Tiny Videos: A Large Dataset for Image and Video Frame Categorization

This paper presents a new method for video and image categorization based on a database of over 50,000 videos collected from YouTube and down-sampled to tiny size. The categorization results achieved by tiny videos are compared with the tiny images framework for a variety of recognition tasks. The tiny images dataset consists of 80 million images collected from the Internet. These are the largest labeled research datasets of videos and images available to date. We show that tiny videos are better suited for classifying sports activities and scenery, while tiny images perform better at recognizing objects. Furthermore, we demonstrate that combining the tiny images and tiny videos datasets improves categorization precision in a wider range of categories.