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20152022
most citedAdaptive Low-Rank Kernel Subspace Clustering

24 citations · 106 across the 26 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CV2021★ 2 cited

MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments

Pan Ji, Runze Li, Bir Bhanu +1

Self-supervised depth estimation for indoor environments is more challenging than its outdoor counterpart in at least the following two aspects: (i) the depth range of indoor seque…

cs.CV2021

Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network

Yang Liu, Saeed Anwar, Zhenyue Qin +3

The prevalent convolutional neural network (CNN) based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. Howe…

cs.LG2021★ 2 cited

Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions

Zhenyue Qin, Yiqun Zhang Saeed Anwar, Dongwoo Kim +3

Most existing graph neural networks (GNNs) learn node embeddings using the framework of message passing and aggregation. Such GNNs are incapable of learning relative positions betw…

cs.CV2021★ 6 cited

Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition

Zhenyue Qin, Yang Liu, Pan Ji +5

Skeleton sequences are lightweight and compact, and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract featu…

eess.IV2021★ 11 cited

Invertible Denoising Network: A Light Solution for Real Noise Removal

Yang Liu, Zhenyue Qin, Saeed Anwar +4

Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying inverti…

cs.CV2021★ 1 cited

Learning Transferable Kinematic Dictionary for 3D Human Pose and Shape Reconstruction

Ze Ma, Yifan Yao, Pan Ji +1

Estimating 3D human pose and shape from a single image is highly under-constrained. To address this ambiguity, we propose a novel prior, namely kinematic dictionary, which explicit…