  Modeling spatial-temporal relations is imperative for recognizing human
actions, especially when a human is interacting with objects, while multiple
objects appear around the human differently over time. Most existing action
recognition models focus on learning overall visual cues of a scene but
disregard informative fine-grained features, which can be captured by learning
human-object relationships and interactions. In this paper, we learn
human-object relationships by exploiting the interaction of their local and
global contexts. We hence propose the Global-Local Interaction Distillation
Network (GLIDN), learning human and object interactions through space and time
via knowledge distillation for fine-grained scene understanding. GLIDN encodes
humans and objects into graph nodes and learns local and global relations via
graph attention network. The local context graphs learn the relation between
humans and objects at a frame level by capturing their co-occurrence at a
specific time step. The global relation graph is constructed based on the
video-level of human and object interactions, identifying their long-term
relations throughout a video sequence. More importantly, we investigate how
knowledge from these graphs can be distilled to their counterparts for
improving human-object interaction (HOI) recognition. We evaluate our model by
conducting comprehensive experiments on two datasets including Charades and
CAD-120 datasets. We have achieved better results than the baselines and
counterpart approaches.
