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Recent advances in image-based object recognition have exploited object proposals to speed up the detection process by reducing the search space. In this paper, we present a novel idea that utilizes true objectness and semantic image filtering (retrieved within the convolutional layers of a Convolutional Neural Network) to propose effective region proposals. Information learned in fully convolutional...
With the success of deep learning in the last few years, the object detection community shifted from processing on exhaustive sliding windows to smaller set of object proposals using more powerful and deep visual representations. Object proposals increase the accuracy and speed up detection process by reducing the search space. In this paper we propose a novel idea of filtering irrelevant edges using...
There has been a surge of efforts in cross-modal recognition and retrieval in recent multimedia research. Towards this goal, we investigate a multi-modal subspace learning algorithm together with the Dropout regularizer. Inspired by the regularization for neural networks, we propose to aritificially remove the effect of certain amount of feature bins using the probabilistic approach to prevent the...
In this study we address the problem of face segmentation in thumbnail images. While there have been several approaches for face detection, none performs detection in such low resolution and segmentation with pixel accuracy. In this paper, we propose convolutional segmentation networks (CSNs) that can be trained to learn segmentation of human faces. Unlike the deep classifiers such as Convolutional...
True authentication predicted on biometrics has received upsurge attention during the last few years, as it provides facile way to access the system through basic physical and behavioral characteristics. Face recognition being a non-intrusive recognition requires less participation from the user compared to iris, speech and fingerprint based biometric. Resistance to false authentication from photographs...
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