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Most prior art in visual understanding relies solely on analyzing the "what"(e.g., event recognition) and "where" (e.g., event localization), which in somecases, fails to describe correct contextual relationships between events orleads to incorrect underlying visual attention. Part of what defines us ashuman and fundamentally different from machines is our instinct to seekcausality behind any association, say an event Y that happened as a directresult of event X. To this end, we propose iPerceive, a framework capable ofunderstanding the "why" between events in a video by building a common-senseknowledge base using contextual cues to infer causal relationships betweenobjects in the video. We demonstrate the effectiveness of our technique usingthe dense video captioning (DVC) and video question answering (VideoQA) tasks.Furthermore, while most prior work in DVC and VideoQA relies solely on visualinformation, other modalities such as audio and speech are vital for a humanobserver's perception of an environment. We formulate DVC and VideoQA tasks asmachine translation problems that utilize multiple modalities. By evaluatingthe performance of iPerceive DVC and iPerceive VideoQA on the ActivityNetCaptions and TVQA datasets respectively, we show that our approach furthers thestate-of-the-art. Code and samples are available at: iperceive.amanchadha.com.

iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering