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- Xiaofang ZhouSuggested from co-authorship
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Works336 from public data
- Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation634
Visualization results of the learned representations validate that DuoRec can largely alleviate the representation degeneration problem, and a model-level augmentation is proposed based on Dropout to enable better semantic preserving.
- Inter-media hashing for large-scale retrieval from heterogeneous data sources620
A novel inter-media hashing (IMH) model is proposed to explore the correlations among multiple media types from different data sources and tackle the scalability issue, which transforms multimedia data from heterogeneous data sources into a common Hamming space, in which fast search can be easily implemented by XOR and bit-count operations.
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- Self-Supervised Learning for Recommender Systems: A Survey463
This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR), and proposes an exclusive definition of SSR, on top of which a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid.
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- Learning to Diversify for Single Domain Generalization364
This paper proposes a style-complement module to enhance the generalization power of the model by synthesizing images from diverse distributions that are complementary to the source ones, and surpasses the state-of-the-art single-DG methods by up to 25.14%.
- Leveraging the Invariant Side of Generative Zero-Shot Learning347
A novel method is proposed which can directly generate the unseen features from random noises which are conditioned by the semantic descriptions, and is named leveraging invariant side GAN (LisGAN), which can outperform state-of-the-art methods with significant improvements.
- Multiple feature hashing for real-time large scale near-duplicate video retrieval333
This paper presents a novel approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR and shows that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.
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- Linear cross-modal hashing for efficient multimedia search299
This paper proposes a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals and proves that this new representation preserves the intra-similarity in each modal.
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- GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection221
The proposed GraphRfi - a GCN-based user representation learning framework to perform robust recommendation and fraudster detection in a unified way is proposed, validated to be more robust to the various types of shilling attacks over the state-of-the-art recommender systems.
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- Enhancing Social Recommendation With Adversarial Graph Convolutional Networks190
A deep adversarial framework based on graph convolutional networks (GCN) is proposed to address the problems of social recommender systems and adopts adversarial training to unify all the components by playing a Minimax game and ensure a coordinated effort to enhance recommendation performance.
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- Learning Private Neural Language Modeling with Attentive Aggregation180
This work proposes a novel model aggregation with an attention mechanism considering the contribution of client models to the global model, together with an optimization technique during server aggregation that minimizes the weighted distance between the server model and client models.
- Near-duplicate video retrieval180
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.
- Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation171
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 is proposed.
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- From Zero-Shot Learning to Cold-Start Recommendation165
This work formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR, which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper.
- Semantics Disentangling for Generalized Zero-Shot Learning159
A novel semantics disentangling framework is proposed for the generalized zero-shot learning task (SDGZSL), where the visual features of unseen classes are firstly estimated by a conditional VAE and then factorized into semantic-consistent and semantic-unrelated latent vectors.
- Neural Memory Streaming Recommender Networks with Adversarial Training159
An adaptive negative sampling framework based on Generative Adversarial Nets (GAN) is developed to optimize the proposed streaming recommender model, which effectively overcomes the limitations of classical negative sampling approaches and improves both effectiveness and efficiency of the model parameter inference.
- Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval158
This paper proposes a novel hashing approach, dubbed as discrete semantic transfer hashing (DSTH), to directly augment the semantics of discrete image hash codes by exploring auxiliary contextual modalities and guarantees direct semantic transfer and avoid information loss.
- Suicidal ideation and mental disorder detection with attentive relation networks147
Text representation is enhanced with lexicon-based sentiment scores and latent topics and relation networks are proposed to detect suicidal ideation and mental disorders with related risk indicators and the model outperforms most of its counterparts.
- A temporal context-aware model for user behavior modeling in social media systems142
The experimental results demonstrate the superiority of the TCAM models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.
- PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion140
This paper presents a systematic approach to backdooring federated recommender systems for targeted item promotion, and takes advantage of the inherent popularity bias that commonly exists in data-driven recommenders.
- Self-taught dimensionality reduction on the high-dimensional small-sized data137
Experimental results at various types of datasets show the proposed STDR outperforms the state-of-the-art algorithms in terms of k-means clustering performance and the proposed solution is theoretically guaranteed that the objective function of the proposed model converges to the global optimum.
- Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis136
It is proved that MKCCA can actually be decomposed into two separate components, i.e. PCA and CCA, which can be used to better remove noises and tackle the issue of trivial learning existing in CCA or traditional Kernel CCA.
- Discrete Multimodal Hashing With Canonical Views for Robust Mobile Landmark Search128
A new augmented Lagrangian multiplier (ALM) based optimization method to directly solve the discrete binary codes and can desirably avoid accumulated quantization errors in conventional optimization method which simply adopts two-step ``relaxing+rounding'' framework.
- Try This Instead127
This paper proposes attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives, and proves the recommendation effectiveness and interpretation quality of A2CF.
