Causal Understanding For Video Question Answering

TL;DR

This paper advances video question answering by proposing novel sampling, encoding, and intervention techniques to better understand causal and temporal reasoning, achieving state-of-the-art results on NExT-QA.

Contribution

It introduces four new methods addressing limitations of prior causal reasoning approaches in video question answering.

Findings

State-of-the-art results on NExT-QA dataset

Improved performance for single-frame and complete-video methods

Systematic approach to sampling and encoding enhances understanding

Abstract

Video Question Answering is a challenging task, which requires the model to reason over multiple frames and understand the interaction between different objects to answer questions based on the context provided within the video, especially in datasets like NExT-QA (Xiao et al., 2021a) which emphasize on causal and temporal questions. Previous approaches leverage either sub-sampled information or causal intervention techniques along with complete video features to tackle the NExT-QA task. In this work we elicit the limitations of these approaches and propose solutions along four novel directions of improvements on theNExT-QA dataset. Our approaches attempts to compensate for the shortcomings in the previous works by systematically attacking each of these problems by smartly sampling frames, explicitly encoding actions and creating interventions that challenge the understanding of the model. Overall, for both single-frame (+6.3%) and complete-video (+1.1%) based approaches, we obtain the state-of-the-art results on NExT-QA dataset.