Visual Turing Test Visual Turing Test Computer vision research is driven by standard evaluation practices. The current systems are tested by their accuracy for tasks like object detection, segmentation and localization. Methods like the convolutional neural networks seem to be doing pretty well in these tasks, but the current systems are still not close to solving the ultimate problem of understanding images the way humans do. So motivated by the ability of humans to understand an image and even tell a story about it, Geman "et al." have introduced the Visual Turing Test for computer vision systems. As described in, it is “an
Turing test intelligence programs may be measured. The organisers of the Hutter Prize believe that compressing natural language text is a hard AI problem, equivalent to passing the Turing test. The data compression test has some advantages over most versions and variations of a Turing test, including: The main disadvantages of using data compression as a test are: A related approach to Hutter's prize which appeared much earlier in the late 1990s is the inclusion of compression problems in an extended Turing test. or by tests which are completely derived from Kolmogorov complexity. Other related tests in this line are presented by