation that have not previ ously warranted investigation For example what
Alexander David W Jacobs and Peter N Belhumeur Columbia University University of Maryland Abstract We address the problem of largescale 64257negrained vi sual categorization describing new methods we have used to produce an online 64257eld guide t
columbiaedu Peter N Belhumeur Columbia University belhumeurcscolumbiaedu Abstract From a set of images in a particular domain labeled with part locations and class we present a method to automati cally learn a large and diverse set of highly discrimi
Chapin Department of EECS Syracuse University kjayaramwedubarajagochapin syredu Abstract Web applications are no longer simple hyperlinked doc uments They have progressively evolved to become highly complexweb pages combine content from several sour
Meyerovich University of California Berkeley lmeyeroveecsberkeleyedu Benjamin Livshits Microsoft Research livshitsmicrosoftcom Abstract Much of the power of modern Web comes from the ability of a Web page to combine content and JavaScript code from
