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.

本文介绍了一种利用人体姿态提示的多层次关系检测策略，通过深度学习的方法将人体姿态融入到三个语义层面的关系表示中，以解决人体场景中精细化的物体交互识别问题。实验证明本方法在公共基准测试中表现优异。