# Visual question answering: from early developments to recent advances -- a survey

arXiv: https://arxiv.org/abs/2501.03939 · HTML: https://mlsanity.com/paper/2501.03939/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2025.

## Built on by (most cited first, 19 of 19)

- [Vision-aligned Latent Reasoning for Multi-modal Large Language Model](https://mlsanity.com/paper/2602.04476/) (2026)
- [Affordance Benchmark for MLLMs](https://mlsanity.com/paper/2506.00893/) (2025)
- [Subspace Alignment for Vision-Language Model Test-time Adaptation](https://mlsanity.com/paper/2601.08139/) (2026)
- [Rethinking the Role of LLMs in Time Series Forecasting](https://mlsanity.com/paper/2602.14744/) (2026)
- [Discriminative Perception via Anchored Description for Reasoning Segmentation](https://mlsanity.com/paper/2603.04002/) (2026)
- [From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark](https://mlsanity.com/paper/2505.17482/) (2025)
- [Culture In a Frame: C3B as a Comic-Based Benchmark for Multimodal Culturally Awareness](https://mlsanity.com/paper/2510.00041/) (2025)
- [Spectral Heat Flow for Conservative Token Condensation in Vision-Language Models](https://mlsanity.com/paper/2607.10640/) (2026)
- [MAP: Mitigating Hallucinations in Large Vision-Language Models with Map-Level Attention Processing](https://mlsanity.com/paper/2508.01653/) (2025)
- [MGA-VQA: Secure and Interpretable Graph-Augmented Visual Question Answering with Memory-Guided Protection Against Unauthorized Knowledge Use](https://mlsanity.com/paper/2511.17881/) (2025)
- [Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessment](https://mlsanity.com/paper/2511.19557/) (2025)
- [MaS-VQA: A Mask-and-Select Framework for Knowledge-Based Visual Question Answering](https://mlsanity.com/paper/2602.15915/) (2026)
- [VALD: Multi-Stage Vision Attack Detection for Efficient LVLM Defense](https://mlsanity.com/paper/2602.19570/) (2026)
- [Elevating Visual Question Answering through Implicitly Learned Reasoning Pathways in LVLMs](https://mlsanity.com/paper/2503.14674/) (2025)
- [Exploring the Application of Visual Question Answering (VQA) for Classroom Activity Monitoring](https://mlsanity.com/paper/2507.22369/) (2025)
- [Image Realness Assessment and Localization with Multimodal Features](https://mlsanity.com/paper/2509.13289/) (2025)
- [Stable Single-Pixel Contrastive Learning for Semantic and Geometric Tasks](https://mlsanity.com/paper/2512.04970/) (2025)
- [Mitigating Entangled Steering in Large Vision-Language Models for Hallucination Reduction](https://mlsanity.com/paper/2604.07914/) (2026)
- [Latent-Aligned Reasoning for Multimodal Recommendation](https://mlsanity.com/paper/2609.04645/) (2026)

## Abstract (arXiv)

Visual Question Answering (VQA) is an evolving research field aimed at enabling machines to answer questions about visual content by integrating image and language processing techniques such as feature extraction, object detection, text embedding, natural language understanding, and language generation. With the growth of multimodal data research, VQA has gained significant attention due to its broad applications, including interactive educational tools, medical image diagnosis, customer service, entertainment, and social media captioning. Additionally, VQA plays a vital role in assisting visually impaired individuals by generating descriptive content from images. This survey introduces a taxonomy of VQA architectures, categorizing them based on design choices and key components to facilitate comparative analysis and evaluation. We review major VQA approaches, focusing on deep learning-based methods, and explore the emerging field of Large Visual Language Models (LVLMs) that have demonstrated success in multimodal tasks like VQA. The paper further examines available datasets and evaluation metrics essential for measuring VQA system performance, followed by an exploration of real-world VQA applications. Finally, we highlight ongoing challenges and future directions in VQA research, presenting open questions and potential areas for further development. This survey serves as a comprehensive resource for researchers and practitioners interested in the latest advancements and future