Image Recognition: Past, Present, and Future

How to spot the fine Austin Squirrel — Classic might not always be enough

How to make sure we look for the right things?

The literature about image recognition is full of anecdotes of things like tanks being recognized by the snow or the blue skies they have been photographed in instead of the tank itself. The issue described here is called “overfitting”. Overfitting occurs when a machine learning model learns all kinds of features of the examples it is trained on, but does not concentrate on the relevant ones. It thus is not general enough to recognize similar objects that were not in the training set.

And the future?

The classic techniques and machine learning described here are both well established and work well in practice. They can be seen as the past and the present of image recognition. But what comes next? There are a couple of techniques that certainly look promising, but they are maybe not readily available or are not quite as mature.

My Workshop at ODSC West 2022 in San Francisco

On the level described here, all might sound straightforward and simple. However, when you want to actually employ those techniques quite a few challenges come up, even if you are familiar with machine learning already.



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