- GAG122
A GAG neural network model with a Wasserstein reservoir is proposed to help preserve a representative sketch of the historical data and verify the superiority of the GAG model compared with the state-of-the-art methods.
- Sparse hashing for fast multimedia search122
Experimental results show that the proposed sparse hashing significantly outperforms state-of-the-art techniques and 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.
- Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation118
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, and comprehensively uncovers multi-level sequential patterns for user preference modelling.
- Graph Embedding for Recommendation against Attribute Inference Attacks118
The proposed GERAI binds the information perturbation mechanism in differential privacy with the recommendation capability of graph convolutional networks and innovatively devise a dual-stage encryption paradigm to simultaneously enforce privacy guarantee on users’ sensitive features and the model optimization process.
- Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices117
A novel Light Location Recommender System (LLRec) to perform next POI recommendation locally on resource-constrained mobile devices, which significantly reduces the dependency on cloud servers, thus allowing for nextPOI recommendation in a stable, cost-effective and secure way.
- Enhancing Collaborative Filtering with Generative Augmentation115
AugCF is a generic and effective CF model called AugCF that supports a wide variety of recommendation tasks and is based on Conditional Generative Adversarial Nets that additionally consider the class as a feature to generate new interaction data, which can be a sufficiently real augmentation to the original dataset.
- Robust discrete code modeling for supervised hashing114
This work proposes a novel supervised hashing approach, termed as Robust Discrete Code Modeling (RDCM), which directly learns high-quality discretebinary codes and hash functions by effectively suppressing the influence of unreliable binary codes and potentially noisily-labeled samples.
- Streaming Ranking Based Recommender Systems112
This paper provides a principled framework called SPMF (Stream-centered Probabilistic Matrix Factorization model), based on BPR (Bayesian Personalized Ranking) optimization framework, for performing efficient ranking based recommendations in stream settings.
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- SK-LSH110
An 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.
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- UQLIPS: a real-time near-duplicate video clip detection system98
Using a large collection of TV commercials, UQLIPS clearly demonstrates that it is feasible to perform real-time NDVC detection with high accuracy.
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- Discrete Nonnegative Spectral Clustering91
A novel spectral clustering scheme is proposed which deeply explores cluster label properties, including discreteness, nonnegativity, and discrimination, as well as learns robust out-of-sample prediction functions and preserves the natural nonnegative characteristic of the clustering labels.
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- CaT: Balanced Continual Graph Learning with Graph Condensation86
A Condense and Train (CaT) framework is proposed in this paper and extensive experiments conducted on four benchmark datasets successfully demonstrate superior performances of the proposed CaT framework in terms of effectiveness and efficiency.
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- In Search of Lost Online Test-Time Adaptation: A Survey82
These findings diverge from existing literature, revealing that transformers demonstrate heightened resilience to diverse domain shifts, and the efficacy of many OTTA methods relies on large batch sizes, and stability in optimization and resistance to perturbations are crucial during adaptation, particularly when the batch size is 1.
- Robust Hashing With Local Models for Approximate Similarity Search81
This paper proposes a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional data points into binary hash codes by effectively utilizing local structural information.
- Discrete Deep Learning for Fast Content-Aware Recommendation80
This paper proposes a hashing based deep learning framework called Discrete Deep Learning (DDL), to map users and items to Hamming space, where a user»s preference for an item can be efficiently calculated by Hamming distance, and this computation scheme significantly improves the efficiency of online recommendation.
- High-order nonlocal Hashing for unsupervised cross-modal retrieval79
A new deep hashing model, termed as High-order Nonlocal Hashing (HNH) to facilitate cross-modal retrieval with the following advantages: a high-order affinity measure that considers the multi- modal neighbourhood structures from a nonlocal perspective, thereby comprehensively capturing the similarity relationships between data items.
- Joint Modeling of Users' Interests and Mobility Patterns for Point-of-Interest Recommendation79
A unified probabilistic generative model, Topic-Region Model (TRM), is proposed, 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 POIs.
- Semi-decentralized Federated Ego Graph Learning for Recommendation78
A semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner.
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- Alleviating Feature Confusion for Generative Zero-shot Learning77
A new feature generating network, named alleviating feature confusion GAN (AFC-GAN), is proposed, which maximizes the decision boundary of seen categories and unseen ones and is able to outperform previous state-of-the-arts under both ZSL and GZSL protocols.
- Domain Adaptive Semantic Segmentation without Source Data76
This work proposes an effective framework for domain adaptive semantic segmentation without source data, which assumes that the model is pre-trained on the source domain, and then adapting to the target domain without accessing source data anymore.
