ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections

Understand

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.