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| October 3
| October 3
| TBA
| Abhishek Sharma
| TBA
| A Sentence is Worth a Thousand Pixels
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| October 10
| October 10
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| November 14
| November 14
| Kota Hara
| TBA
| TBA
| TBA
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In this project we introduce a new method for learning image prior that can be used for many applications in image reconstruction. We learn a generative model on natural image patches. Our generative model is similar to one in Gausian Mixture Model (GMM). The key idea of our approach is to force each component of our generative model to share the same set of basis vectors. This leads to a much faster inference at test time. We used image denoising as our test bed for this image prior learning. Our experimental results shows that we reached about 30x speed up over state-of-the-art method while getting slightly improvement in denoising accuracy.
In this project we introduce a new method for learning image prior that can be used for many applications in image reconstruction. We learn a generative model on natural image patches. Our generative model is similar to one in Gausian Mixture Model (GMM). The key idea of our approach is to force each component of our generative model to share the same set of basis vectors. This leads to a much faster inference at test time. We used image denoising as our test bed for this image prior learning. Our experimental results shows that we reached about 30x speed up over state-of-the-art method while getting slightly improvement in denoising accuracy.
===A Sentence is Worth a Thousand Pixels===
Speaker: [http://www.umiacs.umd.edu/~bhokaal/ Abhishek Sharma] -- Date: October 3, 2013
We are interested in holistic scene understanding where images are accompanied with text in the form of complex sentential descriptions. We propose a holistic conditional random field model for semantic parsing which reasons jointly about which objects are present in the scene, their spatial extent as well as semantic segmentation, and employs text as well as image information as input. We automatically parse the sentences and extract objects and their relationships, and incorporate them into the model, both via potentials as well as by re-ranking candidate detections. We demonstrate the effectiveness of our approach in the challenging UIUC sentences dataset and show segmentation improvements of 12.5% over the visual only model and detection improvements of 5% AP over deformable part-based models.
