ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image
Collections
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Bolei Zhou, Vignesh Jagadeesh, Robinson Piramuthu
2014
Abstract
Discovering visual knowledge from weakly labeled data is crucial to scale up
computer vision recognition system, since it is expensive to obtain fully
labeled data for a large number of concept categories. In this paper, we
propose ConceptLearner, which is a scalable approach to discover visual
concepts from weakly labeled image collections. Thousands of visual concept
detectors are learned automatically, without human in the loop for additional
annotation. We show that these learned detectors could be applied to recognize
concepts at image-level and to detect concepts at image region-level
accurately. Under domain-specific supervision, we further evaluate the learned
concepts for scene recognition on SUN database and for object detection on
Pascal VOC 2007. ConceptLearner shows promising performance compared to fully
supervised and weakly supervised methods.
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