Haikuan Huang — accepted AI-conference papers

2026

REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment

Kai Ye, Xianwei Mao, Sheng Zhou, Zirui Shao, Ye Mo, Liangliang Liu +3

ICML 2026poster

Knowledge-intensive Visual Question Answering (KI-VQA) frequently suffers from severe knowledge conflicts caused by the inherent limitations of open-domain retrieval. However, existing paradigms face critical limitations, including the lack of generalizable conflict detection and intra-model constra…

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2021

Deep Wasserstein Graph Discriminant Learning for Graph Classification

Tong Zhang, Yun Wang, Zhen Cui, Chuanwei Zhou, Baoliang Cui, Haikuan Huang +1

AAAI 2021technical

Graph topological structures are crucial to distinguish different-class graphs. In this work, we propose a deep Wasserstein graph discriminant learning (WGDL) framework to learn discriminative embeddings of graphs in Wasserstein-metric (W-metric) matching space. In order to bypass the calculation of…

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