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

arXiv: https://arxiv.org/abs/2504.05783 · HTML: https://mlsanity.com/paper/2504.05783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 24 of 24)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [A Joint Sequence Fusion Model for Video Question Answering and Retrieval](https://mlsanity.com/paper/1808.02559/) (2018)
- [Self-Chained Image-Language Model for Video Localization and Question Answering](https://mlsanity.com/paper/2305.06988/) (2023)
- [Revisiting the "Video" in Video-Language Understanding](https://mlsanity.com/paper/2206.01720/) (2022)
- [Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering](https://mlsanity.com/paper/1904.04357/) (2019)
- [Video Question Answering: Datasets, Algorithms and Challenges](https://mlsanity.com/paper/2203.01225/) (2022)
- [RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning](https://mlsanity.com/paper/2011.07949/) (2020)
- [Video as Conditional Graph Hierarchy for Multi-Granular Question Answering](https://mlsanity.com/paper/2112.06197/) (2021)
- [Video Transformers: A Survey](https://mlsanity.com/paper/2201.05991/) (2022)
- [Video Graph Transformer for Video Question Answering](https://mlsanity.com/paper/2207.05342/) (2022)
- [Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering](https://mlsanity.com/paper/2104.14085/) (2021)
- [MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering](https://mlsanity.com/paper/2212.09522/) (2022)
- [Paxion: Patching Action Knowledge in Video-Language Foundation Models](https://mlsanity.com/paper/2305.10683/) (2023)
- [DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering](https://mlsanity.com/paper/2107.04768/) (2021)
- [Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering](https://mlsanity.com/paper/2106.13432/) (2021)
- [Equivariant and Invariant Grounding for Video Question Answering](https://mlsanity.com/paper/2207.12783/) (2022)
- [Discovering Spatio-Temporal Rationales for Video Question Answering](https://mlsanity.com/paper/2307.12058/) (2023)
- [Visual Causal Scene Refinement for Video Question Answering](https://mlsanity.com/paper/2305.04224/) (2023)
- [Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering](https://mlsanity.com/paper/2205.04061/) (2022)
- [Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering](https://mlsanity.com/paper/2407.03008/) (2024)
- [Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer](https://mlsanity.com/paper/2302.02136/) (2023)

## Abstract (arXiv)

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.