Xingliang Mao — accepted AI-conference papers

2025

TIETracker: A CLIP-based RGB-T Tracking via Feature Interaction and Semantic Enhancement

Weidai Xia, Xingliang Mao, Wei Wu, Chengzhang Zhu, Fangfang Li

IROS 2025

The goal of RGB-T tracking is to enhance the accuracy and robustness by leveraging the complementary features of RGB and TIR modalities in complex scenarios. Previous methods have overlooked the power of semantic features in extracting valuable information from different modalities and improving int

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2022

WSpeller: Robust Word Segmentation for Enhancing Chinese Spelling Check

Fangfang Li, Youran Shan, Junwen Duan, Xingliang Mao, Minlie Huang

EMNLP 2022finding

Chinese spelling check (CSC) detects and corrects spelling errors in Chinese texts. Previous approaches have combined character-level phonetic and graphic information, ignoring the importance of segment-level information. According to our pilot study, spelling errors are always associated with incor…

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