ORCID Profile
0000-0002-9738-4949
Current Organisation
University of Queensland
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)
Information Systems | Pattern Recognition and Data Mining | Database Management | Global Information Systems | Crop and pasture production | Family and Household Studies | Animal reproduction and breeding | Horticultural crop improvement (incl. selection and breeding) | Infrastructure Engineering and Asset Management | Crop and pasture improvement (incl. selection and breeding) | Environmental Engineering Modelling | Social Change | Environmental Engineering | Data mining and knowledge discovery | Data and information privacy | Information Systems Management | Distributed systems and algorithms | Database systems | Cyberphysical systems and internet of things | Demography | Social Policy | Manufacturing robotics | Data management and data science | Distributed computing and systems software
ANZSRC Socio-Economic Objective (SEO)
Electronic Information Storage and Retrieval Services | Information Processing Services (incl. Data Entry and Capture) | Expanding Knowledge through Studies of Human Society | Water Services and Utilities | Urban and Industrial Water Management | Application Tools and System Utilities | Families and Family Services | Social Class and Inequalities |
Related Links
Publications
Indexing Evolving Events from Tweet Streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Extracting representative motion flows for effective video retrieval
Publisher: Springer Science and Business Media LLC
Date: 24-02-2011
DDHH: A Decentralized Deep Learning Framework for Large-scale Heterogeneous Networks
Publisher: IEEE
Date: 04-2021
Suicidal ideation and mental disorder detection with attentive relation networks
Publisher: Springer Science and Business Media LLC
Date: 24-06-2021
A multi-layer memory sharing network for video captioning
Publisher: Elsevier BV
Date: 04-2023
FFM: Injecting Out-of-Domain Knowledge via Factorized Frequency Modification
Publisher: IEEE
Date: 2023
DeHIN: A Decentralized Framework for Embedding Large-Scale Heterogeneous Information Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Machine Unlearning for Image Retrieval
Publisher: ACM
Date: 10-10-2022
Social lens: Searching and browsing communities by content and interaction
Publisher: IEEE
Date: 04-2017
Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 10-10-2016
DOI: 10.1145/2873055
Abstract: Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, the extreme sparsity of a user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, the Topic-Region Model (TRM) , to simultaneously discover the semantic, temporal, and spatial patterns of users’ check-in activities, and to model their joint effect on users’ decision making for selection of POIs to visit. To demonstrate the applicability and flexibility of TRM, we investigate how it supports two recommendation scenarios in a unified way, that is, hometown recommendation and out-of-town recommendation. TRM effectively overcomes data sparsity by the complementarity and mutual enhancement of the erse information associated with users’ check-in activities (e.g., check-in content, time, and location) in the processes of discovering heterogeneous patterns and producing recommendations. To support real-time POI recommendations, we further extend the TRM model to an online learning model, TRM-Online, to track changing user interests and speed up the model training. In addition, based on the learned model, we propose a clustering-based branch and bound algorithm (CBB) to prune the POI search space and facilitate fast retrieval of the top- k recommendations. We conduct extensive experiments to evaluate the performance of our proposals on two real-world datasets, including recommendation effectiveness, overcoming the cold-start problem, recommendation efficiency, and model-training efficiency. The experimental results demonstrate the superiority of our TRM models, especially TRM-Online, compared with state-of-the-art competitive methods, by making more effective and efficient mobile recommendations. In addition, we study the importance of each type of pattern in the two recommendation scenarios, respectively, and find that exploiting temporal patterns is most important for the hometown recommendation scenario, while the semantic patterns play a dominant role in improving the recommendation effectiveness for out-of-town users.
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
Publisher: IEEE
Date: 12-2021
Quartet-net Learning for Visual Instance Retrieval
Publisher: ACM
Date: 10-2016
Video-to-Shot Tag Propagation by Graph Sparse Group Lasso
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
A Distance-Computation-Free Search Scheme for Binary Code Databases
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Enhancing Social Recommendation With Adversarial Graph Convolutional Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Semantics-reconstructing hashing for cross-modal retrieval
Publisher: Springer International Publishing
Date: 2020
Aggregation-Based Graph Convolutional Hashing for Unsupervised Cross-Modal Retrieval
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Video Captioning by Learning from Global Sentence and Looking Ahead
Publisher: Association for Computing Machinery (ACM)
Date: 07-06-2023
DOI: 10.1145/3587252
Abstract: Video captioning aims to automatically generate natural language sentences describing the content of a video. Although encoder-decoder-based models have achieved promising progress, it is still very challenging to effectively model the linguistic behavior of humans in generating video captions. In this paper, we propose a novel video captioning model by learning from gLobal sEntence and looking AheaD, LEAD for short. Specifically, LEAD consists of two modules: a Vision Module (VM) and a Language Module (LM) . Thereinto, VM is a novel attention network, which can map visual features to high-level language space and model entire sentences explicitly. LM can not only effectively make use of the information of the previous sequence when generating the current word, but also have a look at the future word. Therefore, based on VM and LM, LEAD can obtain global sentence information and future word information to make video captioning more like a fill-in-the-blank task than a word-by-word sentence generation. In addition, we also propose an autonomous strategy and a multi-stage training scheme to optimize the model, which can mitigate the problem of information leakage. Extensive experiments show that LEAD outperforms some state-of-the-art methods on MSR-VTT, MSVD, and VATEX, demonstrating the effectiveness of the proposed approach in video captioning. In addition, we release the code of our proposed model to be publicly available. 1
High-dimensional indexing with oriented cluster representation for multimedia databases
Publisher: IEEE
Date: 06-2009
Multi-Output Regression with Tag Correlation Analysis for Effective Image Tagging
Publisher: Springer International Publishing
Date: 2014
Dual Dimensionality Reduction for Efficient Video Similarity Search
Publisher: Springer Berlin Heidelberg
Date: 2007
Leveraging Weak Semantic Relevance for Complex Video Event Classification
Publisher: IEEE
Date: 10-2017
Human Consensus-Oriented Image Captioning
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: Image captioning aims to describe an image with a concise, accurate, and interesting sentence. To build such an automatic neural captioner, the traditional models align the generated words with a number of human-annotated sentences to mimic human-like captions. However, the crowd-sourced annotations inevitably come with data quality issues such as grammatical errors, wrong identification of visual objects and sub-optimal sentence focus. During the model training, existing methods treat all the annotations equally regardless of the data quality. In this work, we explicitly engage human consensus to measure the quality of ground truth captions in advance, and directly encourage the model to learn high quality captions with high priority. Therefore, the proposed consensus-oriented method can accelerate the training process and achieve superior performance with only supervised objective without time-consuming reinforcement learning. The novel consensus loss can be implemented into most of the existing state-of-the-art methods, boosting the BLEU-4 performance by maximum relative 12.47% comparing to the conventional cross-entropy loss. Extensive experiments are conducted on MS-COCO Image Captioning dataset demonstrating the proposed human consensus-oriented training method can significantly improve the training efficiency and model effectiveness.
CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language
Publisher: IEEE
Date: 03-2020
Bidirectional Discrete Matrix Factorization Hashing for Image Search
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Graph-Based Relation-Aware Representation Learning for Clothing Matching
Publisher: Springer International Publishing
Date: 2020
Local image tagging via graph regularized joint group sparsity
Publisher: Elsevier BV
Date: 05-2013
Ranking weighted clustering coefficient in large dynamic graphs
Publisher: Springer Science and Business Media LLC
Date: 11-10-2016
Discrete Nonnegative Spectral Clustering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Quick identification of near-duplicate video sequences with cut signature
Publisher: Springer Science and Business Media LLC
Date: 03-2011
Online Near-Duplicate Video Clip Detection and Retrieval: An Accurate and Fast System
Publisher: IEEE
Date: 03-2009
DOI: 10.1109/ICDE.2009.17
Special issue on responsible data management and data science
Publisher: Springer Science and Business Media LLC
Date: 23-08-2022
Scalable Video Event Retrieval by Visual State Binary Embedding
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2016
Local Deep Descriptors in Bag-of-Words for Image Retrieval
Publisher: ACM
Date: 23-10-2017
Localized Co-Occurrence Model for Fast Approximate Search in 3D Structure Databases
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion
Publisher: ACM
Date: 11-02-2022
Leveraging the invariant side of generative zero-shot learning
Publisher: IEEE
Date: 06-2019
Dissimilarity measures for content-based image retrieval
Publisher: IEEE
Date: 06-2008
Discrete Deep Learning for Fast Content-Aware Recommendation
Publisher: ACM
Date: 02-02-2018
Completely unsupervised cross-modal hashing
Publisher: Springer International Publishing
Date: 2020
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Publisher: Elsevier BV
Date: 08-2012
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
A Sparse Embedding and Least Variance Encoding Approach to Hashing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches
Publisher: ACM
Date: 12-10-2020
Transfer tagging from image to video
Publisher: ACM
Date: 28-11-2011
Effective multiple feature hashing for large-scale near-duplicate video retrieval
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system
Publisher: Elsevier BV
Date: 10-2018
Robust Learning with Adversarial Perturbations and Label Noise
Publisher: ACM
Date: 13-12-2022
Scalable Supervised Asymmetric Hashing With Semantic and Latent Factor Embedding
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
MRA-Net: Improving VQA Via Multi-Modal Relation Attention Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Streaming ranking based recommender systems
Publisher: ACM
Date: 27-06-2018
Restricted boltzmann machine based active learning for sparse recommendation
Publisher: Springer International Publishing
Date: 2018
Discrete binary hashing towards efficient fashion recommendation
Publisher: Springer International Publishing
Date: 2018
A temporal context-aware model for user behavior modeling in social media systems
Publisher: ACM
Date: 18-06-2014
Multiple feature hashing for real-time large scale near-duplicate video retrieval
Publisher: ACM
Date: 28-11-2011
Semi-decentralized Federated Ego Graph Learning for Recommendation
Publisher: ACM
Date: 30-04-2023
Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
A gram-based string paradigm for efficient video subsequence search
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 03-04-2020
Abstract: Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data. However, most mainstream models suffer from catastrophic forgetting and insufficient robustness issues, thereby failing to fully retain or exploit long-term knowledge while being prone to cause severe error accumulation. In this paper, we propose a novel Continual Meta-Learning approach with Bayesian Graph Neural Networks (CML-BGNN) that mathematically formulates meta-learning as continual learning of a sequence of tasks. With each task forming as a graph, the intra- and inter-task correlations can be well preserved via message-passing and history transition. To remedy topological uncertainty from graph initialization, we utilize Bayes by Backprop strategy that approximates the posterior distribution of task-specific parameters with amortized inference networks, which are seamlessly integrated into the end-to-end edge learning. Extensive experiments conducted on the miniImageNet and tieredImageNet datasets demonstrate the effectiveness and efficiency of the proposed method, improving the performance by 42.8% compared with state-of-the-art on the miniImageNet 5-way 1-shot classification task.
