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Learned Video Compression
We present a new algorithm for video coding, learned end-to-end for the ...
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Real-Time Adaptive Image Compression
We present a machine learning-based approach to lossy image compression ...
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Metric Learning with Adaptive Density Discrimination
Distance metric learning (DML) approaches learn a transformation to a re...
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ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks
This paper aims to classify and locate objects accurately and efficientl...
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Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues
We explore the task of recognizing peoples' identities in photo albums i...
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Compressing Deep Convolutional Networks using Vector Quantization
Deep convolutional neural networks (CNN) has become the most promising m...
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Learning Spatiotemporal Features with 3D Convolutional Networks
We propose a simple, yet effective approach for spatiotemporal feature l...
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Deep Poselets for Human Detection
We address the problem of detecting people in natural scenes using a par...
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Training Convolutional Networks with Noisy Labels
The availability of large labeled datasets has allowed Convolutional Net...
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Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art...
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PANDA: Pose Aligned Networks for Deep Attribute Modeling
We propose a method for inferring human attributes (such as gender, hair...
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