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Human Motion Transfer from Poses in the Wild
In this paper, we tackle the problem of human motion transfer, where we ...
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AtomNAS: Fine-Grained End-to-End Neural Architecture Search
Designing of search space is a critical problem for neural architecture ...
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Deep Model Compression via Filter Auto-sampling
The recent WSNet [1] is a new model compression method through sampling ...
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EnlightenGAN: Deep Light Enhancement without Paired Supervision
Deep learning-based methods have achieved remarkable success in image re...
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Slimmable Neural Networks
We present a simple and general method to train a single neural network ...
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YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
Learning long-term spatial-temporal features are critical for many video...
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YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
Learning long-term spatial-temporal features are critical for many video...
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Wide Activation for Efficient and Accurate Image Super-Resolution
In this report we demonstrate that with same parameters and computationa...
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EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching
Designing the structure of neural networks is considered one of the most...
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Factorized Adversarial Networks for Unsupervised Domain Adaptation
In this paper, we propose Factorized Adversarial Networks (FAN) to solve...
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Efficient Video Object Segmentation via Network Modulation
Video object segmentation targets at segmenting a specific object throug...
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Action Recognition with Visual Attention on Skeleton Images
Action recognition with 3D skeleton sequences is becoming popular due to...
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Learning 3D-FilterMap for Deep Convolutional Neural Networks
We present a novel and compact architecture for deep Convolutional Neura...
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WSNet: Compact and Efficient Networks with Weight Sampling
We present a new approach and a novel architecture, termed WSNet, for le...
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Learning to Segment Human by Watching YouTube
An intuition on human segmentation is that when a human is moving in a v...
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Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach
Emotion recognition from facial expressions is tremendously useful, espe...
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GUN: Gradual Upsampling Network for single image super-resolution
In this paper, we propose an efficient super-resolution (SR) method base...
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Local Patch Classification Based Framework for Single Image Super-Resolution
Recent learning-based super-resolution (SR) methods often focus on the d...
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Learning from Noisy Labels with Distillation
The ability of learning from noisy labels is very useful in many visual ...
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Dense Captioning with Joint Inference and Visual Context
Dense captioning is a newly emerging computer vision topic for understan...
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Proposal-free Network for Instance-level Object Segmentation
Instance-level object segmentation is an important yet under-explored ta...
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Deep Networks for Image Super-Resolution with Sparse Prior
Deep learning techniques have been successfully applied in many areas of...
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DeepFont: Identify Your Font from An Image
As font is one of the core design concepts, automatic font identificatio...
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Self-Tuned Deep Super Resolution
Deep learning has been successfully applied to image super resolution (S...
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Matching-CNN Meets KNN: Quasi-Parametric Human Parsing
Both parametric and non-parametric approaches have demonstrated encourag...
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Real-World Font Recognition Using Deep Network and Domain Adaptation
We address a challenging fine-grain classification problem: recognizing ...
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Designing A Composite Dictionary Adaptively From Joint Examples
We study the complementary behaviors of external and internal examples i...
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Deep Human Parsing with Active Template Regression
In this work, the human parsing task, namely decomposing a human image i...
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Learning Super-Resolution Jointly from External and Internal Examples
Single image super-resolution (SR) aims to estimate a high-resolution (H...
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Collaborative Feature Learning from Social Media
Image feature representation plays an essential role in image recognitio...
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Decomposition-Based Domain Adaptation for Real-World Font Recognition
We present a domain adaption framework to address a domain mismatch betw...
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Scalable Similarity Learning using Large Margin Neighborhood Embedding
Classifying large-scale image data into object categories is an importan...
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GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training
The ability to train large-scale neural networks has resulted in state-o...
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