Dimension-Specific Search for Multimedia Retrieval
Publisher: Springer Berlin Heidelberg
Date: 2009
Semantic Enhanced Video Captioning with Multi-feature Fusion
Publisher: Association for Computing Machinery (ACM)
Date: 30-05-2023
DOI: 10.1145/3588572
Abstract: Video captioning aims to automatically describe a video clip with informative sentences. At present, deep learning-based models have become the mainstream for this task and achieved competitive results on public datasets. Usually, these methods leverage different types of features to generate sentences, e.g., semantic information, 2D or 3D features. However, some methods only treat semantic information as a complement of visual representations and cannot fully exploit it some of them ignore the relationship between different types of features. In addition, most of them select multiple frames of a video with an equally spaced s ling scheme, resulting in much redundant information. To address these issues, we present a novel video-captioning framework, Semantic Enhanced video captioning with Multi-feature Fusion, SEMF for short. It optimizes the use of different types of features from three aspects. First, a semantic encoder is designed to enhance meaningful semantic features through a semantic dictionary to boost performance. Second, a discrete selection module pays attention to important features and obtains different contexts at different steps to reduce feature redundancy. Finally, a multi-feature fusion module uses a novel relation-aware attention mechanism to separate the common and complementary components of different features to provide more effective visual features for the next step. Moreover, the entire framework can be trained in an end-to-end manner. Extensive experiments are conducted on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSR-VTT) datasets. The results demonstrate that SEMF is able to achieve state-of-the-art results.
Keyword Search Over Probabilistic RDF Graphs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
What’s Wrong with That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution
Publisher: IEEE
Date: 06-2016
Multi-attention Network for One Shot Learning
Publisher: IEEE
Date: 07-2017
Scrutinizing Privacy Policy Compliance of Virtual Personal Assistant Apps
Publisher: ACM
Date: 10-10-2022
Exploring Distributional Discrepancy for Multidimensional Point Set Retrieval
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2011
Effective data co-reduction for multimedia similarity search
Publisher: ACM
Date: 12-06-2011
Privacy Protection in Deep Multi-modal Retrieval
Publisher: ACM
Date: 11-07-2021
Multi-feature fusion via hierarchical regression for multimedia analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
Collaborative Learning for Extremely Low Bit Asymmetric Hashing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Feature Reconstruction by Laplacian Eigenmaps for Efficient Instance Search
Publisher: ACM
Date: 05-06-2018
Alleviating Feature Confusion for Generative Zero-shot Learning
Publisher: ACM
Date: 15-10-2019
Cycle-consistent Conditional Adversarial Transfer Networks
Publisher: ACM
Date: 15-10-2019
Modal-adversarial Semantic Learning Network for Extendable Cross-modal Retrieval
Publisher: ACM
Date: 05-06-2018
Learning to Ask Appropriate Questions in Conversational Recommendation
Publisher: ACM
Date: 11-07-2021
Speed up interactive image retrieval
Publisher: Springer Science and Business Media LLC
Date: 23-04-2008
Modeling Daily Crime Events Prediction Using Seq2Seq Architecture
Publisher: Springer International Publishing
Date: 2021
HIGH DIMENSIONAL INDEXING FOR PROTEIN STRUCTURE MATCHING USING BOWTIES
Publisher: PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO.
Date: 2005
Entropy-based uncertainty calibration for generalized zero-shot learning
Publisher: Springer International Publishing
Date: 2021
Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Unifying Multi-Source Social Media Data for Personalized Travel Route Planning
Publisher: ACM
Date: 07-08-2017
Automatic tagging by exploring tag information capability and correlation
Publisher: Springer Science and Business Media LLC
Date: 02-06-2011
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Spatio-temporal Event Modeling and Ranking
Publisher: Springer Berlin Heidelberg
Date: 2013
Transfer Independently Together: A Generalized Framework for Domain Adaptation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Self-Supervised Learning for Recommender Systems: A Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
CRSAL
Publisher: Association for Computing Machinery (ACM)
Date: 13-06-2020
DOI: 10.1145/3394592
Abstract: Recommender systems have been attracting much attention from both academia and industry because of their ability to capture user interests and generate personalized item recommendations. As the life pace in contemporary society speeds up, traditional recommender systems are inevitably limited by their disconnected interaction styles and low adaptivity to users’ evolving demands. Consequently, conversational recommender systems emerge as a prospective research area, where an intelligent dialogue agent is integrated with a recommender system. Conversational recommender systems possess the ability to accurately understand end-users’ intent or request and generate human-like dialogue responses when performing recommendations. However, existing conversational recommender systems only allow the systems to ask users for more preference information, while users’ further questions and concerns about the recommended items (e.g., enquiring the location of a recommended restaurant) can hardly be addressed. Though the recent task-oriented dialogue systems allow for two-way communications, they are not easy to train because of their high dependence on human guidance in terms of user intent recognition and system response generation. Hence, to enable two-way human-machine communications and tackle the challenges brought by manually crafted rules, we propose Conversational Recommender System with Adversarial Learning (CRSAL), a novel end-to-end system to tackle the task of conversational recommendation. In CRSAL, we innovatively design a fully statistical dialogue state tracker coupled with a neural policy agent to precisely capture each user’s intent from limited dialogue data and generate conversational recommendation actions. We further develop an adversarial Actor-Critic reinforcement learning approach to adaptively refine the quality of generated system actions, thus ensuring coherent human-like dialogue responses. Extensive experiments on two benchmark datasets fully demonstrate the superiority of CRSAL on conversational recommendation tasks.
Urban multimedia computing: Emerging methods in multimedia computing for urban data analysis and applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Fashion Recommendation with Multi-relational Representation Learning
Publisher: Springer International Publishing
Date: 2020
Fast parallel path concatenation for graph extraction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Multi-hop path queries over knowledge graphs with neural memory networks
Publisher: Springer International Publishing
Date: 2019
Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.
Guest Editorial: Knowledge-Based Multimedia Computing
Publisher: Springer Science and Business Media LLC
Date: 02-10-2017
Tagging Image with Informative and Correlative Tags
Publisher: Springer Berlin Heidelberg
Date: 2011
Indexing evolving events from tweet streams
Publisher: IEEE
Date: 05-2016
Mining multi-tag association for image tagging
Publisher: Springer Science and Business Media LLC
Date: 22-12-2010
Evaluation of machine learning interpolation techniques for prediction of physical properties
Publisher: Elsevier BV
Date: 02-2015
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Publisher: ACM
Date: 03-11-2019
Group Recommendation with Latent Voting Mechanism
Publisher: IEEE
Date: 04-2020
TSAUB: A temporal-sentiment-aware user behavior model for personalized recommendation
Publisher: Springer International Publishing
Date: 2018
Correction to: Deep-sea organisms tracking using dehazing and deep learning
Publisher: Springer Science and Business Media LLC
Date: 07-07-2020
Transductive Visual-Semantic Embedding for Zero-shot Learning
Publisher: ACM
Date: 06-06-2017
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices
Publisher: ACM
Date: 20-04-2020
Effective and Efficient Query Processing for Video Subsequence Identification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2009
Joint Event-Partner Recommendation in Event-Based Social Networks
Publisher: IEEE
Date: 04-2018
Discrete Multimodal Hashing With Canonical Views for Robust Mobile Landmark Search
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
On the influence propagation of web videos
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2014
Deep Collaborative Discrete Hashing With Semantic-Invariant Structure Construction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
High-order nonlocal Hashing for unsupervised cross-modal retrieval
Publisher: Springer Science and Business Media LLC
Date: 27-02-2021
Databases theory and applications: 28th Australasian database conference, ADC 2017 Brisbane, QLD, Australia, september 25–28, 2017 proceedings
Publisher: Springer International Publishing
Date: 2017
Linear cross-modal hashing for efficient multimedia search
Publisher: ACM
Date: 21-10-2013
Robust discrete code modeling for supervised hashing
Publisher: Elsevier BV
Date: 03-2018
Editorial for Special Issue on Large-scale Pre-training: Data, Models, and Fine-tuning
Publisher: Springer Science and Business Media LLC
Date: 31-03-2023
CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
Publisher: ACM
Date: 17-10-2021
Incomplete Cross-modal Retrieval with Dual-Aligned Variational Autoencoders
Publisher: ACM
Date: 12-10-2020
Learning Elastic Embeddings for Customizing On-Device Recommenders
Publisher: ACM
Date: 14-08-2021
Embedding and predicting the event at early stage
Publisher: Springer Science and Business Media LLC
Date: 22-03-2019
Deep-Sea Organisms Tracking Using Dehazing and Deep Learning
Publisher: Springer Science and Business Media LLC
Date: 10-10-2018
Web Technologies and Applications
Publisher: Springer Berlin Heidelberg
Date: 2011
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation
Publisher: ACM
Date: 15-10-2019
Spatial-aware Multimodal Location Estimation for Social Images
Publisher: ACM
Date: 13-10-2015
Back to the future: A logical framework for temporal information representation and inferencing from financial news
Publisher: IEEE
Date: 2003
SK-LSH
Publisher: Association for Computing Machinery (ACM)
Date: 05-2014
Abstract: Approximate Nearest Neighbor (ANN) search in high dimensional space has become a fundamental paradigm in many applications. Recently, Locality Sensitive Hashing (LSH) and its variants are acknowledged as the most promising solutions to ANN search. However, state-of-the-art LSH approaches suffer from a drawback: accesses to candidate objects require a large number of random I/O operations. In order to guarantee the quality of returned results, sufficient objects should be verified, which would consume enormous I/O cost. To address this issue, we propose a novel method, called SortingKeys-LSH (SK-LSH), which reduces the number of page accesses through locally arranging candidate objects. We firstly define a new measure to evaluate the distance between the compound hash keys of two points. A linear order relationship on the set of compound hash keys is then created, and the corresponding data points can be sorted accordingly. Hence, data points that are close to each other according to the distance measure can be stored locally in an index file. During the ANN search, only a limited number of disk pages among few index files are necessary to be accessed for sufficient candidate generation and verification, which not only significantly reduces the response time but also improves the accuracy of the returned results. Our exhaustive empirical study over several real-world data sets demonstrates the superior efficiency and accuracy of SK-LSH for the ANN search, compared with state-of-the-art methods, including LSB, C2LSH and CK-Means.
I read, I saw, I tell: Texts Assisted Fine-Grained Visual Classification.
Publisher: ACM
Date: 15-10-2018
Proactive Privacy-preserving Learning for Retrieval
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 18-05-2021
Abstract: Deep Neural Networks (DNNs) have recently achieved remarkable performance in image retrieval, yet posing great threats to data privacy. On the one hand, one may misuse a deployed DNNs based system to look up data without consent. On the other hand, organizations or in iduals would legally or illegally collect data to train high-performance models outside the scope of legitimate purposes. Unfortunately, less effort has been made to safeguard data privacy against malicious uses of DNNs. In this paper, we propose a data-centric Proactive Privacy-preserving Learning (PPL) algorithm for hashing based retrieval, which achieves the protection purpose by employing a generator to transfer the original data into the adversarial data with quasi-imperceptible perturbations before releasing them. When the data source is infiltrated, the adversarial data can confuse menacing retrieval models to make erroneous predictions. Given that the prior knowledge of malicious models is not available, a surrogate retrieval model is instead introduced acting as a fooling target. The framework is trained by a two-player game conducted between the generator and the surrogate model. More specifically, the generator is updated to enlarge the gap between the adversarial data and the original data, aiming to lower the search accuracy of the surrogate model. On the contrary, the surrogate model is trained with the opposing objective that is to maintain the search performance. As a result, an effective and robust adversarial generator is encouraged. Furthermore, to facilitate an effective optimization, a Gradient Reversal Layer (GRL) module is inserted to connect two models, enabling the two-player game in a one-step learning. Extensive experiments on three widely-used realistic datasets prove the effectiveness of the proposed method.
