IQ-VQA: Intelligent Visual Question Answering

The chapter 'IQ-VQA: Intelligent Visual Question Answering' delves into the intricacies of Visual Question Answering (VQA) systems, where AI models must answer natural language questions based on contextual images. It highlights the challenges posed by language priors in VQA datasets, which can lead to false impressions of model performance. The author presents a cyclic training scheme and an implication generator module to improve the consistency and robustness of VQA models. The framework is model-independent and can be integrated with any VQA architecture. The implication generator introduces linguistic variations in questions to enhance the model's understanding of contextual images. The chapter also introduces a new VQA-Implication dataset for evaluating consistency and robustness, showcasing significant improvements in model performance. The qualitative and quantitative analysis of attention maps further demonstrates the enhanced multi-modal understanding achieved through the proposed framework. The chapter concludes with a discussion on future work, emphasizing the importance of diverse and human-annotated implications for better performance.