42 citations · 62 across the 8 of their papers we have counts for
8 papers
SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention
Rui Xu, Zhi Liu, Yong Luo +5
Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished…
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang +7
Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer visio…
Off-policy Imitation Learning from Visual Inputs
Zhihao Cheng, Li Shen, Dacheng Tao
Recently, various successful applications utilizing expert states in imitation learning (IL) have been witnessed. However, another IL setting -- IL from visual inputs (ILfVI), whic…
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution
Xinyue Wei, Haozhi Huang, Yujin Shi +3
The classic Monte Carlo path tracing can achieve high quality rendering at the cost of heavy computation. Recent works make use of deep neural networks to accelerate this process,…
Adaptive Compact Attention For Few-shot Video-to-video Translation
Risheng Huang, Li Shen, Xuan Wang +2
This paper proposes an adaptive compact attention model for few-shot video-to-video translation. Existing works in this domain only use features from pixel-wise attention without c…
Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning
Jie An, Tao Li, Haozhi Huang +6
Extracting effective deep features to represent content and style information is the key to universal style transfer. Most existing algorithms use VGG19 as the feature extractor, w…
Generalized Embedding Machines for Recommender Systems
Enneng Yang, Xin Xin, Li Shen +1
Factorization machine (FM) is an effective model for feature-based recommendation which utilizes inner product to capture second-order feature interactions. However, one of the maj…
Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning
Yingru Liu, Xuewen Yang, Dongliang Xie +4
Multi-task learning (MTL) is a common paradigm that seeks to improve the generalization performance of task learning by training related tasks simultaneously. However, it is still…