Bounded coordinate system indexing for real-time video clip search
Publisher: Association for Computing Machinery (ACM)
Date: 05-2009
Abstract: Recently, video clips have become very popular online. The massive influx of video clips has created an urgent need for video search engines to facilitate retrieving relevant clips. Different from traditional long videos, a video clip is a short video often expressing a moment of significance. Due to the high complexity of video data, efficient video clip search from large databases turns out to be very challenging. We propose a novel video clip representation model called the Bounded Coordinate System (BCS), which is the first single representative capturing the dominating content and content—changing trends of a video clip. It summarizes a video clip by a coordinate system, where each of its coordinate axes is identified by principal component analysis (PCA) and bounded by the range of data projections along the axis. The similarity measure of BCS considers the operations of translation, rotation, and scaling for coordinate system matching. Particularly, rotation and scaling reflect the difference of content tendencies. Compared with the quadratic time complexity of existing methods, the time complexity of measuring BCS similarity is linear. The compact video representation together with its linear similarity measure makes real-time search from video clip collections feasible. To further improve the retrieval efficiency for large video databases, a two-dimensional transformation method called Bidistance Transformation (BDT) is introduced to utilize a pair of optimal reference points with respect to bidirectional axes in BCS. Our extensive performance study on a large database of more than 30,000 video clips demonstrates that BCS achieves very high search accuracy according to human judgment. This indicates that content tendencies are important in determining the meanings of video clips and confirms that BCS can capture the inherent moment of video clip to some extent that better resembles human perception. In addition, BDT outperforms existing indexing methods greatly. Integration of the BCS model and BDT indexing can achieve real-time search from large video clip databases.
Neural Memory Streaming Recommender Networks with Adversarial Training
Publisher: ACM
Date: 19-07-2018
Adaptive component embedding for unsupervised domain adaptation
Publisher: IEEE
Date: 07-2019
Attribute-aware explainable complementary clothing recommendation
Publisher: Springer Science and Business Media LLC
Date: 28-07-2021
Heterogeneous Environment Aware Streaming Graph Partitioning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Prototype-Matching Graph Network for Heterogeneous Domain Adaptation
Publisher: ACM
Date: 12-10-2020
Correlation-based retrieval for heavily changed near-duplicate videos
Publisher: Association for Computing Machinery (ACM)
Date: 12-2011
Abstract: The unprecedented and ever-growing number of Web videos nowadays leads to the massive existence of near-duplicate videos. Very often, some near-duplicate videos exhibit great content changes, while the user perceives little information change, for ex le, color features change significantly when transforming a color video with a blue filter. These feature changes contribute to low-level video similarity computations, making conventional similarity-based near-duplicate video retrieval techniques incapable of accurately capturing the implicit relationship between two near-duplicate videos with fairly large content modifications. In this paper, we introduce a new dimension for near-duplicate video retrieval. Different from existing near-duplicate video retrieval approaches which are based on video-content similarity, we explore the correlation between two videos. The intuition is that near-duplicate videos should preserve strong information correlation in spite of intensive content changes. More effective retrieval with stronger tolerance is achieved by replacing video-content similarity measures with information correlation analysis. Theoretical justification and experimental results prove the effectiveness of correlation-based near-duplicate retrieval.
Robust cross-media transfer for visual event detection
Publisher: ACM
Date: 29-10-2012
Database Systems for Advanced Applications
Publisher: Springer Berlin Heidelberg
Date: 2009
Tag localization with spatial correlations and joint group sparsity
Publisher: IEEE
Date: 06-2011
Near-optimal partial linear scan for nearest neighbor search in high-dimensional space
Publisher: Springer Berlin Heidelberg
Date: 2013
Near-duplicate video retrieval
Publisher: Association for Computing Machinery (ACM)
Date: 08-2013
Abstract: The exponential growth of online videos, along with increasing user involvement in video-related activities, has been observed as a constant phenomenon during the last decade. User's time spent on video capturing, editing, uploading, searching, and viewing has boosted to an unprecedented level. The massive publishing and sharing of videos has given rise to the existence of an already large amount of near-duplicate content. This imposes urgent demands on near-duplicate video retrieval as a key role in novel tasks such as video search, video copyright protection, video recommendation, and many more. Driven by its significance, near-duplicate video retrieval has recently attracted a lot of attention. As discovered in recent works, latest improvements and progress in near-duplicate video retrieval, as well as related topics including low-level feature extraction, signature generation, and high-dimensional indexing, are employed to assist the process. As we survey the works in near-duplicate video retrieval, we comparatively investigate existing variants of the definition of near-duplicate video, describe a generic framework, summarize state-of-the-art practices, and explore the emerging trends in this research topic.
