# Where To Look: Focus Regions for Visual Question Answering

arXiv: https://arxiv.org/abs/1511.07394 · HTML: https://mlsanity.com/paper/1511.07394/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2015.

## Built on by (most cited first, 30 of 64)

- [To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning](https://mlsanity.com/paper/2311.07574/) (2023)
- [CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning](https://mlsanity.com/paper/2208.05358/) (2022)
- [Vision-Language Pre-training: Basics, Recent Advances, and Future Trends](https://mlsanity.com/paper/2210.09263/) (2022)
- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
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- [N24News: A New Dataset for Multimodal News Classification](https://mlsanity.com/paper/2108.13327/) (2021)
- [From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data](https://mlsanity.com/paper/2205.03147/) (2022)
- [Vision-Language Intelligence: Tasks, Representation Learning, and Large Models](https://mlsanity.com/paper/2203.01922/) (2022)
- [Interpretable by Design: Learning Predictors by Composing Interpretable Queries](https://mlsanity.com/paper/2207.00938/) (2022)
- [Multi-modal Machine Learning in Engineering Design: A Review and Future Directions](https://mlsanity.com/paper/2302.10909/) (2023)
- [Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline](https://mlsanity.com/paper/2209.12028/) (2022)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [Vision+X: A Survey on Multimodal Learning in the Light of Data](https://mlsanity.com/paper/2210.02884/) (2022)
- [ProTo: Program-Guided Transformer for Program-Guided Tasks](https://mlsanity.com/paper/2110.00804/) (2021)
- [LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation](https://mlsanity.com/paper/2210.15461/) (2022)
- [HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images](https://mlsanity.com/paper/2301.09460/) (2023)
- [LOVA3: Learning to Visual Question Answering, Asking and Assessment](https://mlsanity.com/paper/2405.14974/) (2024)
- [Multimodal Integration of Human-Like Attention in Visual Question Answering](https://mlsanity.com/paper/2109.13139/) (2021)
- [Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning](https://mlsanity.com/paper/2302.09636/) (2023)
- [CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations](https://mlsanity.com/paper/2204.02380/) (2022)
- [Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge](https://mlsanity.com/paper/2305.18842/) (2023)
- [VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis](https://mlsanity.com/paper/2106.10548/) (2021)
- [Asking questions on handwritten document collections](https://mlsanity.com/paper/2110.00711/) (2021)
- [Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model](https://mlsanity.com/paper/2407.05352/) (2024)
- [Diagnosing Human-object Interaction Detectors](https://mlsanity.com/paper/2308.08529/) (2023)
- [X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering](https://mlsanity.com/paper/2107.11576/) (2021)
- [Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering](https://mlsanity.com/paper/2306.16713/) (2023)
- [Answer Questions with Right Image Regions: A Visual Attention Regularization Approach](https://mlsanity.com/paper/2102.01916/) (2021)

## Abstract (arXiv)

We present a method that learns to answer visual questions by selecting image regions relevant to the text-based query. Our method exhibits significant improvements in answering questions such as "what color," where it is necessary to evaluate a specific location, and "what room," where it selectively identifies informative image regions. Our model is tested on the VQA dataset which is the largest human-annotated visual question answering dataset to our knowledge.