  Most existing attention prediction research focuses on salient instances like
humans and objects. However, the more complex interaction-oriented attention,
arising from the comprehension of interactions between instances by human
observers, remains largely unexplored. This is equally crucial for advancing
human-machine interaction and human-centered artificial intelligence. To bridge
this gap, we first collect a novel gaze fixation dataset named IG, comprising
530,000 fixation points across 740 diverse interaction categories, capturing
visual attention during human observers cognitive processes of interactions.
Subsequently, we introduce the zero-shot interaction-oriented attention
prediction task ZeroIA, which challenges models to predict visual cues for
interactions not encountered during training. Thirdly, we present the
Interactive Attention model IA, designed to emulate human observers cognitive
processes to tackle the ZeroIA problem. Extensive experiments demonstrate that
the proposed IA outperforms other state-of-the-art approaches in both ZeroIA
and fully supervised settings. Lastly, we endeavor to apply
interaction-oriented attention to the interaction recognition task itself.
Further experimental results demonstrate the promising potential to enhance the
performance and interpretability of existing state-of-the-art HOI models by
incorporating real human attention data from IG and attention labels generated
by IA.
