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20112023
most citedCentralized Feature Pyramid for Object Detection

343 citations · 2.4k across the 46 of their papers we have counts for

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Showing 2018Show all

9 papers · 1 filter

cs.IR2018★ 8 cited

Fast Matrix Factorization with Non-Uniform Weights on Missing Data

Xiangnan He, Jinhui Tang, Xiaoyu Du +3

Matrix factorization (MF) has been widely used to discover the low-rank structure and to predict the missing entries of data matrix. In many real-world learning systems, the data m…

cs.CV2018★ 19 cited

Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition

Xiangbo Shu, Jinhui Tang, Guo-Jun Qi +2

In this paper, we aim to address the problem of human interaction recognition in videos by exploring the long-term inter-related dynamics among multiple persons. Recently, Long Sho…

cs.IR2018★ 8 cited

Adversarial Training Towards Robust Multimedia Recommender System

Jinhui Tang, Xiaoyu Du, Xiangnan He +3

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to…

cs.IR2018★ 55 cited

Outer Product-based Neural Collaborative Filtering

Xiangnan He, Xiaoyu Du, Xiang Wang +3

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the…

cs.CV2018★ 32 cited

Physics-Based Generative Adversarial Models for Image Restoration and Beyond

Jinshan Pan, Jiangxin Dong, Yang Liu +5

We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, image deraining, etc.). These problems are highly ill-posed,…

cs.CV2018★ 25 cited

Learning Dual Convolutional Neural Networks for Low-Level Vision

Jinshan Pan, Sifei Liu, Deqing Sun +8

In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and deha…