- Cycle-consistent Conditional Adversarial Transfer Networks75
This paper proposes a novel approach named cycle-consistent conditional adversarial transfer networks (3CATN), which takes care of the domain alignment by leveraging adversarial training and condition the adversarial networks with the cross-covariance of learned features and classifier predictions to capture the multimodal structures of data distributions.
- Bounded coordinate system indexing for real-time video clip search75
This work proposes 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 and demonstrates that BCS achieves very high search accuracy according to human judgment.
- Long short-term enhanced memory for sequential recommendation71
A novel architecture based on Long Short-Term Memories (LSTMs), a broadly-used variant of RNNs, specific for sequential recommendation, is proposed, which boosts the memory mechanism of original LSTMs in two ways, including a new structure of gates and a “recover gate” to remedy the inadequacy of memory caused by the forgetting mechanism.
- Discovering areas of interest with geo-tagged images and check-ins71
A density-based clustering method to discover AoI, mainly based on the image densities but also reinforced by the secondary densities from the images' neighboring venues, and a novel joint authority analysis framework to rank AoI.
- Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation69
A novel sequential recommendation framework to overcome challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec, which can outperform the state-of-the-art baselines.
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- Incomplete Cross-modal Retrieval with Dual-Aligned Variational Autoencoders64
A Dual-Aligned Variational Autoencoders (DAVAE) is proposed to learn modality-invariant representations for different modalities and use the learned representations for retrieval to address the incomplete CMR problem.
- Evaluation of machine learning interpolation techniques for prediction of physical properties64
It is found that the scalable Gaussian process approach was able to maintain the high accuracy of the predictions while improving speed considerably, make on-line learning possible.
- Deep-Sea Organisms Tracking Using Dehazing and Deep Learning63
This paper develops a method for underwater real-time recognition and tracking of multi-objects, which is called “You Only Look Once: YOLO”, and provides a very fast and accurate tracker.
- Learning Elastic Embeddings for Customizing On-Device Recommenders60
This paper presents a novel lightweight recommendation paradigm that allows a well-trained recommender to be customized for arbitrary device-specific memory constraints without retraining, and proposes an innovative approach, namely recommendation with universally learned elastic embeddings (RULE).
- Two Birds One Stone58
This paper proposes a novel approach which can simultaneously handle both cold-start and long-tail recommendation in a unified objective and effectively formulate the two problems into a unified objectives and presents an iterative optimization algorithm.
- What are Popular58
A novel topic model which jointly models five Twitter features (text, image, location, timestamp and hashtag) is designed to discover events from the sheer amount of tweets and each event is visualized by representative images selected on three predefined criteria.
- BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge57
This work proposes a two-stage bootstrapping audio-visual segmentation framework by incorporating multi-modal foundation knowledge and develops an audio-visual semantic integration strategy (AVIS) to localize the authentic-sounding objects.
- CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language57
A novel method named Cycle-consistent Adversarial Networks for Zero-Shot Learning (CANZSL), which encourages a visual feature generator to synthesize realistic visual features from semantics, and then inversely translate back the synthesized visual features to the corresponding semantic space by a semantic feature generator.
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- Source-Free Progressive Graph Learning for Open-Set Domain Adaptation54
The Progressive Graph Learning (PGL) framework is proposed, which guarantees a tight upper bound of the target error by integrating a graph neural network with episodic training and leveraging adversarial learning to close the gap between the source and target distributions.
- CRSAL54
In CRSAL, a fully statistical dialogue state tracker coupled with a neural policy agent is designed to precisely capture each user’s intent from limited dialogue data and generate conversational recommendation actions and develops an adversarial Actor-Critic reinforcement learning approach to adaptively refine the quality of generated system actions, thus ensuring coherent human-like dialogue responses.
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- Zero-Shot Learning by Harnessing Adversarial Samples53
HAS advances ZSL through adversarial training which takes into account three crucial aspects: robust generation by enforcing augmentations to be similar to negative classes, while maintaining correct labels, reliable generation by introducing a latent space constraint to avert significant deviations from the original data manifold, and diverse generation by incorporating attribute-based perturbation by adjusting images according to each semantic attribute's localization.
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- Modal-adversarial Semantic Learning Network for Extendable Cross-modal Retrieval52
A novel framework, termed Modal-adversarial Semantic Learning Network (MASLN), is proposed, to tackle the limitation of existing methods on this practical task of extendable cross-modal retrieval task where instances in source and target set have disjoint classes.
- Dissimilarity measures for content-based image retrieval52
16 core dissimilarity measures are introduced and evaluated, and a systematic performance comparison on three image collections, Corel, Getty and Trecvid2003, with 7 different feature spaces is carried out.
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- Scrutinizing Privacy Policy Compliance of Virtual Personal Assistant Apps48
Kipper is developed, which targets Amazon Alexa skills and automatically depicts the skill into the declared privacy profile by analyzing their privacy policy documents with Natural Language Processing (NLP) and machine learning techniques, and derives the behavioral privacy profile of the skill through a black-box testing.
