How does the performance of multimodal models on Visual Genome benchmark tasks vary when trained with differen

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked ``What vehicle is the person riding?''

Research goal: How does the performance of multimodal models on Visual Genome benchmark tasks vary when trained with different vision-language pretraining objectives, measured by caption generation BLEU scores and visual question answering accuracy across object, attribute, and relationship prediction subtasks?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.