Search Results for author: Junyi Cao

Found 4 papers, 3 papers with code

Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous Driving

1 code implementation • 9 Mar 2024 • Junyi Cao, Zhichao Li, Naiyan Wang, Chao Ma

Recent studies have highlighted the promising application of NeRF in autonomous driving contexts.

Paper
Code

Artificial Intelligence Security Competition (AISC)

no code implementations • 7 Dec 2022 • Yinpeng Dong, Peng Chen, Senyou Deng, Lianji L, Yi Sun, Hanyu Zhao, Jiaxing Li, Yunteng Tan, Xinyu Liu, Yangyi Dong, Enhui Xu, Jincai Xu, Shu Xu, Xuelin Fu, Changfeng Sun, Haoliang Han, Xuchong Zhang, Shen Chen, Zhimin Sun, Junyi Cao, Taiping Yao, Shouhong Ding, Yu Wu, Jian Lin, Tianpeng Wu, Ye Wang, Yu Fu, Lin Feng, Kangkang Gao, Zeyu Liu, Yuanzhe Pang, Chengqi Duan, Huipeng Zhou, Yajie Wang, Yuhang Zhao, Shangbo Wu, Haoran Lyu, Zhiyu Lin, YiFei Gao, Shuang Li, Haonan Wang, Jitao Sang, Chen Ma, Junhao Zheng, Yijia Li, Chao Shen, Chenhao Lin, Zhichao Cui, Guoshuai Liu, Huafeng Shi, Kun Hu, Mengxin Zhang

The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.

Autonomous Driving Face Recognition +1

Paper
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End-to-End Reconstruction-Classification Learning for Face Forgery Detection

1 code implementation • CVPR 2022 • Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, Xiaokang Yang

Reconstruction learning over real images enhances the learned representations to be aware of forgery patterns that are even unknown, while classification learning takes the charge of mining the essential discrepancy between real and fake images, facilitating the understanding of forgeries.

Classification

Paper
Code

Co-attention network with label embedding for text classification

1 code implementation • Neurocomputing 2021 • Minqian Liu, Lizhao Liu, Junyi Cao, Qing Du

To alleviate this issue, label embedding frameworks are proposed to adopt the label-to-text attention that directly uses label information to construct the text representation for more efficient text classification.

Ranked #2 on Multi-Label Text Classification on AAPD (Micro F1 metric)

Multi-class Classification Multi-Label Classification +3

Paper
Code

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