- Imbalanced Source-free Domain Adaptation48
Imbalanced Source-free Domain Adaptation is presented, which first train a uniformed model from the source domain, and then proposes secondary label correction, curriculum sampling, plus intra-class tightening and inter-class separation to overcome the joint presence of covariate shift and label shift.
- Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches48
A novel framework called multi-patch generative adversarial nets (MPGAN) is proposed that synthesises local patch features and labels unseen classes with a novel weighted voting strategy and has significantly greater accuracy than state-of-the-art methods.
- CRA-Net48
This work proposes a novel end-to-end VQA model, termed Composed Relation Attention Network (CRA-Net), which devise two question-adaptive relation attention modules that can extract not only the fine-grained and precise binary relations but also the more sophisticated trinary relations.
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- Mining multi-tag association for image tagging46
This paper proposes a novel automatic image tagging method aiming to automatically discover more complete tags associated with information importance for test images by considering the implicit multi-tag associations mined from all the clusters’ documents.
- Exploring Consistent Preferences45
This paper proposes a novel discrete hashing with pair-exemplar (DHPE) to support scalable and efficient large-scale CBVLS and designs a novel optimization method based on augmented Lagrange multiplier to explicitly deal with the discrete constraint, the bit-uncorrelated constraint and balance constraint.
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- Explicit Knowledge Graph Reasoning for Conversational Recommendation42
A new CRS framework, namely, the Knowledge Enhanced Conversational Reasoning (KECR) model, is proposed, which obtains state-of-the-art recommendation effectiveness, as well as competitive dialogue generation performance.
- Exploiting Positional Information for Session-Based Recommendation42
A novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module is proposed to fully exploit the positional information for session-based recommendation tasks.
- Mitigating Generation Shifts for Generalized Zero-Shot Learning41
A novel generative flow framework that consists of multiple conditional affine coupling layers for learning unseen data generation and a relative positioning strategy is proposed to revise the attribute embeddings, guiding them to fully preserve the inter-class geometric structure and further avoid structure disorder in the semantic space.
- Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation41
A novel Curiosity-driven Reinforcement Learning (CRL) framework is proposed to jointly enhance the diversity and accuracy of the generated paragraphs, and discounted imitation learning is integrated for learning from human demonstrations, without separately performing the time-consuming warm-up in advance.
- From community detection to community profiling41
This paper identifies three unique challenges of community profiling and proposes a joint Community Profiling and Detection (CPD) model, which is significantly better than the state-of-the-art baselines in various tasks and also contributes a scalable inference algorithm.
- Local image tagging via graph regularized joint group sparsity41
This work proposes a Laplacian Joint Group Lasso model to jointly reconstruct the regions within a test image with a set of labeled training data and extends the LJGL model to a kernel version in order to achieve the non-linear reconstruction.
- Adaptively Attending to Visual Attributes and Linguistic Knowledge for Captioning40
This work designs a key control unit, termed visual gate, to adaptively decide "when" and "what" the language generator attend to during the word generation process, and employs a bottom-up workflow to learn a pool of semantic attributes for serving as the propositional attention resources.
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- Proactive Privacy-preserving Learning for Cross-modal Retrieval37
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.
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- Transductive Visual-Semantic Embedding for Zero-shot Learning17
This work proposes a novel framework termed transductive visual-semantic embedding (TVSE) for ZSL that achieves competitive performance compared with the state-of-the-arts for zero-shot recognition and retrieval tasks.
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- Navigating Beyond Instructions: Vision-and-Language Navigation in Obstructed Environments14
A novel method called ObVLN (Obstructed VLN), which includes a curriculum training strategy and virtual graph construction to help agents effectively adapt to obstructed environments, is proposed, which shows that ObVLN not only maintains robust performance in unobstructed scenarios but also achieves a substantial performance advantage with unexpected obstructions.
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- Index and Retrieve Multimedia Data: Cross-Modal Hashing by Learning Subspace Relation14
A novel supervised cross-modal hashing method dubbed Subspace Relation Learning for Cross-modAL Hashing (SRLCH), which exploits relation information in semantic labels to make similar data from different modalities closer in the low-dimension Hamming subspace is proposed.
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- Event Video Mashup: From Hundreds of Videos to Minutes of Skeleton9
A novel approach to automatically generate a unified short video from a collection of Web videos to describe the storyline of an event, using a submodular based content selection model that embodies both importance and diversity to depict the event from comprehensive aspects in an efficient way.
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- Editorial for Special Issue on Large-scale Pre-training: Data, Models, and Fine-tuning4
This special issue seeks original and novel contributions toward advancing the theory, architecture, and algorithmic design for large-scale pre-training in machine intelligence as well as downstream applications.
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- Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling–
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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