Advancements in Vietnamese Visual Question Answering

Visual Question Answering (VQA) is a task that combines natural language and images. The goal is to create a system that can answer Questions based on the content of images or videos. Initially, researchers focused on how machines could identify objects and understand scenes in images. But as technology advanced, it became clear that understanding text in images was also essential. This led to many studies on how VQA models can read and comprehend text, especially in languages like Vietnamese.

#Introduction to ViTextVQA Dataset

In Vietnam, the research on VQA is still developing. To support this field, a new and significant dataset called ViTextVQA was created. This dataset contains over 16,000 images and more than 50,000 questions and answers. It focuses mainly on the text found in images. Through various experiments with advanced models, researchers discovered that the order in which words are processed plays a crucial role in how well the answers are formed. This finding greatly improved the performance of models using the ViTextVQA dataset.

#Growth of Visual Question Answering Research

In recent years, VQA has gained popularity among researchers in computer vision and natural language processing. The rise of powerful chatbots that can answer questions about images has driven the growth of this field. Many datasets have been released, especially in languages like English and Chinese. This has led to continued advancements in VQA, allowing models to learn from diverse data and improve their capabilities.

VQA models require a good understanding of both images and questions to provide relevant answers. They need to manage different types of information and be able to make sense of visual content and the meaning of questions.

#Vietnamese Context: Building ViVQA Dataset

In an effort to study VQA in Vietnamese, the ViVQA dataset was created as the first dataset for this task in the language. Although it contained a reasonable number of samples, its quality and effectiveness were insufficient. Researchers later released the OpenViVQA dataset, which allowed for more open-ended questions and answers. This introduced a new direction for research, but the dataset also faced limitations, particularly in dealing with scene text.

To overcome these issues, the ViTextVQA dataset was developed, focusing on extracting information from text in images and enhancing VQA models' abilities to handle text. This dataset serves as a valuable resource for evaluating and improving VQA models in the context of the Vietnamese language.

#Contributions of ViTextVQA Dataset

The ViTextVQA dataset presents several key contributions:

1. It is the first large-scale dataset tailored for text-based VQA tasks in Vietnamese, focusing on both scene text and text within images.
2. The dataset allows researchers to analyze the challenges of VQA models when processing OCR text, paving the way for improved performance.
3. Extensive experiments showed that using a specific language model as a backbone for VQA can be very effective, especially when the OCR text is carefully arranged.

#Related Work and Previous Datasets

Numerous large-scale VQA datasets have been developed, primarily in English. These datasets provide crucial resources that inspire the creation of the ViTextVQA dataset. Examples include the DAQUAR dataset, VQA v1 and v2 datasets, the TextVQA dataset, and others aimed at addressing the shortcomings of previous models.

In Vietnamese, there have been efforts to build VQA datasets like ViVQA and EVJVQA, but these still have certain limitations. The development of ViTextVQA aims to fill gaps by incorporating scene text and improving overall dataset quality.

#Methodology for Creating ViTextVQA Dataset

Creating the ViTextVQA dataset involved a systematic approach:

1. Image Collection: Images were gathered from various online sources and through manual photography to ensure diversity and quality.
2. Annotation Process: Native speakers annotated the images, generating question-answer pairs based on the text present in the images. This process was carefully monitored to maintain quality.
3. Quality Assurance: A rigorous review process helped eliminate errors and ensure the data met high standards.

The final dataset includes a collection of images representing various scenarios and objects, along with relevant questions and answers derived from the text within those images.

#Detailed Analysis of the Dataset

The ViTextVQA dataset consists of varied visual scenes along with their corresponding question-answer pairs. This diversity enables models to learn how to respond accurately to questions based on the content of images. By studying the lengths of questions and answers, as well as the distribution of parts of speech, researchers can gain valuable insights into the structure and use of language within the dataset.

In terms of objects found in the images, common entries include people, signs, letters, and various everyday items. Such a rich variety mirrors real-life situations and helps push the VQA task boundaries further.

#Evaluation of Visual Question Answering Models

Several VQA models were tested using the ViTextVQA dataset. Each model displayed different strengths and weaknesses, highlighting the importance of selecting the right approach for the task.

The assessments focused on metrics like Exact Match (EM) and F1-Score to gauge the overall performance of models on the dataset. Through these evaluations, it became clear that advanced language models specifically fine-tuned for Vietnamese can significantly enhance the effectiveness of VQA tasks.

#Impact of OCR Text in VQA

The experiments conducted revealed that models benefit greatly from OCR text when answering questions. For instance, when questions were supplemented with OCR text, the models consistently showed better performance. This emphasizes the importance of using comprehensive data sources to enhance model accuracy and efficiency.

Additionally, the arrangement of OCR text plays a critical role. Specifically, organizing text from the top-left to the bottom-right improved the understanding of text by the models, leading to better results.

#Analysis of Answer and Question Length

The length of questions and answers also affects model performance. For shorter answers, models tend to perform better. As the length increases, there is often a drop in the accuracy of responses. For questions, the trends indicate that shorter questions lead to higher F1-Scores, while longer ones may lead to varying performance levels.

Understanding how length impacts performance can help inform future model designs and training methodologies.

#Challenges and Future Directions

While the ViTextVQA dataset and the research around it demonstrate promising results, there are still challenges to face. The performance of models remains lower than expected, indicating that continuous work is necessary to overcome these hurdles.

Going forward, one potential avenue is to leverage the dataset for generating questions about images. This could enhance not only VQA tasks but also related applications such as chatbots capable of engaging users more effectively.

#Conclusion

The ViTextVQA dataset represents a significant step forward for VQA research in Vietnamese. By focusing on the unique challenges posed by this language and its specific characteristics, researchers can develop models that improve the accuracy and relevance of answers to visual questions. The insights gained from working with this dataset can influence future work and provide valuable resources for optimizing VQA tasks.