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.

本研究提出了 ConceptLearner，一种可扩展的方法来从弱标记的图像集合中自动学习并发现视觉概念检测器，并表明 ConceptLearner 能够应用于图像级别的概念识别和图像区域级别的概念检测，并在 SUN 数据库和 Pascal VOC 2007 上对场景识别和物体检测的学习表现显示出良好的性能。