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

2025-04-08

AI Summary

Traditional Transformers in video question answering (VideoQA) suffer from oversimplified positional encoding, which inadequately captures temporal dynamics and fails to model nonlinear temporal interactions. To address this, we propose the Temporal Trio Transformer (T3T), the first architecture to explicitly decouple and jointly model three core temporal properties: temporal consistency (formalized via Brownian Bridge priors), temporal variability (via temporal abruptness detection), and cross-modal temporal fusion. T3T introduces temporal difference encoding, multi-granularity cross-modal fusion, and a frame-level temporal feature disentanglement module. Evaluated on multiple mainstream VideoQA benchmarks, T3T achieves significant accuracy improvements over prior methods. Results demonstrate that fine-grained, dual-attribute (smoothness and abruptness) temporal modeling is critical for deep semantic understanding and robust reasoning in VideoQA.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Modeling temporal consistency and variability in VideoQA
• Capturing non-linear interactions in video sequences
• Improving accuracy and depth of video-based question answering

Innovation

• Temporal Trio Transformer models time dynamics
• Brownian Bridge captures smooth temporal transitions
• Temporal Fusion synthesizes video-text features