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Question answering systems in the context of [vague] machine reading applications have also been constructed in the medical domain, for instance related to [vague] Alzheimer's disease. [3] Open-domain question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for ...
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
The high-level architecture of IBM's DeepQA used in Watson [9]. Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.
New series of AI models are designed to help with complex tasks and harder problems, the company said OpenAI reveals new artificial intelligence tool it claims can think like a human before ...
In 2015, Parikh and her students at Virginia Tech worked on AI for Visual Question Answering (VQA). This technology allows users to ask questions about pictures, e.g. "Is this a vegetarian pizza?" [2] [3] Parikh's VQA dataset has been used to evaluate over 30 AI models. [4] In 2017, Parikh published a conversational agent called ParlAI. [5]
In visual question answering, the model might first generate a plausible but incorrect answer based on a superficial understanding. Through reflection, it could identify inconsistencies between its answer and image details, leading to a revised, more accurate response. [27]