Multimodal Object Detection

Multimodal Object Detection is a computer vision task that integrates complementary information from multiple sensory modalities, such as visible light and infrared imagery or RGB and depth data, to localize and classify objects within a scene, thereby improving detection accuracy and robustness in complex environments.

171

Publications

10.4K

Citations

536

Authors

206

Institutions

Publications per year

2017–2026

142

Authors

536

  1. CC
    Chen Change Loy

    Nanyang Technological University, Singapore

    3 publications · 126 citations

    3

    h-index

  2. MN
    Mikio Nakano

    Honda (Japan), Japan

    3 publications · 83 citations

    3

    h-index

  3. ZT
    Zhengzheng Tu

    Anhui University, China

    1 publications · 17 citations

    2

    h-index

  4. HZ
    Hanqi Zhu

    The University of Texas at Dallas, United States

    2 publications · 180 citations

    2

    h-index

  5. QM
    Qiuyu Mao

    University of Science and Technology of China, China

    2 publications · 180 citations

    2

    h-index

1–5 of 536

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 270 citations

    9

    h-index

  2. Remote Sensing

    4 publications · 165 citations

    7

    h-index

  3. 6

    h-index

  4. 5

    h-index

  5. Sensors

    3 publications · 96 citations

    4

    h-index

1–5