LXMERT is a framework designed to learn cross-modal representations between vision and language using a Transformer-based model. The framework includes three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. It is pre-trained on a large dataset with five tasks: masked language modeling, masked object prediction, cross-modality matching, and image question answering. These tasks help the model learn both intra-modal and cross-modal relationships. After pre-training, the model achieves state-of-the-art results on visual question answering datasets like VQA and GQA. It also performs well on the challenging visual reasoning task NLVR², improving previous results by 22% in accuracy. The model's effectiveness is supported by ablation studies and attention visualizations. The framework is compared with BERT and other models, showing that its cross-modal pre-training strategies significantly contribute to its performance. The model is pre-trained on a large-scale dataset of image-and-sentence pairs and fine-tuned for various tasks, demonstrating strong generalization and performance across different visual and language reasoning tasks.LXMERT is a framework designed to learn cross-modal representations between vision and language using a Transformer-based model. The framework includes three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. It is pre-trained on a large dataset with five tasks: masked language modeling, masked object prediction, cross-modality matching, and image question answering. These tasks help the model learn both intra-modal and cross-modal relationships. After pre-training, the model achieves state-of-the-art results on visual question answering datasets like VQA and GQA. It also performs well on the challenging visual reasoning task NLVR², improving previous results by 22% in accuracy. The model's effectiveness is supported by ablation studies and attention visualizations. The framework is compared with BERT and other models, showing that its cross-modal pre-training strategies significantly contribute to its performance. The model is pre-trained on a large-scale dataset of image-and-sentence pairs and fine-tuned for various tasks, demonstrating strong generalization and performance across different visual and language reasoning tasks.