@proceedings {2680,
	title = {DOC: Deep OCclusion Recovering From A Single Image},
	year = {2016},
	abstract = {<p>Recovering the occlusion relationships between objects is a fundamental human visual ability which yields important information about the 3D world. In this paper we propose a deep network architecture, called DOC, which acts on a single image, detects object boundaries and estimates the border ownership (i.e. which side of the boundary is foreground and which is background). We represent occlusion relations by a binary edge map, to indicate the object boundary, and an occlusion orientation variable which is tangential to the boundary and whose direction specifies border ownership by a left-hand rule. We train two related deep convolutional neural networks, called DOC, which exploit local and non-local image cues to estimate this representation and hence recover occlusion relations. In order to train and test DOC we construct a large-scale instance occlusion boundary dataset using PASCAL VOC images, which we call the PASCAL instance occlusion dataset (PIOD). This contains 10,000 images and hence is two orders of magnitude larger than existing occlusion datasets for outdoor images. We test two variants of DOC on PIOD and on the BSDS occlusion dataset and show they outperform state-of-the-art methods. Finally, we perform numerous experiments investigating multiple settings of DOC and transfer between BSDS and PIOD, which provides more insights for further study of occlusion estimation.</p>
},
	author = {Peng Wang and Alan Yuille}
}
@conference {2553,
	title = {Zoom better to see clearer: Human and object parsing with hierarchical auto-zoom net},
	booktitle = {ECCV},
	year = {2016},
	month = {09/2016},
	address = {Amsterdam, The Netherlands},
	author = {Fangting Xia and Peng Wang and Liang-chieh Chen and Alan Yuille}
}
@conference {2582,
	title = {Zoom Better to See Clearer: Human Part Segmentation with Auto Zoom Net},
	booktitle = {ECCV},
	year = {2016},
	abstract = {<p>Parsing articulated objects, e.g . humans and animals, into semantic parts ( e.g . body, head and arms, etc .) from natural images is a challenging and fundamental problem for computer vision. A big dif- ficulty is the large variability of scale and location for objects and their corresponding parts. Even limited mistakes in estimating scale and loca- tion will degrade the parsing output and cause errors in boundary details. To tackle these difficulties, we propose a {\textquotedblleft}Hierarchical Auto-Zoom Net{\textquotedblright} (HAZN) for object part parsing which adapts to the local scales of ob- jects and parts. HAZN is a sequence of two {\textquotedblleft}Auto-Zoom Nets{\textquotedblright} (AZNs), each employing fully convolutional networks that perform two tasks: (1) predict the locations and scales of object instances (the first AZN) or their parts (the second AZN); (2) estimate the part scores for predicted object instance or part regions. Our model can adaptively {\textquotedblleft}zoom{\textquotedblright} (re- size) predicted image regions into their proper scales to refine the parsing. We conduct extensive experiments over the PASCAL part datasets on humans, horses, and cows. For humans, our approach significantly out- performs the state-of-the-arts by 5\% mIOU and is especially better at segmenting small instances and small parts. We obtain similar improve- ments for parsing cows and horses over alternative methods. In summary, our strategy of first zooming into objects and then zooming into parts is very effective. It also enables us to process different regions of the image at different scales adaptively so that, for example, we do not need to waste computational resources scaling the entire image.</p>
},
	author = {Fangting Xia and Peng Wang and Liang-chieh Chen and Alan Yuille}
}
