# Video Question Answering via Attribute-Augmented Attention Network Learning

arXiv: https://arxiv.org/abs/1707.06355 · HTML: https://mlsanity.com/paper/1707.06355/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2017.

## Built on by (most cited first, 30 of 39)

- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [Zero-Shot Video Question Answering via Frozen Bidirectional Language Models](https://mlsanity.com/paper/2206.08155/) (2022)
- [Boundary Proposal Network for Two-Stage Natural Language Video Localization](https://mlsanity.com/paper/2103.08109/) (2021)
- [Pano-AVQA: Grounded Audio-Visual Question Answering on 360^circ Videos](https://mlsanity.com/paper/2110.05122/) (2021)
- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning](https://mlsanity.com/paper/2103.16564/) (2021)
- [Temporal Query Networks for Fine-grained Video Understanding](https://mlsanity.com/paper/2104.09496/) (2021)
- [Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language](https://mlsanity.com/paper/2110.15358/) (2021)
- [Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives](https://mlsanity.com/paper/2406.05615/) (2024)
- [FunQA: Towards Surprising Video Comprehension](https://mlsanity.com/paper/2306.14899/) (2023)
- [Learning to Answer Visual Questions from Web Videos](https://mlsanity.com/paper/2205.05019/) (2022)
- [Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering](https://mlsanity.com/paper/2106.13432/) (2021)
- [Natural Language Video Localization with Learnable Moment Proposals](https://mlsanity.com/paper/2109.10678/) (2021)
- [Connecting Vision and Language with Video Localized Narratives](https://mlsanity.com/paper/2302.11217/) (2023)
- [ViLA: Efficient Video-Language Alignment for Video Question Answering](https://mlsanity.com/paper/2312.08367/) (2023)
- [Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer](https://mlsanity.com/paper/2308.08414/) (2023)
- [TRAVL: A Recipe for Making Video-Language Models Better Judges of Physics Implausibility](https://mlsanity.com/paper/2510.07550/) (2025)
- [Recent Advances in Video Question Answering: A Review of Datasets and Methods](https://mlsanity.com/paper/2101.05954/) (2021)
- [Weakly-Supervised Temporal Article Grounding](https://mlsanity.com/paper/2210.12444/) (2022)
- [DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding](https://mlsanity.com/paper/2312.02549/) (2023)
- [Question-Answering Dense Video Events](https://mlsanity.com/paper/2409.04388/) (2024)
- [WildQA: In-the-Wild Video Question Answering](https://mlsanity.com/paper/2209.06650/) (2022)
- [Object-Centric Representation Learning for Video Question Answering](https://mlsanity.com/paper/2104.05166/) (2021)
- [Transferring Domain-Agnostic Knowledge in Video Question Answering](https://mlsanity.com/paper/2110.13395/) (2021)
- [An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes](https://mlsanity.com/paper/2504.15270/) (2025)
- [Answering Diverse Questions via Text Attached with Key Audio-Visual Clues](https://mlsanity.com/paper/2403.06679/) (2024)
- [Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer](https://mlsanity.com/paper/2302.02136/) (2023)
- [YTCommentQA: Video Question Answerability in Instructional Videos](https://mlsanity.com/paper/2401.17343/) (2024)
- [Deep Learning for Embodied Vision Navigation: A Survey](https://mlsanity.com/paper/2108.04097/) (2021)

## Abstract (arXiv)

Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering. We then incorporate the multi-step reasoning process for our proposed attention network to further improve the performance. We construct a large-scale video question answering dataset. We conduct the experiments on both multiple-choice and open-ended video question answering tasks to show the effectiveness of the proposed method.