  For humans, understanding the relationships between objects using visual
signals is intuitive. For artificial intelligence, however, this task remains
challenging. Researchers have made significant progress studying semantic
relationship detection, such as human-object interaction detection and visual
relationship detection. We take the study of visual relationships a step
further from semantic to geometric. In specific, we predict relative occlusion
and relative distance relationships. However, detecting these relationships
from a single image is challenging. Enforcing focused attention to
task-specific regions plays a critical role in successfully detecting these
relationships. In this work, (1) we propose a novel three-decoder architecture
as the infrastructure for focused attention; 2) we use the generalized
intersection box prediction task to effectively guide our model to focus on
occlusion-specific regions; 3) our model achieves a new state-of-the-art
performance on distance-aware relationship detection. Specifically, our model
increases the distance F1-score from 33.8% to 38.6% and boosts the occlusion
F1-score from 34.4% to 41.2%. Our code is publicly available.
