  Short-term action anticipation (STA) in first-person videos is a challenging
task that involves understanding the next active object interactions and
predicting future actions. Existing action anticipation methods have primarily
focused on utilizing features extracted from video clips, but often overlooked
the importance of objects and their interactions. To this end, we propose a
novel approach that applies a guided attention mechanism between the objects,
and the spatiotemporal features extracted from video clips, enhancing the
motion and contextual information, and further decoding the object-centric and
motion-centric information to address the problem of STA in egocentric videos.
Our method, GANO (Guided Attention for Next active Objects) is a multi-modal,
end-to-end, single transformer-based network. The experimental results
performed on the largest egocentric dataset demonstrate that GANO outperforms
the existing state-of-the-art methods for the prediction of the next active
object label, its bounding box location, the corresponding future action, and
the time to contact the object. The ablation study shows the positive
contribution of the guided attention mechanism compared to other fusion
methods. Moreover, it is possible to improve the next active object location
and class label prediction results of GANO by just appending the learnable
object tokens with the region of interest embeddings.
