While a certain degree of advancement is possible when working in isolation on a scientific problem, interaction with the scientific community can drastically hasten one's progress. Most people have their own experiences with 'isolation' and 'interaction with a community' but I should explicitly delineate how I intend to use these terms. While 'interaction with a community' usually implies two-sided communication such as directly working together on a problem or simply discussing one's research with a group of other scientists, I want to consider a subtler form of interaction.
By reading about past accomplishments and former ideologies in a particular field, one is essentially communicating with the ideas of the past. While many scholarly articles -- in a field such as Computer Vision -- are mostly devoted to algorithmic details and experimental evaluations, it isn't too difficult to find manuscripts which reveal the philosophic underpinnings of the proposed research. It is even possible to find papers which are entirely devoted to understanding the philosophical motivations of a past generation of research.
A prime example of interaction with the past is the paper "Object Recognition in the Geometric Era: A Retrospective," by Joseph L. Mundy from Brown University. Such a compilation of ideas -- perhaps even a mini-summa -- is quite accessible to any researcher in the field of Computer Vision. Avoiding the specific details of any algorithm developed in the so-called Geometric Era of Computer Vision, this text is both entertaining and highly educational. By reading such texts one is effectively communicating (albeit one-way) with a larger scientific community of the past.
To conclude, I would like to point out that neither do I agree with some of the past paradigms of Computer Vision, nor am I a die-hard proponent of the modern statistical machine learning school of thought. However, to explore new territories what better way to scope the world around you than by standing on the shoulders of giants? We should be aware of what has been done in the past, and sometimes de-emphasize algorithmic minutiae in order to understand the philosophical motivations behind former paradigms.
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