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Industry and Academic Research in Computer Vision

arXiv 2107.04902 ↗First posted 10 Jul 2021cs.CVcited by 0

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Abstract (arXiv)

This work aims to study the dynamic between research in the industry and academia in computer vision. The results are demonstrated on a set of top-5 vision conferences that are representative of the field. Since data for such analysis was not readily available, significant effort was spent on gathering and processing meta-data from the original publications. First, this study quantifies the share of industry-sponsored research. Specifically, it shows that the proportion of papers published by industry-affiliated researchers is increasing and that more academics join companies or collaborate with them. Next, the possible impact of industry presence is further explored, namely in the distribution of research topics and citation patterns. The results indicate that the distribution of the research topics is similar in industry and academic papers. However, there is a strong preference towards citing industry papers. Finally, possible reasons for citation bias, such as code availability and influence, are investigated.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 20 references that are in the map. First posted on arXiv 10 Jul 2021.

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4 of 20 references.

References · 4

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  4. 2015Fast R-CNNcited by 2,933

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