What is More Likely to Happen Next? Video-and-Language Future Event Prediction
This work addresses the challenge of multimodal commonsense reasoning for AI, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of predicting future events from video and dialogue by introducing the VLEP dataset with 28,726 examples and a baseline model that uses video, dialogue, and commonsense knowledge, achieving results that show each information type is useful but leave large room for improvement compared to human performance.
Given a video with aligned dialogue, people can often infer what is more likely to happen next. Making such predictions requires not only a deep understanding of the rich dynamics underlying the video and dialogue, but also a significant amount of commonsense knowledge. In this work, we explore whether AI models are able to learn to make such multimodal commonsense next-event predictions. To support research in this direction, we collect a new dataset, named Video-and-Language Event Prediction (VLEP), with 28,726 future event prediction examples (along with their rationales) from 10,234 diverse TV Show and YouTube Lifestyle Vlog video clips. In order to promote the collection of non-trivial challenging examples, we employ an adversarial human-and-model-in-the-loop data collection procedure. We also present a strong baseline incorporating information from video, dialogue, and commonsense knowledge. Experiments show that each type of information is useful for this challenging task, and that compared to the high human performance on VLEP, our model provides a good starting point but leaves large room for future work. Our dataset and code are available at: https://github.com/jayleicn/VideoLanguageFuturePred