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

Muhammad Haris Khan

CVPR 2026×6ACL 2026×3ICLR 2026×3AAAI 2026ICRA 2026IROS 2026多模态推理评测与基准理论与学习动力学社会与人本
新星分资深
43
一作贡献0
核心/参与38
方向广度19
合作网络12
0 一作 · 15 篇 · CVPR 2026 / ACL 2026 / ICLR 2026 / AAAI 2026 / ICRA 2026 / IROS 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

15
作者位次 #4/4CVPR 2026Poster· Computer Vision & Pattern Recognition

StyleDoctor: Towards Specialist Reward Model for Style-centric Generation Tasks

Xilin He · Xiaole Xian · Xiangyu Yue · Muhammad Haris Khan

对齐与偏好多模态OpenReview 项目页
作者位次 #3/4CVPR 2026Poster· Computer Vision & Pattern Recognition

Pixels Don't Lie (But Your Detector Might): Bootstrapping MLLM-as-a-Judge for Trustworthy Deepfake Detection and Reasoning Supervision

Kartik Kuckreja · Parul Gupta · Muhammad Haris Khan · Abhinav Dhall

推理多模态评测与基准OpenReview 项目页
作者位次 #5/5ACL 2026Poster· Natural Language Processing

GCA Framework: A GCC Countries–Grounded Dataset and Agentic Pipeline for Climate Decision Support

Muhammad Umer Sheikh · Khawar shehzad · Salman Khan · Fahad Shahbaz Khan · Muhammad Haris Khan

摘要

Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geo...

多模态智能体与工具可解释性OpenReview 项目页
作者位次 #4/6ACL 2026Findings· Findings of ACL

B io VLM : Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLM s

Mainak Singha · Tanisha Gupta · Ankit Jha · Muhammad Haris Khan · Sayantani Ghosh · Biplab Banerjee

摘要

Pretrained biomedical vision–language models (VLMs) such as BioMedCLIP perform well on average but often degrade on challenging modalities where inter-class margins are small and acquisition-specific variations are pronounced, especially under few-shot supervision and when modality priors differ...

多模态强化学习与后训练效率与系统OpenReview 项目页

常见合作者