Conference Paper

Pose-Sensitive Embedding by Nonlinear NCA Regression

Conference: Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada.
Source: DBLP

ABSTRACT This paper tackles the complex problem of visually matching people in similar pose but with different clothes, background, and other appearance changes. We achieve this with a novel method for learning a nonlinear embedding based on several extensions to the Neighborhood Component Analysis (NCA) framework. Our method is convolutional, enabling it to scale to realistically-sized images. By cheaply labeling the head and hands in large video databases through Amazon Mechanical Turk (a crowd-sourcing service), we can use the task of localizing the head and hands as a proxy for determining body pose. We apply our method to challenging real-world data and show that it can generalize beyond hand localization to infer a more general notion of body pose. We evaluate our method quantitatively against other embedding methods. We also demonstrate that realworld performance can be improved through the use of synthetic data. 1

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    • "Osadchy et al. applied a convolutional network to simultaneously detect and estimate the pitch, yaw and roll of a face [31]. Taylor et al. [42] trained a convolutional neural network to learn an embedding in which images of people in similar pose lie nearby. They used a subset of body parts, namely, the head and hand locations to learn the " gist " of a pose, and resorted to nearest-neighbour matching rather than explicitly modeling pose. "
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    ABSTRACT: This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level features and higher-level weak spatial models. Unconstrained human pose estimation is one of the hardest problems in computer vision, and our new architecture and learning schema shows significant improvement over the current state-of-the-art results. The main contribution of this paper is showing, for the first time, that a specific variation of deep learning is able to outperform all existing traditional architectures on this task. The paper also discusses several lessons learned while researching alternatives, most notably, that it is possible to learn strong low-level feature detectors on features that might even just cover a few pixels in the image. Higher-level spatial models improve somewhat the overall result, but to a much lesser extent then expected. Many researchers previously argued that the kinematic structure and top-down information is crucial for this domain, but with our purely bottom up, and weak spatial model, we could improve other more complicated architectures that currently produce the best results. This mirrors what many other researchers, like those in the speech recognition, object recognition, and other domains have experienced.
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    • "At test time, the distance to the manifold indicate whether the image contains a face, and the position of the closest point on the manifold indicates pose. Taylor et al. [27] [28] use a ConvNet to estimate the location of body parts (hands, head, etc) so as to derive the human body pose. They use a metric learning criterion to train the network to produce points on a body pose manifold. "
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    ABSTRACT: We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learnt simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013), and produced near state of the art results for the detection and classifications tasks. Finally, we release a feature extractor from our best model called OverFeat.
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    • "Supervised learning of end-to-end systems on images have been shown to work well when there is abundant labeled samples [23], including for detection tasks [37] [29] [15] [31] [14] [36]. However, for many input domains, it is hard to find adequate number of labeled data. "
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    ABSTRACT: Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-the-art and competitive results on all major pedestrian datasets with a convolutional network model. The model uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information, and an unsupervised method based on convolutional sparse coding to pre-train the filters at each stage.
    Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 12/2012; DOI:10.1109/CVPR.2013.465
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