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

TL;DR

This work tackles VideoQA by addressing the challenge of temporal dynamics that standard transformers struggle to model. It introduces the Temporal Trio Transformer (T3T), which decomposes temporal modeling into Temporal Smoothing via Brownian Bridge, Temporal Difference for abrupt changes, and Temporal Fusion to integrate temporal cues with textual questions through cross-attention. Empirical results on NExT-QA, MSVD, and MSRVTT show that TS and TD capture complementary temporal information, with TF enabling effective text-guided fusion, yielding superior performance on temporal reasoning tasks. The approach provides interpretable temporal representations and a general framework for video-language understanding that can inform future research in temporal modeling and VideoQA.

Abstract

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 Temporal Trio Transformer (T3T), a novel architecture that models time consistency and time variability. The T3T integrates three key components: Temporal Smoothing (TS), Temporal Difference (TD), and Temporal Fusion (TF). 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. Subsequently, the TF module synthesizes these temporal features with textual cues, facilitating a deeper contextual understanding and response accuracy. 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.

Figures (4)

• Figure 1: The visualization comparison of normalized time smoothing and time difference features. Given the question, they focus on the different parts of video. The consistency learned from TS provides high values on the frames related to the ‘Turn and Back’. The variability extracted from TD pays close attention to the local feature change where ‘Spinning’ is drastic action.
• Figure 2: Overview of our framework for VideoQA. Fig. \ref{['framework']} (a) show the entire process begins with video frames and textual data are separately encoded. The Temporal Trio Transformer (T3T) incorporates three modules: Temporal Smoothing (TS), Temporal Difference (TD), and Temporal Fusion (TF), to deeply capture the temporal dynamics within the video. TS module smooths temporal transitions using the Brownian Bridge Process, detailed in Fig. \ref{['framework']} (b). TD module captures abrupt temporal variations through difference operations, detailed in Fig. \ref{['framework']} (c). These features via the balance value $\alpha$ are fused with textual information in the TF. Finally, The Answer Prediction stage integrates and refines these multimodal features enabling accurate answer selection.
• Figure 3: Comparison of the balance value $\alpha$ for three dataset. We use double axes to distinguish NExT-QA on the left, MSVD and MSRVTT on the right. The best result for each in red.
• Figure 4: The normalized distribution scale extracted by TS module in yellow and TD module in green of the whole NExT-QA test set.