864 citations · 1.9k across the 57 of their papers we have counts for
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Improving Generalization of Transformer for Speech Recognition with Parallel Schedule Sampling and Relative Positional Embedding
Pan Zhou, Ruchao Fan, Wei Chen +1
Transformer has shown promising results in many sequence to sequence transformation tasks recently. It utilizes a number of feed-forward self-attention layers to replace the recurr…
Tell-the-difference: Fine-grained Visual Descriptor via a Discriminating Referee
Shuangjie Xu, Feng Xu, Yu Cheng +1
In this paper, we investigate a novel problem of telling the difference between image pairs in natural language. Compared to previous approaches for single image captioning, it is…
Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs
Zhikang Zou, Yu Cheng, Xiaoye Qu +3
Crowd counting is a challenging task due to the large variations in crowd distributions. Previous methods tend to tackle the whole image with a single fixed structure, which is una…
Enhanced 3D convolutional networks for crowd counting
Zhikang Zou, Huiliang Shao, Xiaoye Qu +2
Recently, convolutional neural networks (CNNs) are the leading defacto method for crowd counting. However, when dealing with video datasets, CNN-based methods still process each vi…
Exact Recovery of Tensor Robust Principal Component Analysis under Linear Transforms
Canyi Lu, Pan Zhou
This work studies the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum. Our model is moti…
A Stochastic Trust Region Method for Non-convex Minimization
Zebang Shen, Pan Zhou, Cong Fang +1
We target the problem of finding a local minimum in non-convex finite-sum minimization. Towards this goal, we first prove that the trust region method with inexact gradient and Hes…
Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training
Chengjie Li, Ruixuan Li, Haozhao Wang +4
Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models. As data are dist…