ORCID Profile
0000-0001-8319-0118
Current Organisation
Australian National University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Pattern Recognition and Data Mining | Communication Technology and Digital Media Studies | Computer Software | Artificial Intelligence and Image Processing | Recommender systems | Autonomous agents and multiagent systems | Software Engineering | Human-centred computing | Computer-Human Interaction | Fairness accountability transparency trust and ethics of computer systems
ANZSRC Socio-Economic Objective (SEO)
Electronic Information Storage and Retrieval Services | Application Software Packages (excl. Computer Games) | Expanding Knowledge in the Information and Computing Sciences | Expanding Knowledge in Language, Communication and Culture |
Related Links
Publications
Viewpoint GoTO rankings considered helpful
Publisher: Association for Computing Machinery (ACM)
Date: 24-06-2019
DOI: 10.1145/3332803
Abstract: Seeking to improve rankings by utilizing more objective data and meaningful metrics.
Multi-concept learning with large-scale multimedia lexicons
Publisher: IEEE
Date: 2008
Detecting generic visual events with temporal cues
Publisher: IEEE
Date: 2006
Bridging Models for Popularity Prediction on Social Media
Publisher: ACM
Date: 30-01-2019
Multimedia semantics: Interactions between content and community
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2012
Special section on communities and media computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2009
AttentionFlow: Visualising Influence in Networks of Time Series
Publisher: ACM
Date: 08-03-2021
Pattern mining in visual concept streams
Publisher: IEEE
Date: 07-2006
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Publisher: ACM
Date: 29-09-2007
Visual Memes in Social Media: Tracking Real-world News in Youtube Videos
Publisher: ACM
Date: 28-11-2011
Evolution of Privacy Loss on Wikipedia
Publisher: ACM
Date: 08-02-2016
Visual event detection using multi-dimensional concept dynamics
Publisher: IEEE
Date: 07-2006
ASNets: Deep Learning for Generalised Planning
Publisher: AI Access Foundation
Date: 04-05-2020
DOI: 10.1613/JAIR.1.11633
Abstract: In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural network architecture that exploits the relational structure of (P)PDDL planning problems to learn a common set of weights that can be applied to any problem in a domain. By mimicking the actions chosen by a traditional, non-learning planner on a handful of small problems in a domain, ASNets are able to learn a generalised reactive policy that can quickly solve much larger instances from the domain. This work extends the ASNet architecture to make it more expressive, while still remaining invariant to a range of symmetries that exist in PPDDL problems. We also present a thorough experimental evaluation of ASNets, including a comparison with heuristic search planners on seven probabilistic and deterministic domains, an extended evaluation on over 18,000 Blocksworld instances, and an ablation study. Finally, we show that sparsity-inducing regularisation can produce ASNets that are compact enough for humans to understand, yielding insights into how the structure of ASNets allows them to generalise across a domain.
How do ideas flow around SIGMM conferences?
Publisher: Association for Computing Machinery (ACM)
Date: 09-01-2018
Abstract: The ACM Multimedia conference just celebrated its quarter century in October 2017. This is a great opportunity to reflect on the intellectual influence of the conference, and the SIGMM community in general. The progress on big scholarly data allows us to make this task analytical. I download a data dump from Microsoft Academic Graph (MAG) in February 2016. I find all papers from ACM Multimedia (MM), the SIGMM flagship conference - there are 4,346 publication entries from 1993 to 2015. I then search the entire MAG for: (1) any paper that appears in the reference list of these MM papers - 35,829 entries across 1,560 publication venues (including both journals and conferences), with an average of 8.24 per paper (2) any paper that cites any of these MM papers - 46826 citations from 1694 publication venues, with an average of 10.77 citations per paper. This data allows us to profile the incoming (references) and outgoing (citations) influence in the community in detail.
Comparative Document Summarisation via Classification
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 17-07-2018
DOI: 10.1609/AAAI.V33I01.330120
Abstract: Thispaperconsidersextractivesummarisationinacomparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13months.Weobserve thatgradient-based optimisationoutperforms discrete and baseline approaches in 15 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a erse range of use cases such as comparing content sources, authors, related topics, or distinct view points.