Zero-Shot Learning by Harnessing Adversarial Samples
Publisher: ACM
Date: 26-10-2023
Self-taught dimensionality reduction on the high-dimensional small-sized data
Publisher: Elsevier BV
Date: 2013
Discovering areas of interest with geo-tagged images and check-ins
Publisher: ACM
Date: 29-10-2012
Dimensionality reduction for dimension-specific search
Publisher: ACM
Date: 23-07-2007
Exploring Consistent Preferences: Discrete Hashing with Pair-Exemplar for Scalable Landmark Search
Publisher: ACM
Date: 19-10-2017
Context sensitive tag expansion with information inference
Publisher: Springer Berlin Heidelberg
Date: 2012
Classifying document titles based on information inference
Publisher: Springer Berlin Heidelberg
Date: 2003
Practical Online Near-Duplicate Subsequence Detection for Continuous Video Streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2010
Flexible discrete multi-view hashing with collective latent feature learning
Publisher: Springer Science and Business Media LLC
Date: 16-03-2020
Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Source-Free Progressive Graph Learning for Open-Set Domain Adaptation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Inductive Structure Consistent Hashing via Flexible Semantic Calibration
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Adversarial Bipartite Graph Learning for Video Domain Adaptation
Publisher: ACM
Date: 12-10-2020
Joint-teaching: Learning to Refine Knowledge for Resource-constrained Unsupervised Cross-modal Retrieval
Publisher: ACM
Date: 17-10-2021
Belief revision for adaptive negotiation agents
Publisher: IEEE Comput. Soc
Date: 2003
Content-Based Video Search: Is there a Need, and Is it Possible?
Publisher: IEEE
Date: 04-2008
DOI: 10.1109/INGS.2008.16
Robust Hashing With Local Models for Approximate Similarity Search
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Robust spatial-temporal deep model for multimedia event detection
Publisher: Elsevier BV
Date: 11-2016
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection
Publisher: ACM
Date: 25-07-2020
Mining near-duplicate graph for cluster-based reranking of web video search results
Publisher: Association for Computing Machinery (ACM)
Date: 11-2010
Abstract: Recently, video search reranking has been an effective mechanism to improve the initial text-based ranking list by incorporating visual consistency among the result videos. While existing methods attempt to rerank all the in idual result videos, they suffer from several drawbacks. In this article, we propose a new video reranking paradigm called cluster-based video reranking (CVR). The idea is to first construct a video near-duplicate graph representing the visual similarity relationship among videos, followed by identifying the near-duplicate clusters from the video near-duplicate graph, then ranking the obtained near-duplicate clusters based on cluster properties and intercluster links, and finally for each ranked cluster, a representative video is selected and returned. Compared to existing methods, the new CVR ranks clusters and exhibits several advantages, including superior reranking by utilizing more reliable cluster properties, fast reranking on a small number of clusters, erse and representative results. Particularly, we formulate the near-duplicate cluster identification as a novel maximally cohesive subgraph mining problem. By leveraging the designed cluster scoring properties indicating the cluster's importance and quality, random walk is applied over the near-duplicate cluster graph to rank clusters. An extensive evaluation study proves the novelty and superiority of our proposals over existing methods.
Truth discovery in material science databases
Publisher: Springer International Publishing
Date: 2015
Discovering domain disentanglement for generalized multi-source domain adaptation
Publisher: IEEE
Date: 18-07-2022
From community detection to community profiling
Publisher: Association for Computing Machinery (ACM)
Date: 03-2017
Abstract: Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we for the first time formalize the concept of community profiling. With rich user information on the network, such as user published content and user diffusion links, we characterize a community in terms of both its internal content profile and external diffusion profile. The difficulty of community profiling is often underestimated. We novelly identify three unique challenges and propose a joint Community Profiling and Detection (CPD) model to address them accordingly. We also contribute a scalable inference algorithm, which scales linearly with the data size and it is easily parallelizable. We evaluate CPD on large-scale real-world data sets, and show that it is significantly better than the state-of-the-art baselines in various tasks.
Presenting diverse location views with real-time near-duplicate photo elimination
Publisher: IEEE
Date: 04-2013
Graph Embedding for Recommendation against Attribute Inference Attacks
Publisher: ACM
Date: 19-04-2021
Imagilar: A real-time image similarity search system on mobile platform
Publisher: Springer Berlin Heidelberg
Date: 2013
Probabilistic Image Tagging with Tags Expanded By Text-Based Search
Publisher: Springer Berlin Heidelberg
Date: 2011
Joint Modeling of Users' Interests and Mobility Patterns for Point-of-Interest Recommendation
Publisher: ACM
Date: 13-10-2015
Sparse hashing for fast multimedia search
Publisher: Association for Computing Machinery (ACM)
Date: 05-2013
Abstract: Hash-based methods achieve fast similarity search by representing high-dimensional data with compact binary codes. However, both generating binary codes and encoding unseen data effectively and efficiently remain very challenging tasks. In this article, we focus on these tasks to implement approximate similarity search by proposing a novel hash based method named sparse hashing (SH for short). To generate interpretable (or semantically meaningful) binary codes, the proposed SH first converts original data into low-dimensional data through a novel nonnegative sparse coding method. SH then converts the low-dimensional data into Hamming space (i.e., binary encoding low-dimensional data) by a new binarization rule. After this, training data are represented by generated binary codes. To efficiently and effectively encode unseen data, SH learns hash functions by taking a-priori knowledge into account, such as implicit group effect of the features in training data, and the correlations between original space and the learned Hamming space. SH is able to perform fast approximate similarity search by efficient bit XOR operations in the memory of a modern PC with short binary code representations. Experimental results show that the proposed SH significantly outperforms state-of-the-art techniques.
