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- Varun K. Nagaraja, Vlad I. Morariu, Larry S. Davis
- ECCV
- 2016

Referring expressions usually describe an object using properties of the object and relationships of the object with other objects. We propose a technique that integrates context between objects to understand referring expressions. Our approach uses an LSTM to learn the probability of a referring expression, with input features from a region and a context… (More)

- Varun K. Nagaraja, Vlad I. Morariu, Larry S. Davis
- BMVC
- 2015

To identify the location of objects of a particular class, a passive computer vision system generally processes all the regions in an image to finally output few regions. However , we can use structure in the scene to search for objects without processing the entire image. We propose a search technique that sequentially processes image regions such that the… (More)

- Varun K. Nagaraja, Wael Abd-Almageed
- 2015 International Joint Conference on Neural…
- 2015

We propose a supervised feature selection technique called the Optimal Loadings, that is based on applying the theory of Optimal Experiment Design (OED) to Partial Least Squares (PLS) regression. We apply the OED criterions to PLS with the goal of selecting an optimal feature subset that minimizes the variance of the regression model and hence minimize its… (More)

Before deriving the score functions, we first formulate the MAP inference problem in binary Markov networks as an Integer Linear Program (ILP) following the work of Globerson and Jaakkola [1]. The integer variables in the ILP are then relaxed to continuous values giving us a relaxed linear program. We then obtain the dual of this relaxed linear program and… (More)

- Varun K. Nagaraja, Vlad I. Morariu, Larry S. Davis
- ECCV Workshops
- 2014

High level semantic analysis typically involves constructing a Markov network over detections from low level detectors to encode context and model relationships between them. In complex higher order networks (e.g. Markov Logic Networks), each detection can be part of many factors and the network size grows rapidly as a function of the number of detections.… (More)

- Wael Abd-Almageed, Varun Nagaraja, § Mohamed Abdelkader, Mohamed Hussein, Bahadir Ozdemir, Wael AbdAlamgeed +10 others
- 2013

Feature selection is an essential problem in many fields such as computer vision. In this paper we introduce a supervised feature selection criterion based on Partial Least Squares regression (PLS). We find an optimal feature subset by applying the theory of Optimal Experiment Design to optimize the eigenvalues of the loadings matrix obtained from PLS.… (More)

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