@conference {14192,
	title = {Active scene recognition with vision and language},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {810 - 817},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a novel approach to utilizing high level knowledge for the problem of scene recognition in an active vision framework, which we call active scene recognition. In traditional approaches, high level knowledge is used in the post-processing to combine the outputs of the object detectors to achieve better classification performance. In contrast, the proposed approach employs high level knowledge actively by implementing an interaction between a reasoning module and a sensory module (Figure 1). Following this paradigm, we implemented an active scene recognizer and evaluated it with a dataset of 20 scenes and 100+ objects. We also extended it to the analysis of dynamic scenes for activity recognition with attributes. Experiments demonstrate the effectiveness of the active paradigm in introducing attention and additional constraints into the sensing process.},
	keywords = {accuracy, active scene recognition, classification performance, Cognition, Computer vision, Detectors, Equations, high level knowledge utilization, HUMANS, image classification, inference mechanisms, object detectors, Object recognition, reasoning module, sensing process, sensory module, support vector machines, Training},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126320},
	author = {Yu,Xiaodong and Ferm{\"u}ller, Cornelia and Ching Lik Teo and Yezhou Yang and Aloimonos, J.}
}
@conference {12413,
	title = {Simulating Audiences: Automating Analysis of Values, Attitudes, and Sentiment},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {734 - 737},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Current events such as the Park51 Project in downtown Manhattan create "critical discourse moments," explosions of discourse around a topic that can be exploited for data gathering. Policymakers have a need to understand the dynamics of public discussion in real time. Human values, which are cognitively related to attitudes and serve as reference points in moral argument, are important indicators of what{\textquoteright}s at stake in a public controversy. This work shows that it is possible to link values data with reader behavior to infer values implicit in a topical corpus, and that it is possible to automate this process using machine learning.},
	keywords = {audience simulation, behavioural sciences computing, crowdsourcing, Educational institutions, human values, HUMANS, learning (artificial intelligence), machine learning, moral argument, natural language processing, Park51 project, Presses, public controversy, public discussion, Security, social sciences computing, support vector machines, Weaving},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.238},
	author = {Templeton,T.C. and Fleischmann,K.R. and Jordan Boyd-Graber}
}
@conference {12441,
	title = {Synthesis-based recognition of low resolution faces},
	booktitle = {2011 International Joint Conference on Biometrics (IJCB)},
	year = {2011},
	month = {2011/10/11/13},
	pages = {1 - 6},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Recognition of low resolution face images is a challenging problem in many practical face recognition systems. Methods have been proposed in the face recognition literature for the problem when the probe is of low resolution, and a high resolution gallery is available for recognition. These methods modify the probe image such that the resultant image provides better discrimination. We formulate the problem differently by leveraging the information available in the high resolution gallery image and propose a generative approach for classifying the probe image. An important feature of our algorithm is that it can handle resolution changes along with illumination variations. The effective- ness of the proposed method is demonstrated using standard datasets and a challenging outdoor face dataset. It is shown that our method is efficient and can perform significantly better than many competitive low resolution face recognition algorithms.},
	keywords = {Dictionaries, Face, face images, face recognition, face recognition literature, face recognition systems, illumination variations, image resolution, low resolution faces, Organizations, PROBES, support vector machines, synthesis based recognition},
	isbn = {978-1-4577-1358-3},
	doi = {10.1109/IJCB.2011.6117545},
	author = {Shekhar, S. and Patel, Vishal M. and Chellapa, Rama}
}
