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Searching for Computer Vision North Stars
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Abstract
Computer vision is one of the most fundamental areas of artificial intelligence research. It has contributed to the tremendous progress in the recent deep learning revolution in AI. In this essay, we provide a perspective of the recent evolution of object recognition in computer vision, a flagship research topic that led to the breakthrough data set of ImageNet and its ensuing algorithm developments. We argue that much of this progress is rooted in the pursuit of research “north stars,” wherein researchers focus on critical problems of a scientific discipline that can galvanize major efforts and groundbreaking progress. Following the success of ImageNet and object recognition, we observe a number of exciting areas of research and a growing list of north star problems to tackle. This essay recounts the brief history of ImageNet, its related work, and the follow-up progress. The goal is to inspire more north star work to advance the field, and AI at large.
Title: Searching for Computer Vision North Stars
Description:
Abstract
Computer vision is one of the most fundamental areas of artificial intelligence research.
It has contributed to the tremendous progress in the recent deep learning revolution in AI.
In this essay, we provide a perspective of the recent evolution of object recognition in computer vision, a flagship research topic that led to the breakthrough data set of ImageNet and its ensuing algorithm developments.
We argue that much of this progress is rooted in the pursuit of research “north stars,” wherein researchers focus on critical problems of a scientific discipline that can galvanize major efforts and groundbreaking progress.
Following the success of ImageNet and object recognition, we observe a number of exciting areas of research and a growing list of north star problems to tackle.
This essay recounts the brief history of ImageNet, its related work, and the follow-up progress.
The goal is to inspire more north star work to advance the field, and AI at large.
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