# A Focused Dynamic Attention Model for Visual Question Answering

arXiv: https://arxiv.org/abs/1604.01485 · HTML: https://mlsanity.com/paper/1604.01485/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2016.

## Built on by (most cited first, 9 of 9)

- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [A Comprehensive Survey on Visual Question Answering Datasets and Algorithms](https://mlsanity.com/paper/2411.11150/) (2024)
- [From Pixels to Objects: Cubic Visual Attention for Visual Question Answering](https://mlsanity.com/paper/2206.01923/) (2022)
- [Recent, rapid advancement in visual question answering architecture: a review](https://mlsanity.com/paper/2203.01322/) (2022)
- [Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering](https://mlsanity.com/paper/2112.07270/) (2021)
- [Improved RAMEN: Towards Domain Generalization for Visual Question Answering](https://mlsanity.com/paper/2109.02370/) (2021)
- [Attention Mechanism based Cognition-level Scene Understanding](https://mlsanity.com/paper/2204.08027/) (2022)
- [Machine Intelligence that Understands Visual and Linguistic Information and Interacts with Humans and Environments](https://mlsanity.com/paper/2605.24020/) (2026)

## Abstract (arXiv)

Visual Question and Answering (VQA) problems are attracting increasing interest from multiple research disciplines. Solving VQA problems requires techniques from both computer vision for understanding the visual contents of a presented image or video, as well as the ones from natural language processing for understanding semantics of the question and generating the answers. Regarding visual content modeling, most of existing VQA methods adopt the strategy of extracting global features from the image or video, which inevitably fails in capturing fine-grained information such as spatial configuration of multiple objects. Extracting features from auto-generated regions -- as some region-based image recognition methods do -- cannot essentially address this problem and may introduce some overwhelming irrelevant features with the question. In this work, we propose a novel Focused Dynamic Attention (FDA) model to provide better aligned image content representation with proposed questions. Being aware of the key words in the question, FDA employs off-the-shelf object detector to identify important regions and fuse the information from the regions and global features via an LSTM unit. Such question-driven representations are then combined with question representation and fed into a reasoning unit for generating the answers. Extensive evaluation on a large-scale benchmark dataset, VQA, clearly demonstrate the superior performance of FDA over well-established baselines.