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，我们还能预测这些物体可能发生的潜在未来交互或动作。本文研究短时物体交互预测（STA）问题，并提出NAOGAT（下一活跃物体引导预测Transformer）——一种多模态端到端Transformer网络。该网络通过关注观测帧中的物体来预测下一活跃物体（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、与之交互的物体以及交互开始时间（即接触时间TTC）。与现有用于动作预测的视频建模架构相比，NAOGAT能够捕捉物体与全局场景上下文之间的关系，从而预测下一活跃物体的检测结果，并基于这些检测结果预测相关的未来动作，利用物体动态特性提升预测精度。事实上，我们方法的核心优势之一在于能够利用给定片段中物体的运动动态（这常被其他模型忽视），并分别解码以物体为中心和以运动为中心的信息。实验表明，我们的模型在两个独立数据集（Ego4D和EpicKitchens-100的"未见集"）上，通过接触时间、下一活跃物体定位等多项附加指标衡量，均优于现有方法。代码将在接收后公开。