346 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Shenzhen UniversityCN44 papers
- Chinese Academy of SciencesCN28 papers
- Southern University of Science and TechnologyCN19 papers
- Peking UniversityCN18 papers
- University of Chinese Academy of SciencesCN14 papers
- Tsinghua UniversityCN11 papers
- City University of Hong KongHK9 papers
- Institute of Applied Physics and Computational MathematicsCN9 papers
- Ping An (China)9 papers
- Shanxi UniversityCN8 papers
- Shenzhen Institutes of Advanced TechnologyCN8 papers
- University of Science and Technology of ChinaCN8 papers
16 papers · 1 filter
A3-FPN: Asymptotic Content-Aware Pyramid Attention Network for Dense Visual Prediction
Meng'en Qin, Yu Song, Quanling Zhao +3
Learning multi-scale representations is the common strategy to tackle object scale variation in dense prediction tasks. Although existing feature pyramid networks have greatly adva…
WeatherRemover: All-in-one Adverse Weather Removal with Multi-scale Feature Map Compression
Weikai Qu, Sijun Liang, Cheng Pan +6
Photographs taken in adverse weather conditions often suffer from blurriness, occlusion, and low brightness due to interference from rain, snow, and fog. These issues can significa…
VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection
Qiang Wang, Xinyuan Gao, Yuhang He +5
Existing Video Detailed Captioning (VDC) methods predominantly rely on costly human annotations or distillation from powerful proprietary models, creating a dependency on external…
Endoscopic Depth Estimation Based on Deep Learning: A Survey
Ke Niu, Zeyun Liu, Xue Feng +5
Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted considerable attention from researchers…
Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection
Hanzhe Liang, Jie Zhang, Tao Dai +3
Reconstruction-based methods have demonstrated very promising results for 3D anomaly detection. However, these methods face great challenges in handling high-precision point clouds…
Unleashing the Potential of All Test Samples: Mean-Shift Guided Test-Time Adaptation
Jizhou Han, Chenhao Ding, SongLin Dong +3
Visual-language models (VLMs) like CLIP exhibit strong generalization but struggle with distribution shifts at test time. Existing training-free test-time adaptation (TTA) methods…
SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding
Xuemei Chen, Huamin Wang, Jing Peng +4
With the wide application of 3D object detection in some fields such as autonomous driving, its energy consumption is constantly increasing, making the research on low-power consum…
OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery
Xiucheng Liang, Jinheng Xie, Tianhong Zhao +2
Building properties, such as height, usage, and material, play a crucial role in spatial data infrastructures, supporting various urban applications. Despite their importance, comp…
Learn by Reasoning: Analogical Weight Generation for Few-Shot Class-Incremental Learning
Jizhou Han, Chenhao Ding, Yuhang He +4
Few-shot class-incremental Learning (FSCIL) enables models to learn new classes from limited data while retaining performance on previously learned classes. Traditional FSCIL metho…
ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science
Koichi Ito, Yihan Zhu, Mahmoud Abdelrahman +9
Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a va…
An Efficient Watermarking Method for Latent Diffusion Models via Low-Rank Adaptation and Dynamic Loss Weighting
Dongdong Lin, Yue Li, Benedetta Tondi +3
The rapid proliferation of Deep Neural Networks (DNNs) is driving a surge in model watermarking technologies, as the trained models themselves constitute valuable intellectual prop…
Test-time generative augmentation for medical image segmentation
Xiao Ma, Yuhui Tao, Zetian Zhang +7
Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusion…
Stylizing Sparse-View 3D Scenes with Hierarchical Neural Representation
Y. Wang, A. Gao, Y. Gong +1
Recently, a surge of 3D style transfer methods has been proposed that leverage the scene reconstruction power of a pre-trained neural radiance field (NeRF). To successfully stylize…
Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A New Benchmark
Jue Wang, Yuxiang Lin, Qi Zhao +4
The widespread use of various chemical gases in industrial processes necessitates effective measures to prevent their leakage during transportation and storage, given their high to…
Anisotropic Neural Representation Learning for High-Quality Neural Rendering
Y. Wang, J. Xu, Y. Zeng +1
Neural radiance fields (NeRFs) have achieved impressive view synthesis results by learning an implicit volumetric representation from multi-view images. To project the implicit rep…
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection
Cong Chen, Shouyang Dong, Ye Tian +3
This paper focuses on Semi-Supervised Object Detection (SSOD). Knowledge Distillation (KD) has been widely used for semi-supervised image classification. However, adapting these me…