106 citations
- Meta (United States)US2 papers
- Shenzhen Institutes of Advanced TechnologyCN2 papers
- University of Maryland, College ParkUS2 papers
- Aalto UniversityFI1 paper
- China University of GeosciencesCN1 paper
- Chinese Academy of SciencesCN1 paper
- Chinese University of Hong Kong, ShenzhenCN1 paper
- Commonwealth Scientific and Industrial Research OrganisationAU1 paper
- Data61AU1 paper
- GE Global Research (United States)US1 paper
- General Electric (United States)US1 paper
- Huazhong University of Science and TechnologyCN1 paper
16 papers
Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities
Renjie Li, Yuanhao Gong, Hai Huang +4
In the dynamic landscape of Artificial Intelligence (AI), two notable phenomena are becoming predominant: the exponential growth of large AI model sizes and the explosion of massiv…
Research on AI Composition Recognition Based on Music Rules
Yang Deng, Ziyao Xu, Li Zhou +2
The development of artificial intelligent composition has resulted in the increasing popularity of machine-generated pieces, with frequent copyright disputes consequently emerging.…
Wavelet Integrated CNNs for Noise-Robust Image Classification
Qiufu Li, Linlin Shen, Sheng Guo +1
Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to th…
Deformable Siamese Attention Networks for Visual Object Tracking
Yuechen Yu, Yilei Xiong, Weilin Huang +1
Siamese-based trackers have achieved excellent performance on visual object tracking. However, the target template is not updated online, and the features of the target template an…
Channel Interaction Networks for Fine-Grained Image Categorization
Yu Gao, Xintong Han, Xun Wang +2
Fine-grained image categorization is challenging due to the subtle inter-class differences.We posit that exploiting the rich relationships between channels can help capture such di…
iFAN: Image-Instance Full Alignment Networks for Adaptive Object Detection
Chenfan Zhuang, Xintong Han, Weilin Huang +1
Training an object detector on a data-rich domain and applying it to a data-poor one with limited performance drop is highly attractive in industry, because it saves huge annotatio…