Media lifecycle and content analysis in social media communities
Publisher: IEEE
Date: 07-2012
A longitudinal study of topic classification on Twitter
Publisher: PeerJ
Date: 07-06-2022
DOI: 10.7717/PEERJ-CS.991
Abstract: Twitter represents a massively distributed information source over topics ranging from social and political events to entertainment and sports news. While recent work has suggested this content can be narrowed down to the personalized interests of in idual users by training topic filters using standard classifiers, there remain many open questions about the efficacy of such classification-based filtering approaches. For ex le, over a year or more after training, how well do such classifiers generalize to future novel topical content, and are such results stable across a range of topics? In addition, how robust is a topic classifier over the time horizon, e.g ., can a model trained in 1 year be used for making predictions in the subsequent year? Furthermore, what features, feature classes, and feature attributes are most critical for long-term classifier performance? To answer these questions, we collected a corpus of over 800 million English Tweets via the Twitter streaming API during 2013 and 2014 and learned topic classifiers for 10 erse themes ranging from social issues to celebrity deaths to the “Iran nuclear deal”. The results of this long-term study of topic classifier performance provide a number of important insights, among them that: (i) such classifiers can indeed generalize to novel topical content with high precision over a year or more after training though performance degrades with time, (ii) the classes of hashtags and simple terms contain the most informative feature instances, (iii) removing tweets containing training hashtags from the validation set allows better generalization, and (iv) the simple volume of tweets by a user correlates more with their informativeness than their follower or friend count. In summary, this work provides a long-term study of topic classifiers on Twitter that further justifies classification-based topical filtering approaches while providing detailed insight into the feature properties most critical for topic classifier performance.
Optimal-scaling-factor assignment for patch-wise image retargeting
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
DOI: 10.1109/MCG.2012.123
Pre-treatment intravoxel incoherent motion diffusion-weighted imaging predicts treatment outcome in nasopharyngeal carcinoma
Publisher: Elsevier BV
Date: 08-2020
Transform and Tell: Entity-Aware News Image Captioning
Publisher: IEEE
Date: 06-2020
Context-aware detection of sneaky vandalism on wikipedia across multiple languages
Publisher: Springer International Publishing
Date: 2015
Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches
Publisher: ACM
Date: 24-10-2016
IBM Multimedia Analysis and Retrieval System
Publisher: ACM
Date: 07-07-2008
Test-retest repeatability of T1rho (T1ρ) MR imaging in the head and neck
Publisher: Elsevier BV
Date: 02-2021
Influence Flowers of Academic Entities
Publisher: IEEE
Date: 10-2019
Structure analysis of soccer video with domain knowledge and hidden Markov models
Publisher: Elsevier BV
Date: 05-2004
A Sketch-Based Approach for Interactive Organization of Video Clips
Publisher: Association for Computing Machinery (ACM)
Date: 08-2014
DOI: 10.1145/2645643
Abstract: With the rapid growth of video resources, techniques for efficient organization of video clips are becoming appealing in the multimedia domain. In this article, a sketch-based approach is proposed to intuitively organize video clips by: (1) enhancing their narrations using sketch annotations and (2) structurizing the organization process by gesture-based free-form sketching on touch devices. There are two main contributions of this work. The first is a sketch graph, a novel representation for the narrative structure of video clips to facilitate content organization. The second is a method to perform context-aware sketch recommendation scalable to large video collections, enabling common users to easily organize sketch annotations. A prototype system integrating the proposed approach was evaluated on the basis of five different aspects concerning its performance and usability. Two sketch searching experiments showed that the proposed context-aware sketch recommendation outperforms, in terms of accuracy and scalability, two state-of-the-art sketch searching methods. Moreover, a user study showed that the sketch graph is consistently preferred over traditional representations such as keywords and keyframes. The second user study showed that the proposed approach is applicable in those scenarios where the video annotator and organizer were the same person. The third user study showed that, for video content organization, using sketch graph users took on average 1/3 less time than using a mass-market tool Movie Maker and took on average 1/4 less time than using a state-of-the-art sketch alternative. These results demonstrated that the proposed sketch graph approach is a promising video organization tool.
