Visual Learning and Recognition: Difference between revisions
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(Simple algorithms + big data) is better than (complicated algorithms + small data) | (Simple algorithms + big data) is better than (complicated algorithms + small data) | ||
==Data-driven Methods in Vision== | ==Data-driven Methods in Vision== | ||
Lecture 4 (September 10, 2020) | |||
This lecture is on the bias of data. It follows Torralba ''et al.''<ref name="torralba2011unbiased"></ref> | |||
;Will big data solve all our problems? | |||
E.g. Can (big company) just dump millions of dollars to collect data and solve any problem? | |||
No. E.g. COVID. | |||
There will always be new tasks or problems. | |||
===We will never have enough data=== | |||
Long tails - Zipf's law | |||
===Data is biased=== | |||
Types of visual bias: | |||
* Observer Bias (human vs bird) | |||
* Capture Bias (photographer vs robot) | |||
* Selection Bias (Flickr vs Google Street View) | |||
* Category/Label Bias | |||
* Negative Set Bias | |||
In general, all datasets will have all of these biases mixed in. | |||
* Social Bias | |||
Graduation photos always have a certain structure. | |||
==Will be on the exam== | ==Will be on the exam== | ||
Revision as of 17:28, 10 September 2020
Notes for CMSC828I Visual Learning and Recognition (Fall 2020) taught by Abhinav Shrivastava
This class covers:
- How a sub-topic evolved
- State of the art
Introduction to Data
September 8, 2020
The extremes of data.
If we have very few images, we are working on an extrapolation problem.
As we approach an infinite number of training samples, learning becomes an interpolation problem.
Traditional datasets are in the order of \(10^2-10^4\) training samples.
Current datasets are in the order of \(10^5-10^7\) training samples.
In tiny images [1], Torrabla et al. use 80 million tiny images.
- What is the capacity of visual long term memory?
In Standing (1973)[2], people could recall whether they've seen 10,000 images with 83% recognition.
- What we don't know is what people are remembering for each item?
In Brady et al.[3], they tested recall for novel (new object), exemplar (same type of object), and state (same object & state). They got 92% for novel, 88% for exemplar, and 87% for state so humans remember the exact state of objects they've seen.
- Rule of thumb
(Simple algorithms + big data) is better than (complicated algorithms + small data)
Data-driven Methods in Vision
Lecture 4 (September 10, 2020)
This lecture is on the bias of data. It follows Torralba et al.[4]
- Will big data solve all our problems?
E.g. Can (big company) just dump millions of dollars to collect data and solve any problem?
No. E.g. COVID.
There will always be new tasks or problems.
We will never have enough data
Long tails - Zipf's law
Data is biased
Types of visual bias:
- Observer Bias (human vs bird)
- Capture Bias (photographer vs robot)
- Selection Bias (Flickr vs Google Street View)
- Category/Label Bias
- Negative Set Bias
In general, all datasets will have all of these biases mixed in.
- Social Bias
Graduation photos always have a certain structure.
Will be on the exam
- Back-prop and SGD,
- Softmax, sigmoid, cross entropy
Misc
References
- ↑ Antonio Torralba, Rob Fergus and William T. Freeman (2008). 80 million tiny images: a large dataset for non-parametric object and scene recognition (PAMI 2008) https://people.csail.mit.edu/torralba/publications/80millionImages.pdf
- ↑ Lionel Standing (1973). Learning 10000 pictures. Journal Quarterly Journal of Experimental Psychology https://www.tandfonline.com/doi/abs/10.1080/14640747308400340
- ↑ Timothy F. Brady, Talia Konkle, George A. Alvarez, and Aude Oliva (2008). Visual long-term memory has a massive storage capacity for object details. http://olivalab.mit.edu/MM/pdfs/BradyKonkleAlvarezOliva2008.pdf.
- ↑ Cite error: Invalid
<ref>tag; no text was provided for refs namedtorralba2011unbiased