Discrete Hashing With Multiple Supervision
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Learning Discrete Hashing Towards Efficient Fashion Recommendation
Publisher: Springer Science and Business Media LLC
Date: 03-11-2018
IDEAL: High-Order-Ensemble Adaptation Network for Learning with Noisy Labels
Publisher: ACM
Date: 10-10-2022
Batch Nearest Neighbor Search for Video Retrieval
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Distribution-based similarity measures for multi-dimensional point set retrieval applications
Publisher: ACM
Date: 26-10-2008
Long short-term enhanced memory for sequential recommendation
Publisher: Springer Science and Business Media LLC
Date: 13-05-2022
DOI: 10.1007/S11280-022-01056-9
Abstract: Sequential recommendation is a stream of studies on recommender systems, which focuses on predicting the next item a user interacts with by modeling the dynamic sequence of user-item interactions. Since being born to explore the dynamic tendency of variable-length temporal sequence, Recurrent Neural Networks (RNNs) have been paid much attention in this area. However, the inherent defects caused by the network structure of RNNs have limited their applications in sequential recommendation, which are mainly shown on two factors: RNNs tend to make point-wise predictions and ignore the collective dependencies because the temporal relationships between items change monotonically RNNs are likely to forget the essential information during processing long sequences. To solve these problems, researchers have done much work to enhance the memory mechanism of RNNs. However, although previous RNN-based methods have achieved promising performance by taking advantage of external knowledge with other advanced techniques, the improvement of the intrinsic property of existing RNNs has not been explored, which is still challenging. Therefore, in this work, we propose a novel architecture based on Long Short-Term Memories (LSTMs), a broadly-used variant of RNNs, specific for sequential recommendation, called Long Short-Term enhanced Memory (LSTeM) , which boosts the memory mechanism of original LSTMs in two ways. Firstly, we design a new structure of gates in LSTMs by introducing a “Q-K-V” triplet, a mechanism to accurately and properly model the correlation between the current item and the user’s historical behaviors at each time step. Secondly, we propose a “recover gate” to remedy the inadequacy of memory caused by the forgetting mechanism, which works with a dynamic global memory embedding. Extensive experiments have demonstrated that LSTeM achieves comparable performance to the state-of-the-art methods on the challenging datasets for sequential recommendation.
Bilinear Optimized Product Quantization for Scalable Visual Content Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Exploiting detected visual objects for frame-level video filtering
Publisher: Springer Science and Business Media LLC
Date: 27-10-2017
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation
Publisher: IEEE
Date: 11-2022
POI Popularity Prediction via Hierarchical Fusion of Multiple Social Clues
Publisher: ACM
Date: 07-08-2017
Hierarchical Latent Concept Discovery for Video Event Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2017
Web Video Event Recognition by Semantic Analysis From Ubiquitous Documents
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Local Graph Convolutional Networks for Cross-Modal Hashing
Publisher: ACM
Date: 17-10-2021
Deep Collaborative Discrete Hashing with Semantic-Invariant Structure
Publisher: ACM
Date: 18-07-2019
Efficient and Continuous Near-duplicate Video Detection
Publisher: IEEE
Date: 04-2010
Social event identification and ranking on flickr
Publisher: Springer Science and Business Media LLC
Date: 10-06-2014
3D protein structure matching by patch signatures
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11827405
Adaptively Attending to Visual Attributes and Linguistic Knowledge for Captioning
Publisher: ACM
Date: 19-10-2017
Jointly Modeling Static Visual Appearance and Temporal Pattern for Unsupervised Video Hashing
Publisher: ACM
Date: 06-11-2017
Using Detected Visual Objects to Index Video Database
Publisher: Springer International Publishing
Date: 2016
Exploiting Positional Information for Session-Based Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 16-11-2021
DOI: 10.1145/3473339
Abstract: For present e-commerce platforms, it is important to accurately predict users’ preference for a timely next-item recommendation. To achieve this goal, session-based recommender systems are developed, which are based on a sequence of the most recent user-item interactions to avoid the influence raised from outdated historical records. Although a session can usually reflect a user’s current preference, a local shift of the user’s intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user’s initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness . Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.
Learning to Diversify for Single Domain Generalization
Publisher: IEEE
Date: 10-2021
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Publisher: ACM
Date: 17-10-2018
Semi-supervised Cross-Modal Hashing with Graph Convolutional Networks
Publisher: Springer International Publishing
Date: 2020
A Domain Adaptation Approach for Multistream Classification
Publisher: Springer International Publishing
Date: 2019
PAIC: Parallelised Attentive Image Captioning
Publisher: Springer International Publishing
Date: 2020
Exploiting Subspace Relation in Semantic Labels for Cross-Modal Hashing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modeling
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
On Both Cold-Start and Long-Tail Recommendation with Social Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Statistical summarization of content features for fast near-duplicate video detection
Publisher: ACM
Date: 29-09-2007
Object-Goal Visual Navigation via Effective Exploration of Relations Among Historical Navigation States
Publisher: IEEE
Date: 06-2023
Heterogeneous Domain Adaptation Through Progressive Alignment
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Video-to-shot tag allocation by weighted sparse group lasso
Publisher: ACM
Date: 28-11-2011
Image classification with manifold learning for out-of-sample data
Publisher: Elsevier BV
Date: 08-2013
Dynamic Batch Nearest Neighbor Search in Video Retrieval
Publisher: IEEE
Date: 2007
Proactive Privacy-preserving Learning for Cross-modal Retrieval
Publisher: Association for Computing Machinery (ACM)
Date: 25-01-2023
DOI: 10.1145/3545799
Abstract: Deep cross-modal retrieval techniques have recently achieved remarkable performance, which also poses severe threats to data privacy potentially. Nowadays, enormous user-generated contents that convey personal information are released and shared on the Internet. One may abuse a retrieval system to pinpoint sensitive information of a particular Internet user, causing privacy leakage. In this article, we propose a data-centric Proactive Privacy-preserving Cross-modal Learning algorithm that fulfills the protection purpose by employing a generator to transform original data into adversarial data with quasi-imperceptible perturbations before releasing them. When the data source is infiltrated, the inside adversarial data can confuse retrieval models under the attacker’s control to make erroneous predictions. We consider the protection under a realistic and challenging setting where the prior knowledge of malicious models is agnostic. To handle this, a surrogate retrieval model is instead introduced, acting as the target to fool. The whole network is trained under a game-theoretical framework, where the generator and the retrieval model persistently evolve to fight against each other. To facilitate the optimization, a Gradient Reversal Layer module is inserted between two models, enabling a one-step learning fashion. Extensive experiments on widely used realistic datasets prove the effectiveness of the proposed method.
