Objects are crucial for understanding human-object interactions. By identifying the relevant objects, one can also predict potential future interactions or actions that may occur with these objects. In this paper, we study the problem of Short-Term Object interaction anticipation (STA) and propose NAOGAT (Next-Active-Object Guided Anticipation Transformer), a multi-modal end-to-end transformer network, that attends to objects in observed frames in order to anticipate the next-active-object (NAO) and, eventually, to guide the model to predict context-aware future actions. The task is challenging since it requires anticipating future action along with the object with which the action occurs and the time after which the interaction will begin, a.k.a. the time to contact (TTC). Compared to existing video modeling architectures for action anticipation, NAOGAT captures the relationship between objects and the global scene context in order to predict detections for the next active object and anticipate relevant future actions given these detections, leveraging the objects' dynamics to improve accuracy. One of the key strengths of our approach, in fact, is its ability to exploit the motion dynamics of objects within a given clip, which is often ignored by other models, and separately decoding the object-centric and motion-centric information. Through our experiments, we show that our model outperforms existing methods on two separate datasets, Ego4D and EpicKitchens-100 ("Unseen Set"), as measured by several additional metrics, such as time to contact, and next-active-object localization. The code will be available upon acceptance.

翻译：物体对于理解人-物交互至关重要。通过识别相关物体，可以预测可能发生的潜在未来交互或动作。本文研究短期物体交互预测（STA）问题，提出NAOGAT（下一活动物体引导预测Transformer），这是一种多模态端到端Transformer网络，通过关注观测帧中的物体来预测下一活动物体（NAO），并最终引导模型预测上下文感知的未来动作。该任务具有挑战性，因为需要同时预测未来动作、与动作交互的物体以及交互开始时间（即接触时间TTC）。相较于现有动作预测视频建模架构，NAOGAT捕捉物体与全局场景上下文之间的关系，预测下一活动物体的检测结果，并基于这些检测结果预测相关未来动作，利用物体动态提升准确性。我们的方法核心优势在于能利用给定片段中物体运动动态（常被其他模型忽略），并分别解码物体中心与运动中心信息。实验表明，在Ego4D和EpicKitchens-100（"未见过集"）两个数据集上，我们的模型在接触时间、下一活动物体定位等多项附加指标上均优于现有方法。代码将在论文被接收后公开。