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 one instruction set computer) and the ability to change an arbitrary amount of memory ("e.g.", the ability to maintain an arbitrary number of data items). Of course, no physical system can have infinite memory; but if the limitation of finite memory is ignored, most programming languages are otherwise Turing complete. In colloquial usage, the terms "Turing complete" or "Turing equivalent" are used to mean that any real-world general-purpose computer or computer language can approximately simulate the computational aspects of any other real-world general-purpose computer or computer language. Real computers constructed so far can be functionally analyzed like a single-tape Turing