From the 1 of 11 papers with an AI index.
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- Shenzhen UniversityCN4 papers
- City University of Hong KongHK3 papers
- Shenzhen MSU-BIT University3 papers
- Aalto UniversityFI1 paper
- Anhui Normal UniversityCN1 paper
- Baidu (China)CN1 paper
- Bohai UniversityCN1 paper
- Central South UniversityCN1 paper
- Chalmers University of TechnologySE1 paper
- Changzhou Institute of TechnologyCN1 paper
- Chinese Academy of SciencesCN1 paper
- Chinese University of Hong KongHK1 paper
5 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…
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…
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…