@proceedings {1883,
	title = {Scene-Domain Active Part Models for Object Representation},
	year = {2015},
	month = {12/2015},
	pages = { 2497 - 2505 },
	address = {Santiago, Chile},
	abstract = {<p>In this paper, we are interested in enhancing the expressivity\&nbsp;and robustness of part-based models for object representation,\&nbsp;in the common scenario where the training data\&nbsp;are based on 2D images. To this end, we propose scene-domain\&nbsp;active part models (SDAPM), which reconstruct\&nbsp;and characterize the 3D geometric statistics between object{\textquoteright}s\&nbsp;parts in 3D scene-domain by using 2D training data\&nbsp;in the image-domain alone. And on top of this, we explicitly\&nbsp;model and handle occlusions in SDAPM. Together with the\&nbsp;developed learning and inference algorithms, such a model\&nbsp;provides rich object descriptions, including 2D object and\&nbsp;parts localization, 3D landmark shape and camera viewpoint,\&nbsp;which offers an effective representation to various image\&nbsp;understanding tasks, such as object and parts detection,\&nbsp;3D landmark shape and viewpoint estimation from images.\&nbsp;Experiments on the above tasks show that SDAPM outperforms\&nbsp;previous part-based models, and thus demonstrates\&nbsp;the potential of the proposed technique.</p>
},
	doi = {10.1109/ICCV.2015.287},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7410644\&tag=1},
	author = {Zhou Ren and Chaohui Wang and Alan Yuille}
}
