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People drive on the road and eat in the kitchen. Can the road imply driving or the kitchen imply eating? This paper addresses such a problem by studying the relations between actions and scenes. To get effective scene representation, we use a deep convolutional neural networks (CNN) model trained from a scene-centric database to predict scene responses for videos. We employ two encoding schemes based...
Localizing heavily occluded human faces is a challenging problem in facial detection. Previous methods mainly employ sliding windows by determining whether windows include human faces. In this paper, we provide a novel segmentation-based perspective for heavily occluded face localization with deep convolutional neural networks (CNN). Our model takes an image as input without complicated pre-processing...
Efficiency and effectiveness are two key factors to evaluate a human segmentation algorithm for real vision applications. However, most existing algorithms only focus on one of them. That is, fast and accurate human segmentation is not yet well addressed. In this paper, we propose a super-fast and highly accurate human segmentation method with very deep convolutional neural networks. We also provide...
Smile detection from facial images is a specialized task in facial expression analysis with many potential applications such as smiling payment, patient monitoring and photo selection. The current methods on this study are to represent face with low-level features, followed by a strong classifier. However, these manual features cannot well discover information implied in facial images for smile detection...
This paper focuses on the problem of finding a few representatives for a given dataset, which have both representation and discrimination ability. To solve this problem, we propose a novel algorithm, called Structure Sparsity based Discriminative Representative Selection (SSDRS), to find a representative subset of data points. The selected representative subset keeps the representation ability based...
Inspired by information processing of complex cells in visual cortex, we present a simple system for fast and robust feature extraction. Our method includes an unsupervised algorithm for learning invariant descriptors from data, and an architecture for the task of digit recognition. The proposed algorithm is not only efficient that training can be accomplished in a few iterations, but can map test...
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