@conference {Zheng2014,
	title = {Dense semantic image segmentation with objects and attributes},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2014},
	pages = {3214{\textendash}3221},
	abstract = {The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e.g. {\textquoteright}I see a shiny red chair{\textquoteright}). In this paper, we formulate the problem of joint visual attribute and object class image segmentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels. In order to learn the label correlations, we adopt a boosting-based piecewise training approach with respect to the visual appearance and co-occurrence cues. We use a filtering-based mean-field approximation approach for efficient joint inference. Further, we develop a hierarchical model to incorporate region-level object and attribute information. Experiments on the aPASCAL, CORE and attribute augmented NYU indoor scenes datasets show that the proposed approach is able to achieve state-of-the-art results.},
	keywords = {Attributes, Image segmentation, Object recognition, Scene Understanding},
	isbn = {9781479951178},
	issn = {10636919},
	doi = {10.1109/CVPR.2014.411},
	url = {http://www.robots.ox.ac.uk/\~{}tvg/http://tu-dresden.de/inf/cvld},
	author = {Zheng, Shuai and Cheng, Ming Ming and Warrell, Jonathan and Sturgess, Paul and Vineet, Vibhav and Carsten Rother and Torr, Philip H.S.}
}
@conference {Vineet2013,
	title = {Higher order priors for joint intrinsic image, objects, and attributes estimation},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2013},
	abstract = {Many methods have been proposed to solve the problems of recovering intrinsic scene properties such as shape, reflectance and illumination from a single image, and object class segmentation separately. While these two problems are mutually informative, in the past not many papers have addressed this topic. In this work we explore such joint estimation of intrinsic scene properties recovered from an image, together with the estimation of the objects and attributes present in the scene. In this way, our unified framework is able to capture the correlations between intrinsic properties (reflectance, shape, illumination), objects (table, tv-monitor), and materials (wooden, plastic) in a given scene. For example, our model is able to enforce the condition that if a set of pixels take same object label, e.g. table, most likely those pixels would receive similar reflectance values. We cast the problem in an energy minimization framework and demonstrate the qualitative and quantitative improvement in the overall accuracy on the NYU and Pascal datasets.},
	issn = {10495258},
	author = {Vineet, Vibhav and Carsten Rother and Torr, Philip H.S.}
}
