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FCOS: Fully Convolutional One-Stage Object Detection
TLDR
We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Expand
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Detecting Text in Natural Image with Connectionist Text Proposal Network
TLDR
We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image. Expand
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Hierarchical Codebook Design for Beamforming Training in Millimeter-Wave Communication
TLDR
We devise an efficient hierarchical codebook by jointly exploiting sub-array and deactivation (turning-off) antenna processing techniques, where closed-form expressions are provided to generate the codebook. Expand
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Bag of Tricks for Image Classification with Convolutional Neural Networks
TLDR
We examine a collection of training procedure and model architecture refinements that improve model accuracy but barely change computational complexity. Expand
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Single Shot Text Detector with Regional Attention
TLDR
We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. Expand
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An End-to-End TextSpotter with Explicit Alignment and Attention
TLDR
We propose a novel text-alignment layer that allows it to precisely compute convolutional features of a text instance in arbitrary orientation, which is the key to boost the performance; (2) a character attention mechanism is introduced by using character spatial information as explicit supervision, leading to large improvements in recognition; (3) two technologies, together with a new RNN branch for word recognition, are integrated seamlessly into a single model. Expand
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Text-Attentional Convolutional Neural Network for Scene Text Detection
TLDR
In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network that particularly focuses on extracting text-related regions and features from the image components. Expand
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SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines
TLDR
We present a method called SimBoost that predicts continuous (non-binary) values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions. Expand
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Higgs Boson Discovery with Boosted Trees
TLDR
We propose to solve the Higgs boson classification problem with a gradient boosting approach. Expand
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Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network
TLDR
We introduce a novel Cascaded Convolutional Text Network that joints two customized convolutional networks for coarse-to-fine text localization. Expand
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