Cross Modal AI Model Training to Increase Scope and Build more Comprehensive and Robust Models
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P111Keywords:
Cross-modal AI, machine learning, artificial intelligence, model training, multimodal data, robustness, generalization, deep learning, model development, neural networks, data fusion, transfer learning, feature extraction, AI adaptability, predictive analytics, multimodal learning, cognitive computing, pattern recognition, multimodal models, AI scalability, knowledge representation, reinforcement learning, AI integration, computer vision, natural language processing (NLP), speech recognition, image processing, audio-visual data, context-aware computing, semantic understanding, data synchronization, cross-modal retrievalAbstract
Development of cross-modal AI has spawned a lot of attention due to its capability to fuse information from different data sources like text, images, audio, and videos in a manner that traditional models cannot. Such an approach makes AI systems more capable of comprehending and interacting with the world by using multiple input forms, which then allows them to identify patterns, make predictions, and carry out tasks with higher accuracy and flexibility. By simultaneously feeding models with different data modalities, scientists can build more complete and reliable systems that are able to generalize over a wider range of tasks, thus raising their capabilities in use cases that closely represent the real situation where a mix of different information is required. Cross-modal AI offers a major benefit over single-modal models by enabling more fruitful, more subtle understanding and decision-making, which happens to be the most important aspect for the application in healthcare, autonomous driving, and entertainment sectors. For illustration, an AI that is trained by both the visual and textual data can not only provide a more comprehensive understanding of the image but also generate accurate captions. At the same time, incorporating various data types into one seamless model still carries some problems that have to be solved, such as data alignment, the management of huge and various datasets, and the computational machinery power requirements for training such models
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