16-824: learning-based methods in vision instructors: alexei (alyosha) efros efros@cs.cmu.edu, 225...

16-824: Learning-based Methods in Vision

Instructors:• Alexei (Alyosha) Efros

efros@cs.cmu.edu, 225 Smith Hall

• Leon Sigal lsigal@disneyresearch.com, Disney Research Pittsburgh

Web Page:• http://www.cs.cmu.edu/~efros/courses/LBMV12

/

Today

Introduction

Why This Course?

Administrative stuff

Overview of the course

Alexei (Alyosha) Efros

Ph.D 2003, from UC Berkeley (signed by Arnie!)

Postdoctoral Fellow, University of Oxford, ’03-’04

Research Interests:

Vision, Graphics, Data-driven “stuff”

Leonid Sigal

PhD 2007, from Brown University

Postdoctoral Fellow, University of Toronto, ’07-’09

Research interests:

Vision, Graphics, Machine Learning

A bit about Us

Why this class?

The Old Days™:

1. Graduate Computer Vision

2. Advanced Machine Perception

Why this class?

The New and Improved Days:

1. Graduate Computer Vision

2. Advanced Machine Perception• Physics-based Methods in Vision• Geometry-based Methods in Vision• Learning-based Methods in Vision

Describing Visual Scenes using Transformed Dirichlet Processes. E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. NIPS, Dec. 2005.

The Hip & Trendy Learning

Learning as Last Resort

Learning as Last Resort

from [Sinha and Adelson 1993]

EXAMPLE: Recovering 3D geometry from

single 2D projection

Infinite number of possible solutions!

Learning-based Methods in Vision

This class is about trying to solve problems that do not have a solution! • Don’t tell your mathematician frineds!

This will be done using Data:• E.g. what happened before is likely to happen again• Google Intelligence (GI): The AI for the post-modern world!• Note: this is not quite statistics

Why is this even worthwhile?• Even a decade ago at ICCV99 Faugeras claimed it wasn’t!

The Vision Story Begins…

“What does it mean, to see? The plain man's answer (and Aristotle's, too). would be, to know what is where by looking.”

-- David Marr, Vision (1982)

Vision: a split personality“What does it mean, to see? The plain man's answer (and

Aristotle's, too). would be, to know what is where by looking. In other words, vision is the process of discovering from images what is present in the world, and where it is.”

Answer #1: pixel of brightness 243 at position (124,54)

…and depth .7 meters

Answer #2: looks like bottom edge of whiteboard showing at the top of the image

Which do we want?

Is the difference just a matter of scale?

depth map

Measurement vs. Perception

Brightness: Measurement vs. Perception

Brightness: Measurement vs. Perception

Proof!

Lengths: Measurement vs. Perception

http://www.michaelbach.de/ot/sze_muelue/index.html

Müller-Lyer Illusion

Vision as Measurement Device

Real-time stereo on Mars

Structure from Motion

Physics-based Vision

Virtualized Reality

…but why do Learning for Vision?“What if I don’t care about this wishy-washy human

perception stuff? I just want to make my robot go!”

Small Reason: • For measurement, other sensors are often better (in DARPA

Grand Challenge, vision was barely used!)• For navigation, you still need to learn!

Big Reason:

The goals of computer vision (what + where) are in terms of what humans care about.

So what do humans care about?

slide by Fei Fei, Fergus & Torralba

Verification: is that a bus?

slide by Fei Fei, Fergus & Torralba

Detection: are there cars?

slide by Fei Fei, Fergus & Torralba

Identification: is that a picture of Mao?

slide by Fei Fei, Fergus & Torralba

Object categorization

sky

building

flag

wallbanner

bus

cars

bus

face

street lamp

slide by Fei Fei, Fergus & Torralba

Scene and context categorization• outdoor

• city

• traffic

• …

slide by Fei Fei, Fergus & Torralba

Rough 3D layout, depth ordering

slide by Fei Fei, Fergus & Torralba

Challenges 1: view point variation

Michelangelo 1475-1564 slide by Fei Fei, Fergus & Torralba

Challenges 2: illumination

slide credit: S. Ullman

Challenges 3: occlusion

Magritte, 1957 slide by Fei Fei, Fergus & Torralba

Challenges 4: scale

slide by Fei Fei, Fergus & Torralba

Challenges 5: deformation

Xu, Beihong 1943slide by Fei Fei, Fergus & Torralba

Challenges 6: background clutter

Klimt, 1913 slide by Fei Fei, Fergus & Torralba

Challenges 7: object intra-class variation

slide by Fei-Fei, Fergus & Torralba

Challenges 8: local ambiguity

slide by Fei-Fei, Fergus & Torralba

Challenges 9: the world behind the image

In this course, we will:

Take a few baby steps…

Role of Learning

Learning Algorithm

Features

Data

Role of Learning

DataFeatures

Algorithm

Shashua

Course Outline

• Overview of Learning for Vision (1 lecture)• Overview of Data for Vision (1 lecture)• Features

• Human Perception and visual neuroscience• Theories of Human Vision• Low-level Vision

• Filters, edge detection, interest points, etc.

• Mid-level Vision• Segmentation, Occlusions, 2-1/2D, scene layout, etc.

• High-Level Vision• Object recognition

• Scene Understanding

• Action / Motion Understaing

• Etc.

Goals

Read some interesting papers together• Learn something new: both you and us!

Get up to speed on big chunk of vision research• understand 70% of CVPR papers!

Use learning-based vision in your own work

Learn how to speakLearn how think critically about papersParticipate in an exciting meta-study!

Course Organization

Requirements:1. Class Participation (33%)

• Keep annotated bibliography• Post on the Class Blog before each class • Ask questions / debate / flight / be involved!

2. Two Projects (66%)• Deconstruction Project

• Implement and Evaluate a paper and present it in class• Must talk to us AT LEAST 2 weeks beforehand!• Can be done in groups of 2 (but must do 2 projects)

• Synthesis Project• Do something worthwhile with what you learned for

Deconstruction Project• Can be done in groups of 2 (1 project)

Class ParticipationKeep annotated bibliography of papers you read (always

a good idea!). The format is up to you. At least, it needs to have:• Summary of key points• A few Interesting insights, “aha moments”, keen observations,

etc.• Weaknesses of approach. Unanswered questions. Areas of

further investigation, improvement.

Before each class:• Submit your summary for current paper(s) in

hard copy (printout/xerox)• Submit a comment on the Class Blog

• ask a question, answer a question, post your thoughts,praise, criticism, start a discussion, etc.

Deconstruction Project1. Pick a paper / set of papers from the list2. Understand it as if you were the author

• Re-implement it• If there is code, understand the code completely• Run it on data the same data (you can contact authors for data and

even code sometimes)

3. Understand it better than the author• Run it on two other data sets (e.g. LabelMe dataset, Flickr dataset,

etc, etc)• Run it with two other feature representations• Run it with two other learning algorithms• Maybe suggest directions for improvement.

4. Prepare an amazing 45min presentation• Discuss with me twice – once when you start the project, 3 days

before the presentation

Synthesis Project

Hopefully can grow out of the deconstruction project

2 people can work on one

End of Semester Awards

We will vote for:• Best Deconstruction Project• Best Synthesis Project• Best Blog Comment

Prize: dinner in a French restaurant in Paris (transportation not included!) or some other worthy prizes