Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Suggested from co-authorship
- Tat‐Seng ChuaSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works553 from public data
- Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition488
TASN consists of a trilinear attention module, which generates attention maps by modeling the inter-channel relationships, an attention-based sampler which highlights attended parts with high resolution, and a feature distiller, which distills part features into an object-level feature by weight sharing and feature preserving strategies.
- Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search465
An approach that simultaneously utilizes both visual and textual information to estimate the relevance of user tagged images is proposed, and the relevance estimation is determined with a hypergraph learning approach.
- 405
- Learning to Assemble Neural Module Tree Networks for Visual Grounding327
A novel modular network called Neural Module Tree network (NMTree) is developed that regularizes the visual grounding along the dependency parsing tree of the sentence, where each node is a neural module that calculates visual attention according to its linguistic feature, and the grounding score is accumulated in a bottom-up direction where as needed.
- Object Relational Graph With Teacher-Recommended Learning for Video Captioning325
This paper proposes an object relational graph (ORG) based encoder, which captures more detailed interaction features to enrich visual representation and designs a teacher-recommended learning method to make full use of the successful external language model (ELM) to integrate the abundant linguistic knowledge into the caption model.
- 305
- Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews287
This paper develops an aspect ranking algorithm, which aims to automatically identify important product aspects from online consumer reviews by simultaneously considering the aspect frequency and the influence of consumers' opinions given to each aspect on their overall opinions.
- Towards a new generation of artificial intelligence in China285
This Perspective briefly introduces the New Generation Artificial Intelligence Development Plan of China (2015–2030) from the point of view of the authors, a group of AI experts from academia and industry who have been involved in various stages of the plan.
- 281
- ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection257
The ContourNet is proposed, which effectively handles the problems taking a further step toward accurate arbitrary-shaped text detection and suppresses false positives by only outputting predictions with high response value in both orthogonal directions.
- 248
- 248
- Disentangle Your Dense Object Detector234
This paper investigates three important conjunctions of dense detectors and proposes Disentangled Dense Object Detector (DDOD), in which simple and effective disentanglement mechanisms are designed and integrated into the current state-of-the-art dense object detectors.
- Parsing-Based View-Aware Embedding Network for Vehicle Re-Identification229
This paper proposes a parsing-based view-aware embedding network (PVEN), which helps capture the stable discriminative information of vehicle under different views and design a common-visible attention to focus on the common visible views, which not only shortens the distance among intra-instances, but also enlarges the discrepancy of inter-instance.
- Mining Travel Patterns from Geotagged Photos229
This study aims to leverage the wealth of these enriched online photos to analyze people’s travel patterns at the local level of a tour destination by building a statistically reliable database of travel paths from a noisy pool of community-contributed geotagged photos on the Internet.
- Group-aware Label Transfer for Domain Adaptive Person Re-identification217
A group-aware Label Transfer (GLT) algorithm is proposed, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning and can better correct the noisy pseudo label in an online fashion and narrow down the search space of the target identity.
- Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning212
A novel self-sustaining representation expansion scheme that fuses main-br branch expansion and side-branch updating to maintain the old features and transfer the invariant knowledge to the new classes is proposed.
- 203
- Visual query suggestion198
This paper proposes a new query suggestion scheme named Visual Query Suggestion (VQS), which provides a more effective query interface to formulate an intent-specific query by joint text and image suggestions, and shows that VQS outperforms these engines in terms of both the quality of query suggestion and search performance.
- Multi-Scale Triplet CNN for Person Re-Identification197
A multi-scale triplet convolutional neural network which captures visual appearance of a person at various scales is proposed, addressing the problem of small training set in person re-identification.
- Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization196
A novel “Filtration and Distillation Learning” (FDL) model is proposed to enhance the region attention of discriminate parts for FGVC that utilizes the proposing-predicting matchability as the performance metric of Region Proposal Network (RPN), thus enable a direct optimization of RPN to filtrate most discriminative regions.
- Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification194
This paper proposes to estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels, by suppressing the contribution of noisy samples, and introduces uncertainty-guided optimization, which brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
- Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning193
This work proposes a novel incremental prototype learning scheme which consists of a random episode selection strategy that adapts the feature representation to various generated incremental episodes to enhance the corresponding extensibility, and a self-promoted prototype refinement mechanism which strengthens the expression ability of the new classes by explicitly considering the dependencies among different classes.
- 183
- Domain-Aware Visual Bias Eliminating for Generalized Zero-Shot Learning181
A novel Domain-aware Visual Bias Eliminating (DVBE) network is proposed that constructs two complementary visual representations, i.e., semantic-free and semantic-aligned, to treat seen and unseen domains separately and outperforms existing methods by averaged 5.7% improvement.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.