Stability and Efficiency of Personalised Cultural Markets
Publisher: ACM
Date: 30-04-2023
Exploring the dynamics of visual events in the multi-dimensional semantic concept space
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11788034_54
SCENT: Scalable Compressed Monitoring of Evolving Multirelational Social Networks
Publisher: Association for Computing Machinery (ACM)
Date: 10-2011
Abstract: We propose SCENT, an innovative, scalable spectral analysis framework for internet scale monitoring of multirelational social media data, encoded in the form of tensor streams. In particular, a significant challenge is to detect key changes in the social media data, which could reflect important events in the real world, sufficiently quickly. Social media data have three challenging characteristics. First, data sizes are enormous recent technological advances allow hundreds of millions of users to create and share content within online social networks. Second, social data are often multifaceted (i.e., have many dimensions of potential interest, from the textual content to user metadata). Finally, the data is dynamic structural changes can occur at multiple time scales and be localized to a subset of users. Consequently, a framework for extracting useful information from social media data needs to scale with data volume, and also with the number and ersity of the facets of the data. In SCENT, we focus on the computational cost of structural change detection in tensor streams. We extend compressed sensing (CS) to tensor data. We show that, through the use of randomized tensor ensembles, SCENT is able to encode the observed tensor streams in the form of compact descriptors. We show that the descriptors allow very fast detection of significant spectral changes in the tensor stream, which also reduce data collection, storage, and processing costs. Experiments over synthetic and real data show that SCENT is faster (17.7x--159x for change detection) and more accurate (above 0.9 F-score) than baseline methods.
SemStyle: Learning to Generate Stylised Image Captions Using Unaligned Text
Publisher: IEEE
Date: 06-2018
Learning Points and Routes to Recommend Trajectories
Publisher: ACM
Date: 24-10-2016
Learning knowledge bases for text and multimedia
Publisher: ACM
Date: 03-11-2014
Learning to distribute vocabulary indexing for scalable visual search
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
Hawkes Processes for Events in Social Media
Publisher: Association for Computing Machinery and Morgan & Claypool
Date: 19-12-2018
Detect irregularly shaped spatio-temporal clusters for decision support
Publisher: IEEE
Date: 07-2011
Rethinking Summarization and Storytelling for Modern Social Multimedia
Publisher: Springer International Publishing
Date: 2018
Efficient non-parametric Bayesian hawkes processes
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: In this paper, we develop an efficient non-parametric Bayesian estimation of the kernel function of Hawkes processes. The non-parametric Bayesian approach is important because it provides flexible Hawkes kernels and quantifies their uncertainty. Our method is based on the cluster representation of Hawkes processes. Utilizing the stationarity of the Hawkes process, we efficiently s le random branching structures and thus, we split the Hawkes process into clusters of Poisson processes. We derive two algorithms --- a block Gibbs s ler and a maximum a posteriori estimator based on expectation maximization --- and we show that our methods have a linear time complexity, both theoretically and empirically. On synthetic data, we show our methods to be able to infer flexible Hawkes triggering kernels. On two large-scale Twitter diffusion datasets, we show that our methods outperform the current state-of-the-art in goodness-of-fit and that the time complexity is linear in the size of the dataset. We also observe that on diffusions related to online videos, the learned kernels reflect the perceived longevity for different content types such as music or pets videos.
Social multimedia computing
Publisher: ACM
Date: 03-11-2014
Smallset Timelines: A Visual Representation of Data Preprocessing Decisions
Publisher: ACM
Date: 20-06-2022
Modeling personal and social network context for event annotation in images
Publisher: ACM
Date: 18-06-2007
Social Affinity Filtering: Recommendation Through Fine-grained Analysis of User Interactions and Activities
Publisher: ACM
Date: 07-10-2013
Improving LDA topic models for microblogs via tweet pooling and automatic labeling
Publisher: ACM
Date: 28-07-2013
Scalable mobile video retrieval with sparse projection learning and pseudo label mining
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
DOI: 10.1109/MMUL.2013.13
Noise reduction in ultra-low light digital holographic microscopy using neural networks
Publisher: SPIE
Date: 30-12-2019
DOI: 10.1117/12.2539548
2015 4thTERMIS World CongressBoston, MassachusettsSeptember 8–11, 2015
Publisher: Mary Ann Liebert Inc
Date: 09-2015
The Accuracy and Value of Machine-generated Image Tags: Design and User Evaluation of an End-to-end Image Tagging System
Publisher: ACM
Date: 05-07-2010
New Objective Functions for Social Collaborative Filtering
Publisher: ACM
Date: 16-04-2012
Video Genetics: a Case Study from Youtube
Publisher: ACM
Date: 25-10-2010
Analytics-driven asset management
Publisher: IBM
Date: 2011
Studying object naming with online photos and caption
Publisher: ACM
Date: 30-10-2015
Tracking large-scale video remix in real-world events
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
Revisiting revisits in trajectory recommendation
Publisher: ACM
Date: 31-08-2017
GeoMM 2014: The third ACM multimedia workshop on geotagging and its applications in multimedia
Publisher: ACM
Date: 03-11-2014
Cognitive mechanism related to line drawings and its applications in intelligent process of visual media: a survey
Publisher: Springer Science and Business Media LLC
Date: 22-06-2016
ImageHive: Interactive Content-Aware Image Summarization
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2012
DOI: 10.1109/MCG.2011.89
An Interactive SpiralTape Video Summarization
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Choosing basic-level concept names using visual and language context
Publisher: IEEE
Date: 2015
DOI: 10.1109/WACV.2015.85
Event mining in multimedia streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Knowledge Guided Attention and Inference for Describing Images Containing Unseen Objects
Publisher: Springer International Publishing
Date: 2018
Content is dead; long-live content!
