Interactive Attention

Interactive attention research focuses on developing AI systems that can selectively attend to relevant information within complex scenes, mirroring human visual attention guided by context and interaction. Current work explores model architectures that incorporate interactive attention mechanisms, often within multi-task learning frameworks, to improve performance in tasks like object recognition, image captioning, and question answering. This research is significant for advancing human-centered AI, particularly in applications requiring nuanced understanding of visual scenes and human-object interactions, such as improving safety and accessibility technologies. The development of large-scale datasets annotated with human gaze patterns is also a key driver of progress in this field.