Visual Learning and Recognition: Difference between revisions

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==Extra Topics==

===Fine-grained Recognition===

===Few-shot Recognition===

* Metric learning methods

* Meta-learning methods

* Data Augmentation Methods

* Semantics

===Zero-shot Recognition===

Goal is train a classifier without having seen a single labeled example.

The information comes from a knowledge graph e.g. from word embeddings.

===Beyond Labelled Datasets===

* Semi-supervised: We have both labelled and unlabeled training samples.

* Weakly-supervised: The labels are weak, noisy, and non-necessarily for the task we want.

* Learning from the Web: Download data from the internet

==Will be on the exam==

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* Challenges

* What methods worked and didn't work.

~~==Misc==~~

~~[[Visible to::users]]~~

==References==

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<ref name="torralba2008tinyimages">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 Link]</ref>

<ref name="standing1973learning">Lionel Standing (1973). Learning 10000 pictures. ''Journal

<ref name="standing1973learning">Lionel Standing (1973). Learning 10000 pictures. ''Journal Quarterly Journal of Experimental Psychology'' [https://www.tandfonline.com/doi/abs/10.1080/14640747308400340 Link]</ref>

Quarterly Journal of Experimental Psychology'' [https://www.tandfonline.com/doi/abs/10.1080/14640747308400340 Link]</ref>

<ref name="brady2008visual">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 Link].</ref>

<ref name="torralba2011unbiased>Antonio Torralba, Alexei A. Efros (2011). Unbiased Look at Dataset Bias (CVPR 2011) [https://people.csail.mit.edu/torralba/publications/datasets_cvpr11.pdf Link]</ref>

@@ Line 1,230: / Line 1,230: @@
 ==Extra Topics==
 ===Fine-grained Recognition===
+===Few-shot Recognition===
+* Metric learning methods
+* Meta-learning methods
+* Data Augmentation Methods
+* Semantics
+===Zero-shot Recognition===
+Goal is train a classifier without having seen a single labeled example.
+The information comes from a knowledge graph e.g. from word embeddings.
+===Beyond Labelled Datasets===
+* Semi-supervised: We have both labelled and unlabeled training samples.
+* Weakly-supervised: The labels are weak, noisy, and non-necessarily for the task we want.
+* Learning from the Web: Download data from the internet
 ==Will be on the exam==
@@ Line 1,267: / Line 1,281: @@
 * Challenges
 * What methods worked and didn't work.
-==Misc==
-[[Visible to::users]]
 ==References==
@@ Line 1,275: / Line 1,286: @@
 <ref name="torralba2008tinyimages">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 Link]</ref>
-<ref name="standing1973learning">Lionel Standing (1973). Learning 10000 pictures. ''Journal
+<ref name="standing1973learning">Lionel Standing (1973). Learning 10000 pictures. ''Journal Quarterly Journal of Experimental Psychology'' [https://www.tandfonline.com/doi/abs/10.1080/14640747308400340 Link]</ref>
-Quarterly Journal of Experimental Psychology'' [https://www.tandfonline.com/doi/abs/10.1080/14640747308400340 Link]</ref>
 <ref name="brady2008visual">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 Link].</ref>
 <ref name="torralba2011unbiased>Antonio Torralba, Alexei A. Efros (2011). Unbiased Look at Dataset Bias (CVPR 2011) [https://people.csail.mit.edu/torralba/publications/datasets_cvpr11.pdf Link]</ref>