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» Annotator Rationales for Visual Recognition
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IJSI
2008
156views more  IJSI 2008»
13 years 7 months ago
Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
Mohamed Farouk Abdel Hady, Friedhelm Schwenker
SIGIR
2010
ACM
13 years 5 months ago
Multimedia with a speech track: searching spontaneous conversational speech
After two successful years at SIGIR in 2007 and 2008, the third workshop on Searching Spontaneous Conversational Speech (SSCS 2009) was held conjunction with the ACM Multimedia 20...
Martha Larson, Roeland Ordelman, Franciska de Jong...
ICRA
2009
IEEE
226views Robotics» more  ICRA 2009»
13 years 5 months ago
3D model selection from an internet database for robotic vision
Abstract-- We propose a new method for automatically accessing an internet database of 3D models that are searchable only by their user-annotated labels, for using them for vision ...
Ulrich Klank, Muhammad Zeeshan Zia, Michael Beetz
CVPR
2012
IEEE
11 years 10 months ago
Multi-attribute spaces: Calibration for attribute fusion and similarity search
Recent work has shown that visual attributes are a powerful approach for applications such as recognition, image description and retrieval. However, fusing multiple attribute scor...
Walter J. Scheirer, Neeraj Kumar, Peter N. Belhume...
CVPR
2009
IEEE
15 years 2 months ago
Learning a Distance Metric from Multi-instance Multi-label Data
Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. ...
Rong Jin (Michigan State University), Shijun Wang...