From the 1 of 51 papers with an AI index.
105 citations
- HUN-REN Centre for Energy ResearchHU9 papers
- Potsdam Institute for Climate Impact ResearchDE7 papers
- University of MariborSI7 papers
- Beijing Normal UniversityCN6 papers
- Korea UniversityKR4 papers
- Kyung Hee UniversityKR4 papers
- Chongqing Normal UniversityCN3 papers
- City University of Hong KongHK3 papers
- University of Electronic Science and Technology of ChinaCN3 papers
- Westlake UniversityCN3 papers
- Chongqing UniversityCN2 papers
- Chongqing University of Posts and TelecommunicationsCN2 papers
6 papers · 1 filter
Setup-Independent Full Projector Compensation
Haibo Li, Qingyue Deng, Jijiang Li +2
Projector compensation seeks to correct geometric and photometric distortions that occur when images are projected onto nonplanar or textured surfaces. However, most existing metho…
ProCap: Projection-Aware Captioning for Spatial Augmented Reality
Zimo Cao, Yuchen Deng, Haibin Ling +1
Spatial augmented reality (SAR) directly projects digital content onto physical scenes using projectors, creating immersive experience without head-mounted displays. However, for S…
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…
RFAConv: Receptive-Field Attention Convolution for Improving Convolutional Neural Networks
Xin Zhang, Chen Liu, Degang Yang +4
In the realm of deep learning, spatial attention mechanisms have emerged as a vital method for enhancing the performance of convolutional neural networks. However, these mechanisms…
GS-ProCams: Gaussian Splatting-based Projector-Camera Systems
Qingyue Deng, Jijiang Li, Haibin Ling +1
We present GS-ProCams, the first Gaussian Splatting-based framework for projector-camera systems (ProCams). GS-ProCams is not only view-agnostic but also significantly enhances the…
SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering
Liang Peng, Yixuan Ye, Cheng Liu +5
Multi-view clustering has been empirically shown to improve learning performance by leveraging the inherent complementary information across multiple views of data. However, in rea…