Multiple Choice VideoQA

Multiple Choice VideoQA focuses on developing systems that accurately answer questions about video content by selecting the correct answer from a set of options. Current research emphasizes improving model robustness and interpretability, particularly addressing challenges like temporal reasoning, handling diverse question types, and mitigating biases in training data. This involves exploring various architectures, including transformer-based models, graph neural networks, and the integration of large language models, often incorporating techniques like contrastive learning and attention mechanisms to better align visual and textual information. Advances in this field have significant implications for applications such as video indexing, retrieval, and content understanding, as well as advancing our understanding of multimodal reasoning.