420 citations · 1.2k across the 58 of their papers we have counts for
14 papers · 1 filter
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification
Rui Yang, Wenrui Dai, Chenglin Li +2
Message passing has evolved as an effective tool for designing Graph Neural Networks (GNNs). However, most existing methods for message passing simply sum or average all the neighb…
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
Haohang Xu, Xiaopeng Zhang, Hao Li +3
Self-supervised learning based on instance discrimination has shown remarkable progress. In particular, contrastive learning, which regards each image as well as its augmentations…
Batch Normalization with Enhanced Linear Transformation
Yuhui Xu, Lingxi Xie, Cihang Xie +5
Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data…
Center-wise Local Image Mixture For Contrastive Representation Learning
Hao Li, Xiaopeng Zhang, Hongkai Xiong
Contrastive learning based on instance discrimination trains model to discriminate different transformations of the anchor sample from other samples, which does not consider the se…
MimicNorm: Weight Mean and Last BN Layer Mimic the Dynamic of Batch Normalization
Wen Fei, Wenrui Dai, Chenglin Li +2
Substantial experiments have validated the success of Batch Normalization (BN) Layer in benefiting convergence and generalization. However, BN requires extra memory and float-point…
Key-Point Sequence Lossless Compression for Intelligent Video Analysis
Weiyao Lin, Xiaoyi He, Wenrui Dai +4
Feature coding has been recently considered to facilitate intelligent video analysis for urban computing. Instead of raw videos, extracted features in the front-end are encoded and…
K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations
Haohang Xu, Hongkai Xiong, Guo-Jun Qi
In this paper, we propose the -Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual inst…
Distilling Object Detectors with Task Adaptive Regularization
Ruoyu Sun, Fuhui Tang, Xiaopeng Zhang +2
Current state-of-the-art object detectors are at the expense of high computational costs and are hard to deploy to low-end devices. Knowledge distillation, which aims at training a…
Graph Pooling with Node Proximity for Hierarchical Representation Learning
Xing Gao, Wenrui Dai, Chenglin Li +2
Graph neural networks have attracted wide attentions to enable representation learning of graph data in recent works. In complement to graph convolution operators, graph pooling is…
Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
Weiyao Lin, Huabin Liu, Shizhan Liu +7
Along with the development of modern smart cities, human-centric video analysis has been encountering the challenge of analyzing diverse and complex events in real scenes. A comple…
TRP: Trained Rank Pruning for Efficient Deep Neural Networks
Yuhui Xu, Yuxi Li, Shuai Zhang +6
To enable DNNs on edge devices like mobile phones, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations. Several…
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
Hao Li, Xiaopeng Zhang, Hongkai Xiong +1
Collecting fine-grained labels usually requires expert-level domain knowledge and is prohibitive to scale up. In this paper, we propose Attribute Mix, a data augmentation strategy…
Latency-Aware Differentiable Neural Architecture Search
Yuhui Xu, Lingxi Xie, Xiaopeng Zhang +4
Differentiable neural architecture search methods became popular in recent years, mainly due to their low search costs and flexibility in designing the search space. However, these…
Spatial-Temporal Transformer Networks for Traffic Flow Forecasting
Mingxing Xu, Wenrui Dai, Chunmiao Liu +4
Traffic forecasting has emerged as a core component of intelligent transportation systems. However, timely accurate traffic forecasting, especially long-term forecasting, still rem…