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Neural networks trained on datasets such as ImageNet have led to majoradvances in visual object classification. One obstacle that prevents networksfrom reasoning more deeply about complex scenes and situations, and fromintegrating visual knowledge with natural language, like humans do, is theirlack of common sense knowledge about the physical world. Videos, unlike stillimages, contain a wealth of detailed information about the physical world.However, most labelled video datasets represent high-level concepts rather thandetailed physical aspects about actions and scenes. In this work, we describeour ongoing collection of the "something-something" database of videoprediction tasks whose solutions require a common sense understanding of thedepicted situation. The database currently contains more than 100,000 videosacross 174 classes, which are defined as caption-templates. We also describethe challenges in crowd-sourcing this data at scale.

The "something something" video database for learning and evaluating visual common sense