Contextual Cue

Contextual cues, encompassing various forms of surrounding information, are crucial for improving the accuracy and robustness of numerous AI systems. Current research focuses on how different model architectures, such as transformers and vision-language models (VLMs), prioritize and utilize these cues in tasks ranging from natural language processing and human-robot interaction to image retrieval and emotion recognition. This work highlights the importance of effectively integrating contextual information to enhance model performance and address limitations like ambiguity resolution and bias mitigation, leading to more reliable and human-like AI systems across diverse applications.