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 completeness is that there exist problems for which it is impossible to predict what a (Turing-complete) system will do over an arbitrarily long time. The classic example is the halting problem: create an algorithm which takes as input (a) a program in some Turing-complete language, and (b) some data to be fed to "that" program; and which determines whether the program, operating on the input, will eventually stop or will continue forever. It is trivial to create an algorithm that can do this for "some" inputs, but impossible to do this in general. For any characteristic of the program's eventual output,