Video Flow as Time Series: Discovering Temporal Consistency and Variability for VideoQA

--Video Question Answering (VideoQA) is a complex video-language task that demands a sophisticated understanding of both visual content and temporal dynamics. Traditional Transformer-style architectures, while effective in integrating multimodal data, often simplify temporal dynamics through positional encoding and fail to capture non-linear interactions within video sequences. In this paper, we introduce the T emporal Trio Transformer (T3T), a novel architecture that models time consistency and time variability. The TS module employs Brownian Bridge for capturing smooth, continuous temporal transitions, while the TD module identifies and encodes significant temporal variations and abrupt changes within the video content. The efficacy of the T3T is demonstrated through extensive testing on multiple VideoQA benchmark datasets. Our results underscore the importance of a nuanced approach to temporal modeling in improving the accuracy and depth of video-based question answering. In the realm of video-language tasks, Video Question Answering (VideoQA) stands out as one of the challenges that demand a high degree of temporal understanding where video and language are both sequential forms of information characterized by their temporality. This task requires models not only to process visual content but also to reason across the temporal sequence of events in a video in response to specific questions [1]-[4].