The Role of Learning in Vision 3.30pm: Rob Fergus 3.40pm: Andrew Ng 3.50pm: Kai Yu 4.00pm: Yann LeCun 4.10pm: Alan Yuille 4.20pm: Deva Ramanan 4.30pm: Erik Learned-Miller 4.40pm: Erik Sudderth 4.50pm: Spotlights - Qiang Ji, M-H Yang 4.55pm: Discussion 5.30pm: End Feature / Deep Learnin Compositional Models Learning Representatio Overview Low-level Representatio Learning on the fly
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The Role of Learning in Vision
3.30pm: Rob Fergus3.40pm: Andrew Ng3.50pm: Kai Yu4.00pm: Yann LeCun4.10pm: Alan Yuille4.20pm: Deva Ramanan4.30pm: Erik Learned-Miller4.40pm: Erik Sudderth4.50pm: Spotlights
- Qiang Ji, M-H Yang4.55pm: Discussion5.30pm: End
Feature / Deep Learning
Compositional Models
Learning Representations
Overview
Low-level Representations
Learning on the fly
An Overview of Hierarchical Feature Learning and Relations to Other Models
Rob Fergus
Dept. of Computer Science, Courant Institute,
New York University
Motivation
• Multitude of hand-designed features currently in use– SIFT, HOG, LBP, MSER, Color-SIFT………….
• Maybe some way of learning the features?
• Also, just capture low-level edge gradients
Felzenszwalb, Girshick, McAllester and Ramanan, PAMI
2007
Yan & Huang (Winner of PASCAL 2010 classification
competition)
• Mid-level cues
Beyond Edges?
“Tokens” from Vision by D.Marr:
Continuation Parallelism Junctions Corners
• High-level object parts:
• Difficult to hand-engineer What about learning them?
• Build hierarchy of feature extractors (≥ 1 layers)– All the way from pixels classifier– Homogenous structure per layer– Unsupervised training