VideoQA Benchmark

Video Question Answering (VideoQA) benchmarks evaluate models' ability to understand and reason about videos by answering questions about their content. Current research focuses on improving model performance through novel architectures like multimodal transformers and recurrent memory networks, often incorporating large language models (LLMs) for enhanced reasoning and addressing data limitations via self-training or efficient input processing techniques. These advancements aim to create more robust and efficient VideoQA systems, impacting fields like video retrieval, content analysis, and human-computer interaction by enabling more sophisticated understanding of visual information. The development of comprehensive benchmarks, encompassing diverse video types and reasoning tasks, is crucial for driving progress in this area.