Xiaojun Chang@Monash

SELECTED PUBLICATIONS

Full paper list can be found at DBLP and Google Scholar.

PREPRINTS

Vision Language Navigation with Multi-granularity Observation and Auxiliary Reasoning Tasks
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  • we propose Multi-granularity Auxiliary Reason- ing Navigation (MG-AuxRN) to facilitate navigation learning. MG-AuxRN perceives multi-granularity input which combining dense object features and global image features.

Fengda Zhu, Yi Zhu, Yanxin Long, Xiaojun Chang, and Xiaodan Liang

Submitted to IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI), 2021

pdf | code

ZeroNAS:Differentiable Generative Adversarial Networks Search for Zero-Shot Learning
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  • Considering the varieties in datasets and tasks, we make the first attempt to bring NAS techniques into the realm of ZSL, and thus propose ZeroNAS to formulate the GAN architecture design for ZSL as a NAS problem.

Caixia Yan, Xiaojun Chang, Zhihui Li, Zongyuan Ge, Weili Guan, Lei Zhu and Qinghua Zheng

Submitted to IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI), 2021

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CONFERENCE PAPERS

Dynamic Slimmable Network
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  • propose a new dynamic network routing regime, achieving good hardware-efficiency by predictively adjusting filter numbers of networks at test time with respect to different inputs.

Changlin Li, Guangrun Wang, Bing Wang, Xiaodan Liang, Zhihui Li, and Xiaojun Chang

CVPR 2021

paper | ORAL | CODE

SOON:Scenario Oriented Object Navigation with Graph-based Exploration
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  • propose a task named Scenario Oriented Object Navigation (SOON), in which an agent is instructed to find an object in a house from an arbitrary starting position

Fengda Zhu, Xiwen Liang, Yi Zhu, Xiaojun Chang, Qizhi Yu, and Xiaodan Liang

CVPR 2021

paper

UPDeT:Universal Multi-agent RL via Policy Decoupling with Transformers
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  • propose a universal policy decoupling transformer model that extends MARL to a much broader scenario

Siyi Hu, Fengda Zhu, Xiaojun Chang and Xiaodan Liang

ICLR 2021

paper

iDARTS:Differentiable Architecture Search with Stochastic Implicit Gradients
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  • This paper deepens our understanding of the hypergradient calculation in the differentiable NAS.

Miao Zhang, Steven Su, Shirui Pan, Xiaojun Chang, Ehsan Abbasnejad and Reza Haffari

ICML 2021

paper

Mining Inter-Video Proposal Relations for Video Object Detection
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  • design a novel Inter-Video Proposal Relation method, which can effectively leverage inter-video proposal relation to learn discriminative representations for video object detection

Mingfei Han, Yali Wang, Xiaojun Chang and Yu Qiao

ECCV 2020

paper | code

Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting
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  • investigate how to utilize visual content for disambiguation and latent space alignment in unsupervised MMT

Po-Yao Huang, Junjie Hu, Xiaojun Chang and Alexander Hauptmann

ACL 2020

paper

Vision-language Navigation with Self-Supervised Auxiliary Reasoning Tasks
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  • Introducing Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to take advantage of the additional training signals derived from the semantic information

Fengda Zhu, Yi Zhu, Xiaojun Chang, Xiaodan Liang

CVPR 2020

paper | DEMO | CODE | Oral

ZSTAD:Zero-Shot Temporal Activity Detection
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  • Proposing a novel problem setting for temporal activity detection in which activities that are not seen during the training stage can be recognized and localized simultaneously

Lingling Zhang, Xiaojun Chang, Jun Liu, Sen Wang, Zongyuan Ge, Minnan Luo, Alexander Hauptmann

CVPR 2020

paper

Unity Style Transfer for Person Re-Identification
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  • smooth the style disparities within the same camera and across different cameras

Chong Liu, Xiaojun Chang, Yi-Dong Shen

CVPR 2020

paper

Neural Architecture Search by Block-wisely Distilling Architecture Knowledge
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  • modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained

Changlin Li, Jiefeng Peng, Liuchun Yuan, Guangrun Wang, Xiaodan Liang, Liang Lin, Xiaojun Chang

CVPR 2020

paper | code

Vision Dialogue Navigation by Exploring Cross-modal Memory
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  • learning an agent endowed with the capability of constant conversation for help with natural language and navigating according to human responses
  • propose a Cross-modal Memory Network (CMN) for remembering and understanding the rich information relevant to historical navigation actions

Yi Zhu, Fengda Zhu, Zhao huan Zhan, Bingqian Lin, Jianbin Jiao, Xiaojun Chang, Xiaodan Liang

CVPR 2020

paper | code

Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization
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  • formulate the supernet training in the One-Shot NAS as a constrained optimization problem of continual learning that the learning of current architecture should not degrade the performance of previous architectures during the supernet training

Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Steven Su

CVPR 2020

paper | code

Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
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  • enhance the intelligent exploration of differentiable Neural Architecture Search in the latent space

Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Zongyuan Ge and Steven Su

NeurIPS 2020

paper | code

Hierarchical Neural Architecture Search for Deep Stereo Matching
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  • leverage the volumetric stereo matching pipeline and allow the network to automat70 ically select the optimal structures for both the Feature Net and the Matching Net

Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, and Zongyuan Ge

NeurIPS 2020

paper | code

Multi-Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations
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  • leveraging visual object detection and propose a model with diverse multi-head attention to learn grounded multilingual multimodal representations

Po-Yao Huang, Xiaojun Chang, Alexander G. Hauptmann

EMNLP 2019

paper

Annotation Efficient Cross-Modal Retrieval with Adversarial Attentive Alignment
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  • propose a novel framework to leverage automatically extracted regional semantics from un-annotated images as additional weak supervision to learn visual-semantic embeddings

Po-Yao Huang, Guoliang Kang, Wenhe Liu, Xiaojun Chang, Alexander G. Hauptmann

ACM MM 2019

paper

RCAA:Relational Context-Aware Agents for Person Search
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  • made the earliest attempt to address the person search problem and built the first deep reinforcement learning based person search framework

Xiaojun Chang, Po-Yao Huang, Yi-Dong Shen, Xiaodan Liang, Yi Yang and Alexander G. Hauptmann

ECCV 2018

paper

Reinforcement Cutting-Agent Learning for Video Object Segmentation
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  • make a pioneer effort to formulate the video object segmentation problem as a Markov Decision Process and propose a novel reinforcement cutting-agent learning framework to tackle this problem

Junwei Han, Le Yang, Dingwen Zhang, Xiaojun Chang, Xiaodan Liang

CVPR 2018

paper

Complex Event Detection by Identifying Reliable Shots from Untrimmed Videos
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  • simultaneously learns a linear SVM classifier and infers a binary indicator for each instance in order to select reliable training instances from each positive or negative bag

Hehe Fan, Xiaojun Chang, De Cheng, Yi Yang, Dong Xu, Alexander G. Hauptmann

ICCV 2017

paper

They are Not Equally Reliable:Semantic Event Search Using Differentiated Concept Classifiers
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  • combine the concept classifiers based on a principled estimate of their accuracy on the unlabeled test videos

Xiaojun Chang, Yao-Liang Yu, Yi Yang, Eric P. Xing

CVPR 2016

paper

Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM
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  • define a novel notion of semantic saliency that assesses the relevance of each shot with the event of interest

Xiaojun Chang, Yi Yang, Eric P. Xing, Yao-Liang Yu

ICML 2015

paper | code

Searching Persuasively:Joint Event Detection and Evidence Recounting with Limited Supervision
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  • propose a joint framework that simultaneously detects high-level events and localizes the indicative concepts of the events

Xiaojun Chang, Yao-Liang Yu, Yi Yang, Alexander G. Hauptmann

ACM MM 2015

paper

 

JOURNAL PAPERS

A Comprehensive Survey of Neural Architecture Search:Challenges and Solutions
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  • we provide a new perspective, beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work

Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen and Xin Wang

Accepted by ACM Computing Surveys, 2021

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One-Shot Neural Architecture Search:Maximising Diversity to Overcome Catastrophic Forgetting
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  • To improve transferability, we further devised a variant of NSAS, called NSAS-C, which searches for deeper architectures in the convolutional cell search.

Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Chuan Zhou, Zongyuan Ge, and Steven Su

IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI), 2020

pdf | code

Semantic Pooling for Complex Event Analysis in Untrimmed Videos
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  • design the informed nearly-isotonic SVM classifier (NI-SVM) that is able to exploit the carefully constructed ordering information

Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing

IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI) 39(8) 1617-1632 (2017)

pdf | code

Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization
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  • propose a semi-supervised batch mode multi-class active learning algorithm for visual concept recognition

Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, Alexander G. Hauptmann

International Journal of Computer Vision (IJCV) 113(2) 113-127 (2015)

pdf

Semantics Preserving Graph Propagation for Zero-Shot Object Detection
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  • propose a semantics preserving graph propagation model based on GCN for the challenging ZSD task, which leverages both the semantic description and structural knowledge in prior category graphs to facilitate the semantic coherency of region graph.

Caixia Yan, Qinghua Zheng, Xiaojun Chang, Minnan Luo, ChungHsing Yeh and Alexander G. Hauptmann

IEEE Transactions on Image Processing 29:8163-8176 (2020)

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Self-supervised Deep Correlation Tracking
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  • formulate a multi-cycle consistency loss based selfsupervised learning manner to pre-training the deep feature extraction network, which can take advantage of extensive unlabeled video samples rather than limited manually annotated samples

Di Yuan, Xiaojun Chang, Po-Yao Huang, Qiao Liu, and Zhenyu He

IEEE Transactions on Image Processing 30:976-985 (2021)

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