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We propose a novel node splitting method for regression trees and incorporate it into the random regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first finds clusters in the training data which at least locally minimize the empirical loss without...
In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on. The detection of fashion items can be an important first step of various e-commerce applications for...
We present a hierarchical method for human pose estimation from a single still image. In our approach, a dependency graph representing relationships between reference points such as body joints is constructed and the positions of these reference points are sequentially estimated by a successive application of multidimensional output regressions along the dependency paths, starting from the root node...
We present an algorithm for 3d pose estimation of articulated people in natural images. The poses are disassembled into a collection of local patches and a new pose is inferred by assembling the local patches. This concept allows inference of a wide variety of poses from a small number of training patches. The actual process is realized efficiently by a novel voting scheme where each local patch extracted...
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