Thus, we have developed a video-comparing technology which can be used, for example, to search for and recognize, in real time, hundreds of thousands of specific video clips on thousands of channels, in movie libraries, and video hosting services. Currently, searching for videos based on other videos does not make up an even remotely significant share of video search requests—everyone searches for videos using texts in the first place: titles, names, descriptions, or tags. It is what people are used to, but in reality, it is neither handy nor precise—you have to process a lot of unnecessary information (for example, tons of repeated content) before you find what you really want to. Searching for videos using videos with the help of TAPe would make the process so much more convenient and straightforward. And all we need to solve the above-mentioned tasks is a single server without any graphics cards.
One of the reasons behind such efficiency is that our algorithms do not imply what is referred to as the convolution method, which is the most resource-intensive operation that no modern computer vision neural network can do without. The human brain does not need this type of operation, and our technology is built so as to follow the processing pattern used by the human brain, or the language of thought. The TAPe-based technology, similarly to the human brain, processes any image right away as a whole, and the recognition results are not conditional upon the absence of noise.