activity
20172022
most citedDual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration

17 citations · 44 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV20222 cited

Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation

Shentong Mo, Zhun Sun, Chao Li

Contrastive self-supervised learning (CSL) with a prototypical regularization has been introduced in learning meaningful representations for downstream tasks that require strong se…

cs.LG20212 cited

On the Memory Mechanism of Tensor-Power Recurrent Models

Hejia Qiu, Chao Li, Ying Weng +3

Tensor-power (TP) recurrent model is a family of non-linear dynamical systems, of which the recurrence relation consists of a p-fold (a.k.a., degree-p) tensor product. Despite such…

cs.CV20192 cited

Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment

Mingzhen Shao, Zhun Sun, Mete Ozay +1

We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in rece…

cs.CV201917 cited

Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration

Xing Liu, Masanori Suganuma, Zhun Sun +1

In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exp…

cs.LG2018

Low-Rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling

Chao Li, Zhun Sun, Jinshi Yu +2

Although the convolutional neural networks (CNNs) have become popular for various image processing and computer vision task recently, it remains a challenging problem to reduce the…

cs.LG2018

Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling

Chao Li, Mohammad Emtiyaz Khan, Zhun Sun +4

Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis. The numerical defects of TD methods, however…