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Real-world visual classification tasks typically need to deal with data observed from different domains. Inspired by canonical correlation analysis (CCA), we propose an enhanced CCA with local density for associating and recognizing cross-domain data. In addition to maximizing the correlation of the projected cross-domain data, our CCA model further exploits the local density information observed...
Estimating the 3D shape information of a face from a single image is a challenging task, especially when the input image is captured under unconstrained scenarios (e.g., variations of pose, illumination, expression, or even disguise). Previous approaches to this problem typically require careful initialization, registration, or segmentation of the face image regions. With the objective to match the...
For cross-view action recognition and many real-world visual classification problems, one needs to recognize test data at a particular target domain of interest, while training data are collected at a different source domain. Without eliminating such domain differences, recognition of test data using classifiers trained in the source domain will not be expected to produce satisfactory performance...
We propose a learning-based algorithm for body shape estimation, which only requires 2D clothing images taken in multiple views as the input data. Compared with the use of 3D scanners or depth cameras, although our setting is more user friendly, it also makes the learning and estimation problems more challenging. In addition to utilizing ground truth body images for constructing human body models...
This paper presents a saliency-based video object extraction (VOE) framework. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object). To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion...
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