Publisher: ACM
Date: 29-10-2012
A taxonomy of semantic web data retrieval techniques
Publisher: ACM
Date: 07-10-2015
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series
Publisher: ACM
Date: 19-04-2021
Holo-UNet: hologram-to-hologram neural network restoration for high fidelity low light quantitative phase imaging of live cells
Publisher: Optica Publishing Group
Date: 09-09-2020
DOI: 10.1364/BOE.395302
Abstract: Intensity shot noise in digital holograms distorts the quality of the phase images after phase retrieval, limiting the usefulness of quantitative phase microscopy (QPM) systems in long term live cell imaging. In this paper, we devise a hologram-to-hologram neural network, Holo-UNet, that restores high quality digital holograms under high shot noise conditions (sub-mW/cm 2 intensities) at high acquisition rates (sub-milliseconds). In comparison to current phase recovery methods, Holo-UNet denoises the recorded hologram, and so prevents shot noise from propagating through the phase retrieval step that in turn adversely affects phase and intensity images. Holo-UNet was tested on 2 independent QPM systems without any adjustment to the hardware setting. In both cases, Holo-UNet outperformed existing phase recovery and block-matching techniques by ∼ 1.8 folds in phase fidelity as measured by SSIM. Holo-UNet is immediately applicable to a wide range of other high-speed interferometric phase imaging techniques. The network paves the way towards the expansion of high-speed low light QPM biological imaging with minimal dependence on hardware constraints.
Quantitative T1ρ MRI of the Head and Neck Discriminates Carcinoma and Benign Hyperplasia in the Nasopharynx
Publisher: American Society of Neuroradiology (ASNR)
Date: 29-10-2020
DOI: 10.3174/AJNR.A6828
Social media use by government: From the routine to the critical
Publisher: ACM
Date: 12-06-2011
Visualizing graph differences from social media streams
Publisher: ACM
Date: 30-01-2019
Visual saliency with side information
Publisher: IEEE
Date: 04-2009
Probabilistic Visual Concept Trees
Publisher: ACM
Date: 25-10-2010
Learning knowledge bases for multimedia in 2015
Publisher: ACM
Date: 13-10-2015
Semantic model vectors for complex video event recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Social Media Use by Government: From the Routine to The Critical
Publisher: Elsevier BV
Date: 10-2012
Bilateral Correspondence Model for Words-and-Pictures Association in Multimedia-Rich Microblogs
Publisher: Association for Computing Machinery (ACM)
Date: 06-2014
DOI: 10.1145/2611388
Abstract: Nowadays, the amount of multimedia contents in microblogs is growing significantly. More than 20% of microblogs link to a picture or video in certain large systems. The rich semantics in microblogs provides an opportunity to endow images with higher-level semantics beyond object labels. However, this raises new challenges for understanding the association between multimodal multimedia contents in multimedia-rich microblogs. Disobeying the fundamental assumptions of traditional annotation, tagging, and retrieval systems, pictures and words in multimedia-rich microblogs are loosely associated and a correspondence between pictures and words cannot be established. To address the aforementioned challenges, we present the first study analyzing and modeling the associations between multimodal contents in microblog streams, aiming to discover multimodal topics from microblogs by establishing correspondences between pictures and words in microblogs. We first use a data-driven approach to analyze the new characteristics of the words, pictures, and their association types in microblogs. We then propose a novel generative model called the Bilateral Correspondence Latent Dirichlet Allocation (BC-LDA) model. Our BC-LDA model can assign flexible associations between pictures and words and is able to not only allow picture-word co-occurrence with bilateral directions, but also single modal association. This flexible association can best fit the data distribution, so that the model can discover various types of joint topics and generate pictures and words with the topics accordingly. We evaluate this model extensively on a large-scale real multimedia-rich microblogs dataset. We demonstrate the advantages of the proposed model in several application scenarios, including image tagging, text illustration, and topic discovery. The experimental results demonstrate that our proposed model can significantly and consistently outperform traditional approaches.
