Contextual Object Recognition

Contextual Object Recognition is a context-aware computational method that identifies and interprets objects within an augmented reality environment by integrating spatial, temporal, and semantic cues to disambiguate and enhance object detection based on situational relevance.

79

Publications

7.4K

Citations

234

Authors

124

Institutions

Publications per year

2017–2026

24

Authors

234

  1. CL
    C. Lawrence Zitnick

    Microsoft (United States), United States

    4 publications · 1.5K citations

    4

    h-index

  2. SB
    Serge Belongie

    University of California San Diego, United States

    3 publications · 850 citations

    3

    h-index

  3. AT
    Antonio Torralba

    Massachusetts Institute of Technology, United States

    3 publications · 298 citations

    3

    h-index

  4. QX
    Qian Xie

    Nanjing University of Aeronautics and Astronautics, China

    2 publications · 236 citations

    2

    h-index

  5. YB
    Yalong Bai

    Harbin Institute of Technology, China

    2 publications · 100 citations

    2

    h-index

1–5 of 234

Venues

  1. 7

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 125 citations

    4

    h-index

  3. Frontiers in Psychology

    2 publications · 116 citations

    2

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 77 citations

    2

    h-index

  5. Defense Technical Information Center (DTIC)

    Repository · 1 publications · 10 citations

    1

    h-index

1–5