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Claim this profileWorks272 from public data
- R³Net: Recurrent Residual Refinement Network for Saliency Detection522
A novel recurrent residual refinement network (R^3Net) equipped with residual refinement blocks (RRBs) to more accurately detect salient regions of an input image that outperforms competitors in all the benchmark datasets.
- 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.
- Maximum Density Divergence for Domain Adaptation342
This paper proposes a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning and proposes a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence.
- A Comprehensive Survey on Source-Free Domain Adaptation308
A comprehensive survey of recent advances in Source-Free Domain Adaptation (SFDA) is provided and several components of each method are modularized to more clearly illustrate their relationships and mechanisms in light of the composite properties of each method.
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- Locality Preserving Joint Transfer for Domain Adaptation274
This work proposes a novel approach to jointly exploit feature adaptation with distribution matching and sample adaptation with landmark selection, suitable for both homogeneous- and heterogeneous-domain adaptations by learning domain-specific projections.
- MMALFM235
A proposed multi-modal aspect-aware topic model (MATM) is applied on text reviews and item images to model users’ preferences and items’ features from different aspects, and also estimates the aspect importance of a user toward an item to alleviate the data sparsity problem and gain good interpretability for recommendation.
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- Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation212
A cross-domain gradient discrepancy minimization (CGDM) method which explicitly minimizes the discrepancy of gradients generated by source samples and target samples so it can be used as a good supervision to improve the accuracy of target samples.
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- DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation162
This work proposes a novel graph-based solution, namely DA-GCN, to address the challenges of shared-account Cross-domain Sequential Recommendation, and devise a domain-aware graph convolution network to learn user-specific node representations.
- 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.
- Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions152
A comprehensive review of cross-modal retrieval’s evolution, spanning from shallow statistical analysis techniques to vision-language pretraining (VLP) models, and delves deeply into the principles and architectures underpinning existing cross-modal retrieval methods.
- Dynamic Multi-View Hashing for Online Image Retrieval130
This paper proposes dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image, and demonstrates superior performance of DWVH over several state-of-the-art hashing methods.
- 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.
- Multi-Behavior Recommendation with Cascading Graph Convolution Networks127
A novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN), in which the embeddings learned from one behavior are used as the input features for the next behavior's embedding learning after a feature transformation operation.
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- Explicit Interaction Model towards Text Classification121
A novel framework, EXplicit interAction Model (dubbed as EXAM), equipped with the interaction mechanism to incorporate word-level matching signals into the text classification task is designed.
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- Online Cross-Modal Hashing for Web Image Retrieval120
This paper proposes Online Cross-modal Hashing (OCMH) which can effectively address the above two problems by learning the shared latent codes (SLC) of hash codes and dynamic transfer matrix, and demonstrates the effectiveness and efficiency of OCMH for online cross- modal web image retrieval.
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 2026-10-14. 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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