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This paper addresses the problem of transferring CNNs pre-trained for face recognition to a face attribute prediction task. To transfer an off-the-shelf CNN to a novel task, a typical solution is to fine-tune the network towards the novel task. As demonstrated in the state-of-the-art face attribute prediction approach, fine-tuning the high-level CNN hidden layer by using labeled attribute data leads...
Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world. The key to this problem is to build proper feature representations to cope with these unfavourable conditions. Given the success of Convolutional Neural Network (CNN) in image classification, the high-level CNN feature, as an intuitive and reasonable choice, has been widely...
Single-channel 112-Gb/s PAM-4 transmission based on low-cost intensity modulation and direct detection (IM/DD) optics is experimentally demonstrated over 1-km standard single-mode fiber. By employing a digital precompensation, duobinary encode/decoding with PAM-4 signal and 7-level training-sequence-aided least mean square (TS-LMS) algorithm, we successfully achieve a receiver sensitivity of about...
Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks — face localization, facial descriptor construction, and attribute classification — in a pipeline. As a typical classification problem, face attribute prediction has been addressed...
To understand text contents better, many research efforts have been made exploring detection and classification of the semantic relation between a concept pair. As described herein, we present our study of a semantic relation classification task as a graph-based multi-view learning task: each intra-view graph is constructed with instances in the view; a node's label ldquoscorerdquo is propagated on...
Traditional negative selection algorithms do not perform any differentiation for training self dataset and only use the mechanism of negative selection. They will generate excessive invalid detectors and have poor detection performance when the training selves contain noisy data. In this paper, an outlier robust algorithm is proposed. The new algorithm will divide the training selves into internal...
This paper proposes a pain expression recognition method using boosted Gabor features. At first, each neonatal facial image which is normalized to the size of 112times92 pixels is convoluted with the 2D Gabor filters to extract 412160 Gabor features. Since the high-dimensional Gabor feature vectors are quite redundant, we propose a modified version of AdaBoost algorithm, called the HybridBoost, to...
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