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Claim this profileWorks74 from public data
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- Underwater Species Detection using Channel Sharpening Attention93
A real-world underwater object detection dataset (UODD), which covers more than 3K images of the most common aquatic products, and Channel Sharpening Attention Module (CSAM) as a plug-and-play module to further fuse high-level image information, providing the network with the privilege of selecting feature maps.
- Cross-SRN: Structure-Preserving Super-Resolution Network With Cross Convolution83
A hierarchical feature exploitation network to probe and preserve structural information in a multi-scale feature fusion manner, invoking a lightweight structure-preserving network named as Cross-SRN, which outperforms the state-of-the-art methods on the selected benchmark.
- Novel Coplanar Line-Points Invariants for Robust Line Matching Across Views46
A new coplanar line-points invariant upon a newly developed projective invariant, named characteristic number, is constructed and a line matching algorithm using the invariant is proposed, rendering more robust matching across views.
- Learning Pixel-wise Alignment for Unsupervised Image Stitching37
An unsupervised image stitching framework, breaking through the coplanar constraints in homography estimation, yielding accurate pixel-wise alignment under limited overlapping regions, achieves state-of-the-art performance that surpasses both traditional and deep learning approaches by a large margin.
- Hierarchical projective invariant contexts for shape recognition37
A new shape descriptor (HCNC) invariant to projective deformations, compact with a hierarchical strategy, rendering efficient matching and robust to noise, part-missing and articulated deformations.
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- Line matching based on line-points invariant and local homography27
A novel line-points invariant based on a new projective invariant named characteristic number is presented, able to reflect the intrinsic geometry of line and points, which keeps invariant across views.
- Region-based CNN for Logo Detection27
This paper aims to experimentally investigate the appropriate architecture and settings of R-CNN for logo detection, by comparing several popular frameworks ofR-CNN and considering the characteristics of logo objects in images.
- 3D facial landmark localization using texture regression via conformal mapping26
A novel 3D facial landmark localization algorithm that can convert a 3D model to 2D using both geometry and texture information is proposed, which is impervious to pose changes and robust with respect to changes in expression.
- Unseen No More: Unlocking the Potential of CLIP for Generative Zero-shot HOI Detection24
This work presents the first generation-based model using CLIP for zero-shot HOI detection, coined HOIGen, and develops a CLIP-injected feature generator in accordance with the generation of human, object and union features that allows to unlock the potential of CLIP for feature generation instead of feature extraction only.
- Multi-Layer Sparse Representation for Weighted LBP-Patches Based Facial Expression Recognition23
A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works.
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- Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution15
The self-similarity is considered and a hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing and the experimental result shows that HSRNet achieves better quantitative and visual performance than other works and remits the aliasing more effectively.
- Coupling Deep Textural and Shape Features for Sketch Recognition15
This paper explicitly explores the shape properties of sketches, which has almost been neglected before in the context of deep learning, and proposes a sequential dual learning strategy that combines both shape and texture features.
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- A rotation robust shape transformer for cartoon character recognition8
A rotation robust shape transformer for cartoon character recognition, which beats both handcrafted and deep learning methods on the proposed challenging Cartoon dataset and traditional datasets, and develops an online cartoon character recognition application for animation scenarios.
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- FARS: A Fully Automated Research System Deployed at Scale7
FARS (Fully Automated Research System), a fully automated AI-for-AI research system designed to operate across research topics at scale, is presented and reviews indicate that FARS can produce review-worthy and occasionally strong AI/ML research artifacts in a large-scale public deployment.
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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 2026-10-15. 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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