• Publications
  • Influence
Generative Image Inpainting with Contextual Attention
TLDR
This work proposes a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions.
Free-Form Image Inpainting With Gated Convolution
TLDR
The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers.
MAttNet: Modular Attention Network for Referring Expression Comprehension
TLDR
This work proposes to decompose expressions into three modular components related to subject appearance, location, and relationship to other objects, which allows for flexibly adapt to expressions containing different types of information in an end-to-end framework.
A convolutional neural network cascade for face detection
TLDR
This work proposes a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance, and introduces a CNN-based calibration stage after each of the detection stages in the cascade.
A unified approach to salient object detection via low rank matrix recovery
  • Xiaohui Shen, Ying Wu
  • Computer Science
    IEEE Conference on Computer Vision and Pattern…
  • 16 June 2012
TLDR
A unified model to incorporate traditional low-level features with higher-level guidance to detect salient objects and can be considered as a prototype framework not only for general salient object detection, but also for potential task-dependent saliency applications.
Minimum Barrier Salient Object Detection at 80 FPS
TLDR
A technique based on color whitening is proposed to extend the salient object detection method to leverage the appearance-based backgroundness cue, which further improves the performance, while still being one order of magnitude faster than all the other leading methods.
Top-Down Neural Attention by Excitation Backprop
TLDR
A new backpropagation scheme, called Excitation Backprop, is proposed to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process, and the concept of contrastive attention is introduced to make the top- down attention maps more discriminative.
EnlightenGAN: Deep Light Enhancement Without Paired Supervision
TLDR
This paper proposes a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images.
Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing
TLDR
A new benchmark Look into Person (LIP) is introduced that makes a significant advance in terms of scalability, diversity and difficulty, and a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision.
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
TLDR
This work proposes to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function.
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