2.6k citations · 4.6k across the 107 of their papers we have counts for
12 papers · 1 filter
Sharpen Focus: Learning with Attention Separability and Consistency
Lezi Wang, Ziyan Wu, Srikrishna Karanam +4
Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks. Despite good localizatio…
MRI Reconstruction via Cascaded Channel-wise Attention Network
Qiaoying Huang, Dong Yang, Pengxiang Wu +3
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can practically benefit patient due to reduced time of MRI scan, but it i…
CU-Net: Coupled U-Nets
Zhiqiang Tang, Xi Peng, Shijie Geng +2
We design a new connectivity pattern for the U-Net architecture. Given several stacked U-Nets, we couple each U-Net pair through the connections of their semantic blocks, resulting…
Quantized Densely Connected U-Nets for Efficient Landmark Localization
Zhiqiang Tang, Xi Peng, Shijie Geng +3
In this paper, we propose quantized densely connected U-Nets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally reused…
Learning to Forecast and Refine Residual Motion for Image-to-Video Generation
Long Zhao, Xi Peng, Yu Tian +2
We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object. Recent methods for such proble…
CR-GAN: Learning Complete Representations for Multi-view Generation
Yu Tian, Xi Peng, Long Zhao +2
Generating multi-view images from a single-view input is an essential yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that…
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
Xi Peng, Zhiqiang Tang, Fei Yang +2
Random data augmentation is a critical technique to avoid overfitting in training deep neural network models. However, data augmentation and network training are usually treated as…
Self-Attention Generative Adversarial Networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas +1
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Tradit…
Improving GANs Using Optimal Transport
Tim Salimans, Han Zhang, Alec Radford +1
We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data d…
Scenarios: A New Representation for Complex Scene Understanding
Zachary A. Daniels, Dimitris N. Metaxas
The ability for computational agents to reason about the high-level content of real world scene images is important for many applications. Existing attempts at addressing the probl…
Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution
Rahil Mehrizi, Xi Peng, Zhiqiang Tang +3
Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for Work-related Musculoskeletal Disorders. To impro…
RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment
Xi Peng, Rogerio S. Feris, Xiaoyu Wang +1
We propose a novel method for real-time face alignment in videos based on a recurrent encoder-decoder network model. Our proposed model predicts 2D facial point heat maps regulariz…