Search Results for author: Ge Shi

Found 9 papers, 0 papers with code

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts

no code implementations23 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.

Knowledge Guided Entity-aware Video Captioning and A Basketball Benchmark

no code implementations25 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.

Video Captioning

Rethink Baseline of Integrated Gradients from the Perspective of Shapley Value

no code implementations7 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.

Boosting Event Extraction with Denoised Structure-to-Text Augmentation

no code implementations16 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.

Event Extraction Text Augmentation +1

Knowledge Augmented Relation Inference for Group Activity Recognition

no code implementations28 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.

Group Activity Recognition Relation

Learning to Compose Diversified Prompts for Image Emotion Classification

no code implementations26 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.

Classification Emotion Classification +2

Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction

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.

Feature Engineering Relation +2

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