How do we know that we solved vision
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How do we know that we solved vision?. 16-721: Learning-Based Methods in Vision A. Efros, CMU, Spring 2009. Columbia Object Image Library (COIL-100) (1996). Corel Dataset. Yu & Shi, 2004. Average Caltech categories (Torralba). { }. all photos. Flickr.com. Flickr Paris. Real Paris.

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How do we know that we solved vision

How do we know that we solved vision?

16-721: Learning-Based Methods in Vision

A. Efros, CMU, Spring 2009


How do we know that we solved vision

Columbia Object Image Library (COIL-100) (1996)


How do we know that we solved vision

Corel Dataset


How do we know that we solved vision

Yu & Shi, 2004


How do we know that we solved vision

Average Caltech categories (Torralba)


How do we know that we solved vision

{ }

all photos

Flickr.com


Flickr paris

Flickr Paris


Real paris

Real Paris


Automated data collection

Automated Data Collection

Kang, Efros, Hebert, Kanade, 2009


Something more objective

Something More Objective?

Famous Tsukuba Image

Middlebury Stereo Dataset


Issue 1

Issue 1

  • We might be testing too soon…

  • Need to evaluate the entire system:

    • Give it enough data

    • Ground it in the physical world

    • Allow it to affect / manipulate its environment

  • Do we need to solve Hard AI?

    • Maybe not. We don’t need Human Vision per se – how about Rat Vision?


Issue 2

Issue 2

  • We might be looking for “magic” where none exist…


How do we know that we solved vision

Valentino Braitenberg, Vehicles

Source Material: http://www.bcp.psych.ualberta.ca/~mike/

Pearl_Street/Margin/Vehicles/index.html

Introduces a series of (hypothetical) simple robots that seem,

to the outside observer, to exhibit complex behavior.

The complex behavior does not come from a complex brain, but

from a simple agent interacting with a rich environment.

Vehicle 1: Getting around

A single sensor is attached to a single motor.

Propulsion of the motor is proportional to the signal

detected by the sensor.

The vehicle will always move in a straight line,

slowing down in the cold, speeding up in the warm.

Braitenberg: “Imagine, now, what you would think if you saw such a vehicle swimming around in a pond. It is restless, you would say, and does not like warm water. But it is quite stupid, since it is not able to turn back to the nice cold sport it overshot in its restless ness. Anyway, you would say, it is ALIVE, since you have never seen a particle of dead matter move around quite like that.”


More complex vehicles

More complex vehicles


Moral of the story

Moral of the Story

  • “Law of Uphill Analysis and Downhill Invention: machines are easy to understand if you’re creating them; much harder to understand ‘from the outside’.

  • Psychological consequence: if we don’t know the internal structure of a machine, we tend to overestimate its complexity.”


Turing tests for vision

Turing Tests for Vision

  • Your thoughts…


Have we solved vision if we solve all the boundary cases

Have we solved vision if we solve all the boundary cases?

Varum


Computer vision database zhaoyin jia

Computer Vision Database Zhaoyin Jia

During the Spring break

Before the deadline

In the class

Best project in 16721

Love

Kiss

Failed in 16721

Cute

Adorable

Safe

Threatened

Run

Call for help

More threatened

Run faster

Need more help

  • Object segmentation/recognition

    Detailed segmented/labeled, all the scenes in life.

  • Semantic meaning in image/video

    Human understanding of the image/story behind the image

  • Feeling/reaction after understanding


How do we know that we solved vision

How do we know that we solved vision?

Yuandong Tian

  • General Rule: Turing test

  • IfCVS == HVSin

  • Training&Performance&Speed&Failure case

  • ThenWe declarevision is solved. Beers and Being laid off.

  • Verifiable Specific Rules:

  • Challenges in Training

  • Full-automatic object Discovery & Categorization from unlabeled, long video sequence.

  • Multi-view robust real-timeRecognition of ten of thousands of objects, given few trainings of each object.

  • Challenges in Performance

  • Pixel-wise Localization and Registration in cluttered and degraded scene;

  • Long-term real-time robust Tracking for generic objects in cluttered and degraded video sequence.

  • Human failure – human vision illusion

  • Able to explain human vision illusions, and Reproduce them.

  • Conclusion:

  • Good luck for all!

16-721: Learning-based method in vision


Turing test for vision

Turing Test for Vision

  • From the blog:

    • No overall test. Vision is task-dependent. Do one problem at a time.

    • Use Computer Graphics to generate tons of test data

    • A well-executed Grand Challenge

      • Genre Classification in Video

    • The Ultimate Dataset (25-year-old grad student)

    • Need to handle corner cases / illusions. “Dynamic range of difficulty”. 

    • It’s all about committees, independent evaluations, and releasing source code

    • It’s hopeless…


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