  Detecting human-object interactions (HOIs) is an intricate challenge in the
field of computer vision. Existing methods for HOI detection heavily rely on
appearance-based features, but these may not fully capture all the essential
characteristics necessary for accurate detection. To overcome these challenges,
we propose an innovative graph-based approach called TMGHOI (Translational
Model for Human-Object Interaction Detection). Our method effectively captures
the sentiment representation of HOIs by integrating both spatial and semantic
knowledge. By representing HOIs as a graph, where the interaction components
serve as nodes and their spatial relationships as edges. To extract crucial
spatial and semantic information, TMGHOI employs separate spatial and semantic
encoders. Subsequently, these encodings are combined to construct a knowledge
graph that effectively captures the sentiment representation of HOIs.
Additionally, the ability to incorporate prior knowledge enhances the
understanding of interactions, further boosting detection accuracy. We
conducted extensive evaluations on the widely-used HICO-DET datasets to
demonstrate the effectiveness of TMGHOI. Our approach outperformed existing
state-of-the-art graph-based methods by a significant margin, showcasing its
potential as a superior solution for HOI detection. We are confident that
TMGHOI has the potential to significantly improve the accuracy and efficiency
of HOI detection. Its integration of spatial and semantic knowledge, along with
its computational efficiency and practicality, makes it a valuable tool for
researchers and practitioners in the computer vision community. As with any
research, we acknowledge the importance of further exploration and evaluation
on various datasets to establish the generalizability and robustness of our
proposed method.
