Causal Understanding For Video Question Answering
This work addresses the problem of improving causal understanding in video question answering for AI researchers, though it is incremental as it builds on existing methods.
The paper tackled the challenge of Video Question Answering, particularly on the NExT-QA dataset with causal and temporal questions, by proposing improvements in frame sampling, action encoding, and interventions, achieving state-of-the-art results with gains of +6.3% for single-frame and +1.1% for complete-video approaches.
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.