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

Junjie Hu

ACL 2026×2CVPR 2026多模态对齐与偏好效率与系统可解释性代码
新星分贡献者
53
一作贡献0
核心/参与17
方向广度13
合作网络12
0 一作 · 3 篇 · 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

3
作者位次 #8/8ACL 2026Findings· Findings of ACL

MENTOR : Efficient Autoregressive Image Generation with Balanced Multimodal Control

Haozhe Zhao · Zefan Cai · Shuzheng Si · Liang Chen · Jiuxiang Gu · Wen Xiao · Minjia Zhang · Junjie Hu

摘要

Recent text-to-image models achieve impressive visual quality but still face challenges in precise controllability, balancing multimodal inputs, and high training cost for multimodal image generation. To address these limitations, we propose MENTOR, an autoregressive (AR) framework with a two-sta...

多模态对齐与偏好效率与系统OpenReview 项目页
作者位次 #2/3ACL 2026Findings· Findings of ACL

From Heads to Neurons: Causal Attribution and Steering in Multi-Task Vision–Language Models

Qidong Wang · Junjie Hu · Ming Jiang

摘要

Recent work has increasingly explored neuron-level interpretation in vision-language models (VLMs) to identify neurons critical to final predictions. However, existing neuron analyses generally focus on single tasks, limiting the comparability of neuron importance across tasks. Moreover, ranking...

可解释性多模态代码OpenReview 项目页

常见合作者