  Reasoning human object interactions is a core problem in human-centric scene
understanding and detecting such relations poses a unique challenge to vision
systems due to large variations in human-object configurations, multiple
co-occurring relation instances and subtle visual difference between relation
categories. To address those challenges, we propose a multi-level relation
detection strategy that utilizes human pose cues to capture global spatial
configurations of relations and as an attention mechanism to dynamically zoom
into relevant regions at human part level. Specifically, we develop a
multi-branch deep network to learn a pose-augmented relation representation at
three semantic levels, incorporating interaction context, object features and
detailed semantic part cues. As a result, our approach is capable of generating
robust predictions on fine-grained human object interactions with interpretable
outputs. Extensive experimental evaluations on public benchmarks show that our
model outperforms prior methods by a considerable margin, demonstrating its
efficacy in handling complex scenes.
