no code implementations • 1 Oct 2024 • Mingye Zhu, Yi Liu, Quan Wang, Junbo Guo, Zhendong Mao
Recent breakthroughs in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values.
1 code implementation • 22 May 2024 • Mingye Zhu, Yi Liu, Lei Zhang, Junbo Guo, Zhendong Mao
Recently, tremendous strides have been made to align the generation of Large Language Models (LLMs) with human values to mitigate toxic or unhelpful content.
1 code implementation • 20 Oct 2022 • Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo, Yanyan Yang, Yongdong Zhang
In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task.
2 code implementations • ICMR 2021 • Zilong Fu, Guoqing Jin, Hongtao Xie, Junbo Guo
To tackle this issue, in this paper, we propose a dual parallel attention network (DPAN), in which a newly designed parallel context attention module (PCAM) is cascaded with the original PPAM, using linguistic contextual information to compensate for the information inconsistency between queries and keys.
Ranked #13 on
Scene Text Recognition
on ICDAR2013
no code implementations • ACL 2020 • Lei Zhong, Juan Cao, Qiang Sheng, Junbo Guo, Ziang Wang
Identifying controversial posts on social media is a fundamental task for mining public sentiment, assessing the influence of events, and alleviating the polarized views.
no code implementations • 13 Aug 2019 • Peng Qi, Juan Cao, Tianyun Yang, Junbo Guo, Jintao Li
In the real world, fake-news images may have significantly different characteristics from real-news images at both physical and semantic levels, which can be clearly reflected in the frequency and pixel domain, respectively.
no code implementations • 1 Jan 2019 • Jiarong Dong, Ke Gao, Xiaokai Chen, Junbo Guo, Juan Cao, Yongdong Zhang
To address this issue, we propose a novel learning strategy called Information Loss, which focuses on the relationship between the video-specific visual content and corresponding representative words.
2 code implementations • 7 Nov 2018 • Rui Zhang, Sheng Tang, Yu Li, Junbo Guo, Yongdong Zhang, Jintao Li, Shuicheng Yan
The S3-GAN consists of an encoder network, a generator network, and an adversarial network.