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- Chinese Academy of SciencesCN17 papers
- Tsinghua UniversityCN17 papers
- Ministry of Industry and Information TechnologyCN16 papers
- Xi'an Jiaotong UniversityCN16 papers
- Xidian UniversityCN16 papers
- Australian National UniversityAU11 papers
- Moscow Institute of Physics and TechnologyRU11 papers
- University of Chinese Academy of SciencesCN10 papers
- Beijing Normal UniversityCN9 papers
- Nankai UniversityCN9 papers
- Centre National de la Recherche ScientifiqueFR8 papers
- Fudan UniversityCN8 papers
5 papers · 2 filters
Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey
Zhongling Huang, Xidan Zhang, Zuqian Tang +3
SAR images possess unique attributes that present challenges for both human observers and vision AI models to interpret, owing to their electromagnetic characteristics. The interpr…
Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning
Ruiqi Wu, Bingliang Jiao, Wenxuan Wang +2
The Visible-Infrared Person Re-identification (VI ReID) aims to match visible and infrared images of the same pedestrians across non-overlapped camera views. These two input modali…
Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
Qiangguo Jin, Hui Cui, Changming Sun +5
Acquiring pixel-level annotations is often limited in applications such as histology studies that require domain expertise. Various semi-supervised learning approaches have been de…
Vehicle Perception from Satellite
Bin Zhao, Pengfei Han, Xuelong Li
Satellites are capable of capturing high-resolution videos. It makes vehicle perception from satellite become possible. Compared to street surveillance, drive recorder or other equ…
NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images
Junyu Gao, Liangliang Zhao, Xuelong Li
Object counting is a hot topic in computer vision, which aims to estimate the number of objects in a given image. However, most methods only count objects of a single category for…