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The Pascal Visual Object Classes (VOC) Challenge
The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
The Pascal Visual Object Classes Challenge: A Retrospective
A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation
A new annotated database of challenging consumer images is introduced, an order of magnitude larger than currently available datasets, and over 50% relative improvement in pose estimation accuracy over a state-of-the-art method is demonstrated.
The PASCAL visual object classes challenge 2006 (VOC2006) results
This report presents the results of the 2006 PASCAL Visual Object Classes Challenge (VOC2006). Details of the challenge, data, and evaluation are presented. Participants in the challenge submitted
Learning effective human pose estimation from inaccurate annotation
A significant increase in pose estimation accuracy is demonstrated, while simultaneously reducing computational expense by a factor of 10, and a dataset of10,000 highly articulated poses is contributed.
Hello! My name is... Buffy'' -- Automatic Naming of Characters in TV Video
It is demonstrated that high precision can be achieved by combining multiple sources of information, both visual and textual, by automatic generation of time stamped character annotation by aligning subtitles and transcripts.
The 2005 PASCAL Visual Object Classes Challenge
This chapter provides details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved in the PASCAL Visual Object Classes Challenge.
Learning Models for Object Recognition from Natural Language Descriptions
This work proposes natural language processing methods for extracting salient visual attributes from natural language descriptions to use as ‘templates’ for the object categories, and applies vision methods to extract corresponding attributes from test images.
Person Spotting: Video Shot Retrieval for Face Sets
Progress is described in harnessing multiple exemplars of each person in a form that can easily be associated automatically using straightforward visual tracking in order to retrieve humans automatically in videos, given a query face in a shot.