Detecting stress based on social interactions in social networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-0010
Twitter-driven YouTube Views: Beyond Individual Influencers
Publisher: ACM
Date: 03-11-2014
Content is Dead ... Long Live Content: The New Age of Multimedia-Hard Problems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
DOI: 10.1109/MMUL.2014.5
Social event detection with interaction graph modeling
Publisher: ACM
Date: 29-10-2012
Editorial for the ICMR 2019 special issue
Publisher: Springer Science and Business Media LLC
Date: 21-02-2020
Differential topic models
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Keypoint encoding and transmission for improved feature extraction from compressed images
Publisher: IEEE
Date: 06-2015
Fast kernel learning for spatial pyramid matching
Publisher: IEEE
Date: 06-2008
Slightly supervised learning of part-based appearance models
Publisher: IEEE
Date: 2004
AutoRec: Autoencoders meet collaborative filtering
Publisher: ACM
Date: 18-05-2015
Layered dynamic mixture model for pattern discovery in asynchronous multi-modal streams
Publisher: IEEE
Date: 2005
Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos
Publisher: ACM Press
Date: 2018
Modeling Emotion Influence in Image Social Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity
Publisher: International World Wide Web Conferences Steering Committee
Date: 03-04-2017
Analyzing social media via event facets
Publisher: ACM
Date: 29-10-2012
The Shapes of the Fourth Estate During the Pandemic: Profiling COVID-19 News Consumption in Eight Countries
Publisher: Association for Computing Machinery (ACM)
Date: 28-09-2023
DOI: 10.1145/3610108
Contextual wisdom: Social relations and correlations for multimedia event annotation
Publisher: ACM
Date: 29-09-2007
Video mining: Pattern discovery versus pattern recognition
Publisher: IEEE
Date: 2004
Picture Tags and World Knowledge: Learning Tag Relations from Visual Semantic Sources
Publisher: ACM
Date: 21-10-2013
IBM multimedia search and retrieval system
Publisher: ACM
Date: 09-07-2007
Describing and Predicting Online Items with Reshare Cascades via Dual Mixture Self-exciting Processes
Publisher: ACM
Date: 19-10-2020
Analyzing and Predicting Emoji Usages in Social Media
Publisher: ACM Press
Date: 2018
Analysing user identity via time-sensitive semantic edit distance (t-SED): a case study of Russian trolls on Twitter
Publisher: Springer Science and Business Media LLC
Date: 07-2019
Intercultural literacy dialogue: international assessment moderation in early childhood teacher education
Publisher: Informa UK Limited
Date: 18-02-2020
Discovering meaningful multimedia patterns with audio-visual concepts and associated text
Publisher: IEEE
Date: 2004
Roslingifier: Semi-Automated Storytelling for Animated Scatterplots
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Structure analysis of soccer video with hidden Markov models
Publisher: IEEE
Date: 05-2002
Social Collaborative Filtering for Cold-start Recommendations
Publisher: ACM
Date: 06-10-2014
Related Organisations
Related Funding Activities
Discovery Projects - Grant ID: DP180101985
Start Date: 06-2018
End Date: 09-2022
Amount: $332,516.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP140102185
Start Date: 05-2014
End Date: 12-2017
Amount: $440,000.00
Funder: Australian Research Council
View Funded ActivityARC Future Fellowships - Grant ID: FT230100563
Start Date: 07-2023
End Date: 06-2027
Amount: $1,113,857.00
Funder: Australian Research Council
View Funded Activity