Learning Everything About Anything Webly-Supervised Visual Concept Learning
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Santosh Divvala Ali Farhadi Carlos Guestrin
How can we learn everything about anything?
Problems with human supervision
Key Challenges:
• How to gather training data
(queries, images, annotations)?
• How to tame intra-class
variance?
• Biased, non-comprehensive
• Concept-specific expertise
• Early hard decisions
Proposed approach: Webly-supervised learning
Results: Relationships discovered
Results: Weakly-supervised detection on PASCAL VOC
http://levan.cs.uw.edu
Fully automated system: Train your own concept!
Which detection to pick? What defines a category?
Open challenges
Merging synonyms Detector training
237 Concepts 75,000,000 images
90,000 detectors 18,000,000 annotations