@inproceedings{duan-etal-2025-brainloc,
    title = "{B}rain{L}oc: Brain Signal-Based Object Detection with Multi-modal Alignment",
    author = "Duan, Jiaqi  and
      Yang, Xiaoda  and
      Luan, Kaixuan  and
      Qiu, Hongshun  and
      Yan, Weicai  and
      Zhang, Xueyi  and
      Zhang, Youliang  and
      Li, Zhaoyang  and
      Huang, Donglin  and
      Lu, JunYu  and
      Jiang, Ziyue  and
      Yang, Xifeng",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1180/",
    doi = "10.18653/v1/2025.findings-emnlp.1180",
    pages = "21652--21662",
    ISBN = "979-8-89176-335-7",
    abstract = "Object detection is a core challenge in computer vision. Traditional methods primarily rely on intermediate modalities such as text, speech, or visual cues to interpret user intent, leading to inefficient and potentially distorted expressions of intent. Brain signals, particularly fMRI signals, emerge as a novel modality that can directly reflect user intent, eliminating ambiguities introduced during modality conversion. However, brain signal-based object detection still faces challenges in accuracy and robustness. To address these challenges, we present BrainLoc, a lightweight object detection model guided by fMRI signals. First, we employ a multi-modal alignment strategy that enhances fMRI signal feature extraction by incorporating various modalities including images and text. Second, we propose a cross-domain fusion module that promotes interaction between fMRI features and category features, improving the representation of category information in fMRI signals. Extensive experiments demonstrate that BrainLoc achieves state-of-the-art performance in brain signal-based object detection tasks, showing significant advantages in both accuracy and convenience."
}