113 citations · 229 across the 13 of their papers we have counts for
14 papers
Decoupled Kullback-Leibler Divergence Loss
Jiequan Cui, Zhuotao Tian, Zhisheng Zhong +3
In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss…
Understanding Imbalanced Semantic Segmentation Through Neural Collapse
Zhisheng Zhong, Jiequan Cui, Yibo Yang +4
A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equi…
Generalized Parametric Contrastive Learning
Jiequan Cui, Zhisheng Zhong, Zhuotao Tian +3
In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on theoretical analysis, we obse…
Region Rebalance for Long-Tailed Semantic Segmentation
Jiequan Cui, Yuhui Yuan, Zhisheng Zhong +4
In this paper, we study the problem of class imbalance in semantic segmentation. We first investigate and identify the main challenges of addressing this issue through pixel rebala…
Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition
Zhisheng Zhong, Jiequan Cui, Zeming Li +3
Deep neural networks perform poorly on heavily class-imbalanced datasets. Given the promising performance of contrastive learning, we propose Rebalanced Siamese Contrastive Mining…
Parametric Contrastive Learning
Jiequan Cui, Zhisheng Zhong, Shu Liu +2
In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to b…
Improving Calibration for Long-Tailed Recognition
Zhisheng Zhong, Jiequan Cui, Shu Liu +1
Deep neural networks may perform poorly when training datasets are heavily class-imbalanced. Recently, two-stage methods decouple representation learning and classifier learning to…
ResLT: Residual Learning for Long-tailed Recognition
Jiequan Cui, Shu Liu, Zhuotao Tian +2
Deep learning algorithms face great challenges with long-tailed data distribution which, however, is quite a common case in real-world scenarios. Previous methods tackle the proble…
Channel-Level Variable Quantization Network for Deep Image Compression
Zhisheng Zhong, Hiroaki Akutsu, Kiyoharu Aizawa
Deep image compression systems mainly contain four components: encoder, quantizer, entropy model, and decoder. To optimize these four components, a joint rate-distortion framework…
Expectation-Maximization Attention Networks for Semantic Segmentation
Xia Li, Zhisheng Zhong, Jianlong Wu +3
Self-attention mechanism has been widely used for various tasks. It is designed to compute the representation of each position by a weighted sum of the features at all positions. T…
ADA-Tucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition
Zhisheng Zhong, Fangyin Wei, Zhouchen Lin +1
Despite the recent success of deep learning models in numerous applications, their widespread use on mobile devices is seriously impeded by storage and computational requirements.…
Differentiable Linearized ADMM
Xingyu Xie, Jianlong Wu, Zhisheng Zhong +2
Recently, a number of learning-based optimization methods that combine data-driven architectures with the classical optimization algorithms have been proposed and explored, showing…
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
Zhisheng Zhong, Tiancheng Shen, Yibo Yang +2
Convolutional neural networks (CNNs) have recently achieved great success in single-image super-resolution (SISR). However, these methods tend to produce over-smoothed outputs and…
Convolutional Neural Networks with Alternately Updated Clique
Yibo Yang, Zhisheng Zhong, Tiancheng Shen +1
Improving information flow in deep networks helps to ease the training difficulties and utilize parameters more efficiently. Here we propose a new convolutional neural network arch…