Text Based Cue

Text-based cues are increasingly used to improve various machine learning tasks by incorporating textual information alongside other data modalities. Current research focuses on leveraging these cues for weakly-supervised learning in image segmentation and time series forecasting, often employing attention mechanisms and large language models to effectively fuse textual and visual or temporal data. This approach shows promise in reducing annotation costs for computationally expensive tasks, improving model performance, and enabling new applications such as privacy-preserving speaker extraction and enhanced multimodal understanding in areas like emotion recognition and moral judgment analysis. The integration of textual cues represents a significant advancement in multimodal learning, offering improved accuracy and efficiency across diverse fields.