no code implementations • 6 Oct 2024 • Qichao Ma, Rui-Jie Zhu, Peiye Liu, Renye Yan, Fahong Zhang, Ling Liang, Meng Li, Zhaofei Yu, Zongwei Wang, Yimao Cai, Tiejun Huang
However, the gap between them exists, where direct assessments of how dataset contributions impact LLM outputs are missing.
no code implementations • 29 May 2024 • Shusong Xu, Peiye Liu
Artificial Intelligence Generated Content(AIGC), known for its superior visual results, represents a promising mitigation method for high-cost advertising applications.
no code implementations • 17 May 2024 • Bo Wu, Peiye Liu, Wen-Huang Cheng, Bei Liu, Zhaoyang Zeng, Jia Wang, Qiushi Huang, Jiebo Luo
The research progress analysis provides an overall analysis of the solutions and trends in recent years.
1 code implementation • CVPR 2024 • Ruoxi Zhu, Shusong Xu, Peiye Liu, Sicheng Li, Yanheng Lu, Dimin Niu, Zihao Liu, Zihao Meng, Zhiyong Li, Xinhua Chen, Yibo Fan
However obtaining paired HDR and high-quality LDR images is difficult posing a challenge to deep learning based tone mapping methods.
1 code implementation • CVPR 2020 • Peiye Liu, Bo Wu, Huadong Ma, Mingoo Seok
Recent studies on automatic neural architecture search techniques have demonstrated significant performance, competitive to or even better than hand-crafted neural architectures.
Ranked #121 on Neural Architecture Search on ImageNet
no code implementations • 4 Oct 2019 • Bo Wu, Wen-Huang Cheng, Peiye Liu, Bei Liu, Zhaoyang Zeng, Jiebo Luo
In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction task Temporal Popularity Prediction, which focuses on predicting future interaction or attractiveness (in terms of clicks, views or likes etc.)
no code implementations • 22 Jul 2019 • Peiye Liu, Bo Wu, Huadong Ma, Mingoo Seok
Recent studies on automatic neural architectures search have demonstrated significant performance, competitive to or even better than hand-crafted neural architectures.
no code implementations • 18 Oct 2018 • Peiye Liu, Wu Liu, Huadong Ma, Tao Mei, Mingoo Seok
To transfer the knowledge of intermediate representations, we set high-level teacher feature maps as a target, toward which the student feature maps are trained.