no code implementations • 1 Jul 2019 • Linara Adilova, Livin Natious, Siming Chen, Olivier Thonnard, Michael Kamp
One of the main tasks of cybersecurity is recognizing malicious interactions with an arbitrary system.
1 code implementation • 5 Mar 2021 • Linara Adilova, Siming Chen, Michael Kamp
We propose to approach this challenge through decomposition: by clustering the data we break down the problem, obtaining simpler modeling task in each cluster which can be modeled more accurately.
no code implementations • 24 Apr 2021 • Peng Xie, Wenyuan Tao, Jie Li, Wentao Huang, Siming Chen
The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly-formatted embeddings.
1 code implementation • 27 Mar 2022 • Yu Zhang, Yun Wang, Haidong Zhang, Bin Zhu, Siming Chen, Dongmei Zhang
In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios.
no code implementations • 14 Jul 2022 • Chenxi Ma, Bo Yan, Qing Lin, Weimin Tan, Siming Chen
To enhance the semantic accuracy and the visual quality of the reconstructed image, we explore the multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution (TGSR) framework, which can effectively utilize the information from the text and image modalities.
no code implementations • 18 Mar 2023 • Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang shen, Jie zhou, Siming Chen, Tao Gui, Qi Zhang, Xuanjing Huang
GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities.
no code implementations • 20 Feb 2024 • Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei
The powerful Large Vision Language Models make it possible to handle a variety of tasks simultaneously, but even with carefully designed prompting methods, the general domain models often fall short in aligning with the unique speaking style and context of social media tasks.