Visual question answering: from early developments to recent advances -- a survey (2501.03939v1)

Abstract: 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 LLMs (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

Visual Question Answering: From Early Developments to Recent Advances - A Survey

The paper "Visual Question Answering: From Early Developments to Recent Advances - A Survey" presents a detailed examination of the Visual Question Answering (VQA) field, charting its evolution from its inception to its current state and highlighting future directions for research. VQA is a multimodal task combining computer vision and natural language processing, requiring systems to interpret and respond to questions about visual content. This task has gained increasing attention due to its complexity and broad applicability, ranging from assisting visually impaired individuals to enhancing interactive educational tools.

Key Components and Taxonomy of VQA

The authors propose a taxonomy that categorizes VQA architectures based on their core components: Vision Encoders, Language Encoders, Fusion Methods, and Answer Decoders. This classification provides a structured framework for analyzing different VQA approaches:

• Vision Encoders: Initially, grid-based methods using Convolutional Neural Networks (CNNs) dominated early developments. These methods were later surpassed by object-based approaches, such as Faster R-CNN, which improved performance by focusing on object-level features. More recently, Vision Transformer (ViT)-based encoders have emerged, offering scalability and efficient image patch processing, improving computational efficiency and performance.
• Language Encoders: VQA systems have transitioned from basic Bag-of-Words models to advanced techniques like LSTMs and, later, Transformers. Transformers, particularly BERT and its derivatives, have become prominent due to their ability to capture complex linguistic nuances, thereby enhancing question understanding.
• Fusion Methods: The fusion of visual and linguistic features has evolved significantly. Initial models employed simple concatenation or element-wise products, which lacked depth. Subsequently, attention mechanisms were introduced to focus on relevant image regions and question segments. The introduction of co-attention and Transformer-based architectures further improved the integration of modalities. Large Visual-LLMs (LVLMs) now lead the field, leveraging large-scale training to enhance robustness and applicability across tasks.
• Answer Decoders: VQA systems address open-ended and closed-vocabulary formats, with multiple-choice models and recent developments in generating free-form answers. For enhanced performance, some systems utilize LLMs fine-tuned to produce human-like responses.

Datasets and Evaluation

The paper reviews various datasets utilized in VQA, each contributing uniquely to the field's challenges. Datasets such as VQA v1.0 and v2.0, CLEVR, and GQA provide diverse testing grounds, pushing models to handle different reasoning complexities. Evaluation metrics have similarly evolved, with traditional accuracy measures often supplemented by more nuanced metrics to capture answer quality in open-ended tasks.

Challenges and Future Directions

Despite significant advances, VQA research faces challenges such as addressing biases in datasets, improving reasoning capabilities, and reducing data requirements for model training. The paper highlights the promise of LVLMs in addressing these issues by using pre-training techniques that leverage vast datasets, thus enhancing model generalization and robustness.

Looking forward, the authors suggest focusing on developing models with real-time capabilities, ensuring scalability, and improving interpretability. Additionally, there's a call for more specialized datasets in healthcare and education to push the boundaries of what VQA systems can achieve effectively.

Implications

This comprehensive survey underscores VQA's potential applications across various domains, affirming its role in advancing AI technologies that interact naturally with humans. By tackling current limitations and embracing innovations in multimodal learning, VQA systems are poised to become integral tools in fields requiring complex reasoning and human-like proficiency in understanding visual and textual information. The paper serves as a valuable resource for researchers and practitioners, offering a thorough understanding of VQA's landscape and pointing to directions for future exploration.