paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
GY

Guanghui Ye

AAAI 2026×3ACL 2026×2CVPR 2026多模态评测与基准可解释性推理代码
新星分新星
93
一作贡献110
核心/参与10
方向广度16
合作网络12
3 一作 · 6 篇 · AAAI 2026 / ACL 2026 / CVPR 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

6
作者位次 #6/7ACL 2026Poster· Natural Language Processing

Towards Mitigating Modality Bias in Vision-Language Models for Temporal Action Localization

Jiaqi Li · Guangming Wang · Shuntian Zheng · Minzhe Ni · Xiaoman Lu · Guanghui Ye · Yu Guan

摘要

Temporal Action Localization (TAL) requires identifying both the boundaries and categories of actions in untrimmed videos. While vision-language models (VLMs) offer rich semantics to complement visual evidence, existing approaches tend to overemphasize linguistic priors at the expense of visual p...

多模态推理代码OpenReview 项目页
一作ACL 2026Poster· Natural Language Processing

Automatic and Reliable Evaluation for Academic Caption-to-Figure Generation with LMM s

Guanghui Ye · Huan Zhao · Qin Zhu · Fengnan Li · Jiaqi Li · Yixian Shen · Zhonghao Ren · Zhihua Jiang

摘要

Existing datasets for evaluating text-to-image generation focus mostly on real-life images, which poses challenges for assessing academicfigure generation given real scientific captions, which is a hot topic in AI for Science. To fill the gap, we propose HE4AFG, a novel datasetwhich first provide...

多模态评测与基准数据与预训练OpenReview 项目页

常见合作者