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| November 1

| November 1

| Raviteja Vemulapalli

| Mohammad Rastegari

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| November 15

| November 15

| Ang Li

| ''(CVPR deadline, no meeting)''

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===Attribute Discovery via Predictable and Discriminative Binary Codes===

===Attribute Discovery via Predictable and Discriminative Binary Codes===

Speaker: [http://www.cs.dartmouth.edu/~mrastegari/ Mohammad Rastegari] -- Date: September 27, 2012

Speaker: [http://www.cs.umd.edu/~mrastega/ Mohammad Rastegari] -- Date: September 27, 2012

We present images with binary codes in a way that balances discrimination and learnability of the codes. In our method, each image claims its own code in a way that maintains discrimination while being predictable from visual data. Category memberships are usually good proxies for visual similarity but should not be enforced as a hard constraint. Our method learns codes that maximize separability of categories unless there is strong visual evidence against it. Simple linear SVMs can achieve state-of-the-art results with our short codes. In fact, our method produces state-of-the-art results on Caltech256 with only 128- dimensional bit vectors and outperforms state of the art by using longer codes. We also evaluate our method on ImageNet and show that our method outperforms state-of-the-art binary code methods on this large scale dataset. Lastly, our codes can discover a discriminative set of attributes.

We present images with binary codes in a way that balances discrimination and learnability of the codes. In our method, each image claims its own code in a way that maintains discrimination while being predictable from visual data. Category memberships are usually good proxies for visual similarity but should not be enforced as a hard constraint. Our method learns codes that maximize separability of categories unless there is strong visual evidence against it. Simple linear SVMs can achieve state-of-the-art results with our short codes. In fact, our method produces state-of-the-art results on Caltech256 with only 128- dimensional bit vectors and outperforms state of the art by using longer codes. We also evaluate our method on ImageNet and show that our method outperforms state-of-the-art binary code methods on this large scale dataset. Lastly, our codes can discover a discriminative set of attributes.

===TBA===

===TBA===

Speaker: Raviteja Vemulapalli -- Date: November 1, 2012

Speaker: [http://www.cs.umd.edu/~mrastega/ Mohammad Rastegari] -- Date: November 1, 2012

===TBA===

===TBA===