Imbalanced Source-free Domain Adaptation
Publisher: ACM
Date: 17-10-2021
Supervised Robust Discrete Multimodal Hashing for Cross-Media Retrieval
Publisher: ACM
Date: 24-10-2016
Learning Private Neural Language Modeling with Attentive Aggregation
Publisher: IEEE
Date: 07-2019
Learning Transferrable and Interpretable Representations for Domain Generalization
Publisher: ACM
Date: 17-10-2021
Domain Adaptive Semantic Segmentation without Source Data
Publisher: ACM
Date: 17-10-2021
Enhancing Collaborative Filtering with Generative Augmentation
Publisher: ACM
Date: 25-07-2019
Discovering the Most Influential Sites over Uncertain Data: A Rank-Based Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2012
Fast parallel path concatenation for graph extraction
Publisher: IEEE
Date: 04-2018
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Publisher: ACM
Date: 22-06-2013
Semantics Disentangling for Generalized Zero-Shot Learning
Publisher: IEEE
Date: 10-2021
On the Prediction of Re-tweeting Activities in Social Networks – A Report on WISE 2012 Challenge
Publisher: Springer Berlin Heidelberg
Date: 2012
Interactive near-duplicate video retrieval and detection
Publisher: ACM
Date: 19-10-2009
Multi-View Visual Classification via a Mixed-Norm Regularizer
Publisher: Springer Berlin Heidelberg
Date: 2013
Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks
Publisher: Association for Computing Machinery (ACM)
Date: 23-05-2020
DOI: 10.1145/3382764
Abstract: Different from the traditional recommender system, the session-based recommender system introduces the concept of the session , i.e., a sequence of interactions between a user and multiple items within a period, to preserve the user’s recent interest. The existing work on the session-based recommender system mainly relies on mining sequential patterns within in idual sessions, which are not expressive enough to capture more complicated dependency relationships among items. In addition, it does not consider the cross-session information due to the anonymity of the session data, where the linkage between different sessions is prevented. In this article, we solve these problems with the graph neural networks technique. First, each session is represented as a graph rather than a linear sequence structure, based on which a novel F ull G raph N eural N etwork (FGNN) is proposed to learn complicated item dependency. To exploit and incorporate cross-session information in the in idual session’s representation learning, we further construct a B roadly C onnected S ession (BCS) graph to link different sessions and a novel Mask-Readout function to improve session embedding based on the BCS graph. Extensive experiments have been conducted on two e-commerce benchmark datasets, i.e., Yoochoose and Diginetica , and the experimental results demonstrate the superiority of our proposal through comparisons with state-of-the-art session-based recommender models.
Efficient Histogram-Based Similarity Search in Ultra-High Dimensional Space
Publisher: Springer Berlin Heidelberg
Date: 2011
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error
Publisher: ACM
Date: 26-10-2023
Mitigating Generation Shifts for Generalized Zero-Shot Learning
Publisher: ACM
Date: 17-10-2021
Scalable gaussian process regression for prediction of material properties
Publisher: Springer International Publishing
Date: 2014
Related Organisations
Related Funding Activities
Declaration, Exploration, Enhancement And Provenance: The DEEP Approach To Data Quality Management Systems
Start Date: 2014
End Date: 2016
Funder: Australian Research Council
View Funded ActivityReal-time Event Detection, Prediction And Visualisation For Emergency Response
Start Date: 2013
End Date: 2017
Funder: Australian Research Council
View Funded ActivityMonitoring Social Events For User Online Behaviour Analytics
Start Date: 2017
End Date: 2019
Funder: Australian Research Council
View Funded Activity社会事件驱动的用户在线行为分析
Start Date: 2017
End Date: 2018
Funder: National Natural Science Foundation of China
View Funded ActivityDeep Attribute-aware Hashing For Cross Retrieval
Start Date: 2019
End Date: 2021
Funder: Australian Research Council
View Funded ActivityMonitoring Online Topic Evolvements With Near-duplicate Videos
Start Date: 2011
End Date: 2013
Funder: Australian Research Council
View Funded ActivityARC Future Fellowships - Grant ID: FT130101530
Start Date: 05-2014
End Date: 01-2019
Amount: $720,320.00
Funder: Australian Research Council
View Funded ActivityARC Centres Of Excellence - Grant ID: CE200100025
Start Date: 2021
End Date: 12-2027
Amount: $32,137,008.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP230101196
Start Date: 12-2023
End Date: 12-2026
Amount: $485,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP110103871
Start Date: 2011
End Date: 12-2013
Amount: $267,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP170103954
Start Date: 2017
End Date: 12-2020
Amount: $268,500.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP140103171
Start Date: 2014
End Date: 12-2017
Amount: $452,000.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Training Centres - Grant ID: IC230100016
Start Date: 2023
End Date: 12-2027
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Research Hubs - Grant ID: IH230100013
Start Date: 06-2024
End Date: 06-2029
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP190102353
Start Date: 01-2019
End Date: 12-2022
Amount: $262,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP210300584
Start Date: 09-2023
End Date: 09-2028
Amount: $875,432.00
Funder: Australian Research Council
View Funded Activity