Visual Commonsense Tests

The Visual Commonsense Tests are a subtask in the field of Natural Language Processing, aimed at evaluating the model's understanding of common sense in visual scenes. This task is accomplished by predicting five types of attributes (color, shape, material, size, and visual co-occurrence) for over 5,000 subjects, with the goal of enhancing the model’s reasoning and judgment capabilities in complex visual environments, and improving its robustness and generalization in real-world applications.