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