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Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated their advantage over linear ones due to their powerful generalization capability. In the literature, kernel functions are typically used to achieve non-linearity in(More)
We consider the problem of depth estimation from a single molecular image in this work. It is a challenging task as no reliable depth cues are available, e.g., stereo correspondences , motions etc. Previous efforts have been focusing on exploiting geometric priors or additional sources of information , with all using hand-crafted features. Recently, there(More)
Fast nearest neighbor searching is becoming an increasingly important tool in solving many large-scale problems. Recently a number of approaches to learning data-dependent hash functions have been developed. In this work, we propose a column generation based method for learning data-dependent hash functions on the basis of proximity comparison information.(More)
Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neu-ral networks (CNNs) for the task. We show how to improve semantic segmentation through the use of contextual information. Specifically, we explore 'patch-patch' context and 'patch-background' context with deep CNNs. For learning the patch-patch(More)
To study the biologic role of migration inhibitory factor (MIF), a pleiotropic cytokine, we generated a mouse strain lacking MIF by gene targeting in embryonic stem cells. Analysis of the role of MIF during sepsis showed that MIF-/- mice were resistant to the lethal effects of high dose bacterial lipopolysaccharide (LPS), or Staphylococcus aureus(More)
Most existing approaches to hashing apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of the method to respond to the data, and can result in complex optimization problems that are difficult to solve. Here we propose a flexible yet simple(More)
In this article, we tackle the problem of depth estimation from single monocular images. Compared with depth estimation using multiple images such as stereo depth perception, depth from monocular images is much more challenging. Prior work typically focuses on exploiting geometric priors or additional sources of information, most using hand-crafted(More)
Previous studies indicate resveratrol pretreatment can protect cardiomyocytes. However, it is largely unknown whether resveratrol protects cardiomyocytes when applied at reperfusion. The purpose of this study was to investigate whether resveratrol given at reoxygenation could protect cardiomyocytes under the anoxia/reoxygenation (A/R) condition and to(More)
To build large-scale query-by-example image retrieval systems, embedding image features into a binary Hamming space provides great benefits. Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the binary Hamming space. Most existing approaches apply a single form of hash function,(More)
Conditional Random Rields (CRF) have been widely applied in image segmentations. While most studies rely on hand-crafted features, we here propose to exploit a pre-trained large convolutional neural network (CNN) to generate deep features for CRF learning. The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for(More)