Visual Entity Detection Task

An Visual Entity Detection Task is a computer vision task that is a detection task.

• AKA: Visual Entity Identification.
• Context:
  • Input: Image Data.
  • output: Category Identifier.
  • measure(s): Intersection over Union, ...
  • It can support a Visual Item Recognition Task.
• Example(s):
  • Face Detection.
  • Shoes Detection.
  • “Which squares contain traffic lights?” [1]
  • …
• Counter-Example(s):
  • Named Entity Identification Task.
• See: Video Surveillance, Computer Vision, Image Processing, Pedestrian Detection, Image Retrieval.

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References

2020

• (Hou et al., 2020) ⇒ Jingyi Hou, Xinxiao Wu, Xiaoxun Zhang, Yayun Qi, Yunde Jia, and Jiebo Luo. (2020). “Joint Commonsense and Relation Reasoning for Image and Video Captioning.” In: Proceedings of AAAI-2020.

2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/object_detection Retrieved:2017-6-2.
  • Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

2014

• (Zitnick & Dollár, 2014) ⇒ C Lawrence Zitnick, and Piotr Dollár. (2014). “Edge Boxes: Locating Object Proposals from Edges.” In: European Conference on Computer Vision.
  • QUOTE: The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection.

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