activity
20122025
most citedLearning with Augmented Features for Heterogeneous Domain Adaptation

223 citations · 302 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CV2025

Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization

Zhiwang Zhang, Dong Xu, Wanli Ouyang +1

In this work, we propose a division-and-summarization (DaS) framework for dense video captioning. After partitioning each untrimmed long video as multiple event proposals, where ea…

eess.SY2023★ 1 cited

Bid Optimization for Offsite Display Ad Campaigns on eCommerce

Hangjian Li, Dong Xu, Konstantin Shmakov +2

Online retailers often use third-party demand-side-platforms (DSPs) to conduct offsite advertising and reach shoppers across the Internet on behalf of their advertisers. The proces…

cs.PF2023

Rethinking Memory Profiling and Migration for Multi-Tiered Large Memory Systems

Jie Ren, Dong Xu, Ivy Peng +4

Multi-tiered large memory systems call for rethinking of memory profiling and migration because of the unique problems unseen in the traditional memory systems with smaller capacit…

cs.CV2022★ 28 cited

Slow Motion Matters: A Slow Motion Enhanced Network for Weakly Supervised Temporal Action Localization

Weiqi Sun, Rui Su, Qian Yu +1

Weakly supervised temporal action localization (WTAL) aims to localize actions in untrimmed videos with only weak supervision information (e.g. video-level labels). Most existing m…

cs.CV2022★ 2 cited

Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound

Feiyu Wang, Qin Wang, Wen Li +2

In this work, we revisit the semi-supervised learning (SSL) problem from a new perspective of explicitly reducing empirical distribution mismatch between labeled and unlabeled samp…

cs.GT2020★ 1 cited

A Hybrid Simulation-based Duopoly Game Framework for Analysis of Supply Chain and Marketing Activities

Dong Xu, Chao Meng, Qingpeng Zhang +2

A hybrid simulation-based framework involving system dynamics and agent-based simulation is proposed to address duopoly game considering multiple strategic decision variables and r…

cs.SE2020

Dynamic Scheduling and Workforce Assignment in Open Source Software Development

Hui Xi, Dong Xu, Young-Jun Son

A novel modeling framework is proposed for dynamic scheduling of projects and workforce assignment in open source software development (OSSD). The goal is to help project managers…

eess.IV2018★ 6 cited

DVC: An End-to-end Deep Video Compression Framework

Guo Lu, Wanli Ouyang, Dong Xu +3

Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information. In this paper, taking adv…

cs.CV2017★ 9 cited

Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates

Jun Liu, Amir Shahroudy, Dong Xu +2

Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model th…

cs.CV2016★ 26 cited

Learning Multi-level Deep Representations for Image Emotion Classification

Tianrong Rao, Min Xu, Dong Xu

In this paper, we propose a new deep network that learns multi-level deep representations for image emotion classification (MldrNet). Image emotion can be recognized through image…

stat.ML2016★ 5 cited

Dimensionality-Dependent Generalization Bounds for -Dimensional Coding Schemes

Tongliang Liu, Dacheng Tao, Dong Xu

The -dimensional coding schemes refer to a collection of methods that attempt to represent data using a set of representative -dimensional vectors, and include non-negative m…

cs.LG2015★ 1 cited

Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient

Mingkui Tan, Shijie Xiao, Junbin Gao +3

Nuclear-norm regularization plays a vital role in many learning tasks, such as low-rank matrix recovery (MR), and low-rank representation (LRR). Solving this problem directly can b…

cs.LG2012★ 223 cited

Learning with Augmented Features for Heterogeneous Domain Adaptation

Lixin Duan, Dong Xu, Ivor Tsang

We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features…