The field of causal video question answering (QA) is rapidly evolving, but there’s a noticeable lack of depth in causal reasoning within current datasets. To bridge this gap, researchers have turned to the animated world of cartoons, specifically the classic “Tom and Jerry” series, to create a new and challenging dataset named Causal Chaos!. This dataset is designed to probe deeper into the causal reasoning process with its complex questions and layered answers.

Causal Chaos! stands out because it incorporates extended causal chains that are intricately woven into the dynamic interactions and visuals of the cartoons. Moreover, the established principles of animation provide a clear and unambiguous framework for causal relationships, making the dataset not only more challenging but also well-defined for models to interpret.

To further enhance the dataset’s complexity, the researchers have implemented a technique called hard negative mining, which includes a version focused on Casual Confusion. This approach ensures that while models may perform adequately, there remains significant scope for enhancement, particularly with open-ended responses. The dataset highlights the need for more sophisticated methods in modeling explicit causal relationships and the integration of visual and linguistic elements.

As researchers continue to delve into these areas, Causal Chaos!—alongside other complementary datasets—promises to be a catalyst for advancement in the field.
In a move to support ongoing research and development, the creators of Causal Chaos! plan to make the dataset, along with the associated codes and models, publicly available, thereby contributing valuable resources to the community and fostering further innovation in causal video question answering.