activity
20172021
most citedSolving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method

102 citations · 201 across the 18 of their papers we have counts for

collaborators

25 papers

cs.CV2021

Structure First Detail Next: Image Inpainting with Pyramid Generator

Shuyi Qu, Zhenxing Niu, Kaizhu Huang +4

Recent deep generative models have achieved promising performance in image inpainting. However, it is still very challenging for a neural network to generate realistic image detail…

cs.LG20213 cited

Accelerating Gossip SGD with Periodic Global Averaging

Yiming Chen, Kun Yuan, Yingya Zhang +3

Communication overhead hinders the scalability of large-scale distributed training. Gossip SGD, where each node averages only with its neighbors, is more communication-efficient th…

cs.LG20212 cited

DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training

Kun Yuan, Yiming Chen, Xinmeng Huang +4

The scale of deep learning nowadays calls for efficient distributed training algorithms. Decentralized momentum SGD (DmSGD), in which each node averages only with its neighbors, is…

cs.CV20215 cited

Learning Position and Target Consistency for Memory-based Video Object Segmentation

Li Hu, Peng Zhang, Bang Zhang +3

This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentat…

cs.CV20213 cited

Multiple Object Tracking with Correlation Learning

Qiang Wang, Yun Zheng, Pan Pan +1

Recent works have shown that convolutional networks have substantially improved the performance of multiple object tracking by simultaneously learning detection and appearance feat…

cs.CV202119 cited

Few-Shot Incremental Learning with Continually Evolved Classifiers

Chi Zhang, Nan Song, Guosheng Lin +3

Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points, without forgetting knowledge…