# Modular Blended Attention Network for Video Question Answering

arXiv: https://arxiv.org/abs/2311.12866 · HTML: https://mlsanity.com/paper/2311.12866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://mlsanity.com/paper/2102.06183/) (2021)
- [TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering](https://mlsanity.com/paper/1704.04497/) (2017)
- [HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://mlsanity.com/paper/2005.00200/) (2020)
- [Just Ask: Learning to Answer Questions from Millions of Narrated Videos](https://mlsanity.com/paper/2012.00451/) (2020)
- [TVQA+: Spatio-Temporal Grounding for Video Question Answering](https://mlsanity.com/paper/1904.11574/) (2019)
- [Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering](https://mlsanity.com/paper/1904.04357/) (2019)
- [TGIF: A New Dataset and Benchmark on Animated GIF Description](https://mlsanity.com/paper/1604.02748/) (2016)
- [Motion-Appearance Co-Memory Networks for Video Question Answering](https://mlsanity.com/paper/1803.10906/) (2018)
- [Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis](https://mlsanity.com/paper/1911.05544/) (2019)
- [Location-aware Graph Convolutional Networks for Video Question Answering](https://mlsanity.com/paper/2008.09105/) (2020)
- [Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering](https://mlsanity.com/paper/2104.14085/) (2021)
- [Look Before you Speak: Visually Contextualized Utterances](https://mlsanity.com/paper/2012.05710/) (2020)
- [DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering](https://mlsanity.com/paper/2107.04768/) (2021)

## Abstract (arXiv)

In multimodal machine learning tasks, it is due to the complexity of the assignments that the network structure, in most cases, is assembled in a sophisticated way. The holistic architecture can be separated into several logical parts according to the respective ends that the modules are devised to achieve. As the number of modalities of information representation increases, constructing ad hoc subnetworks for processing the data from divergent modalities while mediating the fusion of different information types has become a cumbersome and expensive problem. In this paper, we present an approach to facilitate the question with a reusable and composable neural unit; by connecting the units in series or parallel, the arduous network constructing of multimodal machine learning tasks will be accomplished in a much straightforward way. Additionally, through parameter sharing (weights replication) among the units, the space complexity will be significantly reduced. We have conducted experiments on three commonly used datasets; our method achieves impressive performance compared to several video QA baselines.