no code implementations • 23 Mar 2024 • Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, JinKun Lin, Yangguang Mei, Lingnan Xu
In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
no code implementations • 14 Feb 2024 • Ge Shi, Zhili Yang
Then we render the output of optical flow net to a fully convolutional SegNet model.
no code implementations • 25 Jan 2024 • Zeyu Xi, Ge Shi, Xuefen Li, Junchi Yan, Zun Li, Lifang Wu, Zilin Liu, Liang Wang
We develop a knowledge guided entity-aware video captioning network (KEANet) based on a candidate player list in encoder-decoder form for basketball live text broadcast.
no code implementations • 7 Oct 2023 • Shuyang Liu, Zixuan Chen, Ge Shi, Ji Wang, Changjie Fan, Yu Xiong, Runze Wu Yujing Hu, Ze Ji, Yang Gao
Thus, we propose a novel baseline construction method called Shapley Integrated Gradients (SIG) that searches for a set of baselines by proportional sampling to partly simulate the computation path of Shapley Value.
no code implementations • 16 May 2023 • Bo wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin
Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations.
no code implementations • 28 Feb 2023 • Xianglong Lang, Zhuming Wang, Zun Li, Meng Tian, Ge Shi, Lifang Wu, Liang Wang
Specifically, the framework consists of a Visual Representation Module to extract individual appearance features, a Knowledge Augmented Semantic Relation Module explore semantic representations of individual actions, and a Knowledge-Semantic-Visual Interaction Module aims to integrate visual and semantic information by the knowledge.
no code implementations • ACL 2022 • Xiao Liu, Heyan Huang, Ge Shi, Bo wang
We consider event extraction in a generative manner with template-based conditional generation.
no code implementations • 26 Jan 2022 • Sinuo Deng, Lifang Wu, Ge Shi, Lehao Xing, Meng Jian, Ye Xiang
We first introduce a prompt tuning method that mimics the pretraining objective of CLIP and thus can leverage the rich image and text semantics entailed in CLIP.
no code implementations • EMNLP 2018 • Ge Shi, Chong Feng, Lifu Huang, Boliang Zhang, Heng Ji, Lejian Liao, He-Yan Huang
Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions.