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You're building a multimodal model that takes an image and a question as input and outputs an answer (Visual Question Answering - VQA). You find your model is heavily relying on the question type (e.g., 'What color is...' always predicts 'blue') and ignoring the image content. Select TWO of the following techniques that could help mitigate this 'language prior' problem.
Correct Answer: B,D
B and D are the best answers. Using a question-only baseline (B) allows you to directly quantify the model's reliance on language priors and then penalize the model for over-relying on them during training, encouraging it to pay more attention to the image. Balancing the dataset (D) by ensuring an equal number of correct answers for each question type makes it harder for the model to simply predict based on the question type alone. The image encoder shouldn't be replaced as that is needed in the task. More images wouldn't necessarily fix the data imbalance.