no code implementations • 12 Apr 2024 • Linhuang Wang, Xin Kang, Fei Ding, Satoshi Nakagawa, Fuji Ren
Our approach takes spatial features of different scales extracted by CNN and feeds them into a Multi-scale Embedding Layer (MELayer).
no code implementations • 2 Jan 2024 • Weijin Cheng, Jianzhi Liu, Jiawen Deng, Fuji Ren
Consequently, we propose a simple and safe prompt engineering method (SSP) to improve image generation quality by providing optimal camera descriptions.
1 code implementation • 24 May 2023 • Zheng Hu, Satoshi Nakagawa, Shi-Min Cai, Fuji Ren
To mitigate potential negative transfer, we separate the item representations into market embeddings and item embeddings.
no code implementations • 17 Dec 2022 • Shaopeng Wei, Yu Zhao, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Fuji Ren, Gang Kou
Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.
no code implementations • 14 Dec 2022 • Leyuan Qu, Taihao Li, Cornelius Weber, Theresa Pekarek-Rosin, Fuji Ren, Stefan Wermter
Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • journal 2020 • Omar Ahmed, Fuji Ren, Ammar Hawbani, Yaser Al-Sharabi
The main contribution of this paper is to develop a novel Energy Optimized Congestion Control based on Temperature Aware Routing Algorithm (EOCC-TARA) using Enhanced Multi-objective Spider Monkey Optimization (EMSMO) for SDN-based WBAN.
no code implementations • 13 Jul 2019 • Yu Gu, Xiang Zhang, Zhi Liu, Fuji Ren
The ever evolving informatics technology has gradually bounded human and computer in a compact way.
no code implementations • 29 Nov 2018 • Xiaohua Wang, Muzi Peng, Lijuan Pan, Min Hu, Chunhua Jin, Fuji Ren
In this paper, a two-level attention with two-stage multi-task learning (2Att-2Mt) framework is proposed for facial emotion estimation on only static images.