EventFormer: A Node-graph Hierarchical Attention Transformer for Action-centric Video Event Prediction

TL;DR

EventFormer introduces a hierarchical attention transformer for action-centric video event prediction, leveraging a new structured dataset with rich annotations to improve understanding of complex video events.

Contribution

The paper presents EventFormer, a novel node-graph hierarchical attention model, and introduces AVEP, a large dataset with fine-grained multimodal annotations for video event prediction.

Findings

EventFormer outperforms state-of-the-art video prediction models.

The AVEP dataset enables better structured representation of video events.

Traditional visual models struggle with complex event structures.

Abstract

Script event induction, which aims to predict the subsequent event based on the context, is a challenging task in NLP, achieving remarkable success in practical applications. However, human events are mostly recorded and presented in the form of videos rather than scripts, yet there is a lack of related research in the realm of vision. To address this problem, we introduce AVEP (Action-centric Video Event Prediction), a task that distinguishes itself from existing video prediction tasks through its incorporation of more complex logic and richer semantic information. We present a large structured dataset, which consists of about $35K$ annotated videos and more than $178K$ video clips of event, built upon existing video event datasets to support this task. The dataset offers more fine-grained annotations, where the atomic unit is represented as a multimodal event argument node, providing better structured representations of video events. Due to the complexity of event structures, traditional visual models that take patches or frames as input are not well-suited for AVEP. We propose EventFormer, a node-graph hierarchical attention based video event prediction model, which can capture both the relationships between events and their arguments and the coreferencial relationships between arguments. We conducted experiments using several SOTA video prediction models as well as LVLMs on AVEP, demonstrating both the complexity of the task and the value of the dataset. Our approach outperforms all these video prediction models. We will release the dataset and code for replicating the experiments and annotations.