VisualBERT is a simple and flexible framework for vision-and-language tasks, combining BERT with object detection to process text and images jointly. It uses a stack of Transformer layers to align text and image regions through self-attention. Two pre-training objectives are introduced: masked language modeling and sentence-image prediction, using image caption data. VisualBERT outperforms or matches state-of-the-art models on tasks like VQA, VCR, NLVR2, and Flickr30K. It can ground language elements to image regions without explicit supervision and is sensitive to syntactic relationships. Experiments show that pre-training on image captions improves text and visual representations. VisualBERT is trained on COCO captions and fine-tuned for various tasks. It uses a multi-layer Transformer to process text and image features together, allowing the model to capture intricate associations between text and image. The model is evaluated on four vision-and-language tasks, demonstrating strong performance. Ablation studies show that task-agnostic pre-training and early fusion of vision and language are crucial for performance. VisualBERT's attention mechanism enables it to align text and image regions effectively, even without direct supervision. The model's performance is validated across multiple tasks, showing its effectiveness in understanding detailed image semantics.VisualBERT is a simple and flexible framework for vision-and-language tasks, combining BERT with object detection to process text and images jointly. It uses a stack of Transformer layers to align text and image regions through self-attention. Two pre-training objectives are introduced: masked language modeling and sentence-image prediction, using image caption data. VisualBERT outperforms or matches state-of-the-art models on tasks like VQA, VCR, NLVR2, and Flickr30K. It can ground language elements to image regions without explicit supervision and is sensitive to syntactic relationships. Experiments show that pre-training on image captions improves text and visual representations. VisualBERT is trained on COCO captions and fine-tuned for various tasks. It uses a multi-layer Transformer to process text and image features together, allowing the model to capture intricate associations between text and image. The model is evaluated on four vision-and-language tasks, demonstrating strong performance. Ablation studies show that task-agnostic pre-training and early fusion of vision and language are crucial for performance. VisualBERT's attention mechanism enables it to align text and image regions effectively, even without direct supervision. The model's performance is validated across multiple tasks, showing its effectiveness in understanding detailed image semantics.