21![Web-based Models for Natural Language Processing MIRELLA LAPATA and FRANK KELLER University of Edinburgh Previous work demonstrated that web counts can be used to approximate bigram counts, thus suggesting that web-based Web-based Models for Natural Language Processing MIRELLA LAPATA and FRANK KELLER University of Edinburgh Previous work demonstrated that web counts can be used to approximate bigram counts, thus suggesting that web-based](https://www.pdfsearch.io/img/4c76862982f3a2bd8f2b94c7d902eea9.jpg) | Add to Reading ListSource URL: homepages.inf.ed.ac.ukLanguage: English - Date: 2006-01-25 12:04:52
|
---|
22![Position Statement on Physician Assistants Team-based models of medical care that are characterised by responsiveness to local needs, mutual reliance and flexibility have always been a part of rural and remote medicine. Position Statement on Physician Assistants Team-based models of medical care that are characterised by responsiveness to local needs, mutual reliance and flexibility have always been a part of rural and remote medicine.](https://www.pdfsearch.io/img/edee6575d1ef49659594dd12ba7ac6c7.jpg) | Add to Reading ListSource URL: www.acrrm.org.auLanguage: English - Date: 2014-04-03 14:48:42
|
---|
23![Spatial Priors for Part-Based Recognition using Statistical Models David Crandall1 Cornell University [removed] Pedro Felzenszwalb Spatial Priors for Part-Based Recognition using Statistical Models David Crandall1 Cornell University [removed] Pedro Felzenszwalb](https://www.pdfsearch.io/img/9ef8bdfa1691e6609e48c040239db6bf.jpg) | Add to Reading ListSource URL: vision.soic.indiana.eduLanguage: English - Date: 2014-08-03 00:38:07
|
---|
24![Pictorial Structures for Object Recognition Pedro F. Felzenszwalb Artificial Intelligence Lab, Massachusetts Institute of Technology [removed] Daniel P. Huttenlocher Computer Science Department, Cornell University Pictorial Structures for Object Recognition Pedro F. Felzenszwalb Artificial Intelligence Lab, Massachusetts Institute of Technology [removed] Daniel P. Huttenlocher Computer Science Department, Cornell University](https://www.pdfsearch.io/img/b69db2672f2e5fd2505992ffa74446a5.jpg) | Add to Reading ListSource URL: www.cs.cornell.eduLanguage: English - Date: 2004-04-04 22:57:52
|
---|
25![Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition David J. Crandall and Daniel P. Huttenlocher Cornell University, Ithaca, NY 14850, USA, {crandall,dph}@cs.cornell.edu Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition David J. Crandall and Daniel P. Huttenlocher Cornell University, Ithaca, NY 14850, USA, {crandall,dph}@cs.cornell.edu](https://www.pdfsearch.io/img/aed2dedde12fbb60a166d164bae73128.jpg) | Add to Reading ListSource URL: vision.soic.indiana.eduLanguage: English - Date: 2014-08-03 00:38:09
|
---|
26![Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition David J. Crandall and Daniel P. Huttenlocher Cornell University, Ithaca, NY 14850, USA, {crandall,dph}@cs.cornell.edu Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition David J. Crandall and Daniel P. Huttenlocher Cornell University, Ithaca, NY 14850, USA, {crandall,dph}@cs.cornell.edu](https://www.pdfsearch.io/img/80575991217dcaea20d278ca85ef31d9.jpg) | Add to Reading ListSource URL: www.cs.cornell.eduLanguage: English - Date: 2006-02-18 10:40:24
|
---|
27![Spatial Priors for Part-Based Recognition using Statistical Models David Crandall1 Cornell University [removed] Pedro Felzenszwalb Spatial Priors for Part-Based Recognition using Statistical Models David Crandall1 Cornell University [removed] Pedro Felzenszwalb](https://www.pdfsearch.io/img/ca49197d809113cd89608c27faedd456.jpg) | Add to Reading ListSource URL: www.cs.cornell.eduLanguage: English - Date: 2005-04-11 14:45:39
|
---|
28![1 Object Detection with Discriminatively Trained Part Based Models Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester and Deva Ramanan Abstract—We describe an object detection system based on mixtures of multis 1 Object Detection with Discriminatively Trained Part Based Models Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester and Deva Ramanan Abstract—We describe an object detection system based on mixtures of multis](https://www.pdfsearch.io/img/de3db395f37b249770518a723419161d.jpg) | Add to Reading ListSource URL: www.cs.berkeley.eduLanguage: English - Date: 2013-05-01 18:04:34
|
---|
29![Practical 3-D Object Detection Using Category and Instance-level Appearance Models Kate Saenko, Sergey Karayev, Yangqing Jia, Alex Shyr, Allison Janoch, Jonathan Long, Mario Fritz, Trevor Darrell Abstract— Effective ro Practical 3-D Object Detection Using Category and Instance-level Appearance Models Kate Saenko, Sergey Karayev, Yangqing Jia, Alex Shyr, Allison Janoch, Jonathan Long, Mario Fritz, Trevor Darrell Abstract— Effective ro](https://www.pdfsearch.io/img/3657cd0c311b22bb8032d451d33ee5da.jpg) | Add to Reading ListSource URL: sergeykarayev.comLanguage: English - Date: 2015-01-07 04:16:21
|
---|
30![Object Detection with Heuristic Coarse-to-Fine Search Ross Girshick May 29, 2009
Abstract Object Detection with Heuristic Coarse-to-Fine Search Ross Girshick May 29, 2009
Abstract](https://www.pdfsearch.io/img/a3f83cac79556efa96a8b03cc47599bd.jpg) | Add to Reading ListSource URL: www.cs.berkeley.eduLanguage: English - Date: 2013-05-01 18:04:37
|
---|