Deep Object Occlusion Relationship Detection Based on Associative Embedding Clustering

Visual relationship detection is crucial for understanding scenes depicted in images when aiming to detect objects within the image and recognize the visual relationships between each pair of objects. Nevertheless, profound occlusion, as a typical visual relationship existing between objects and constituting a pivotal semantic feature, has regrettably been subjected to insufficient scrutiny. To address this issue, we propose a pioneering approach termed DOORD-AEC, which is specifically designed for detecting occlusion spatial relationships among targets. DOORD-AEC introduces associative embedding clustering to supervise a convolutional neural network with two branches, enabling it to take in an input image and produce a triplet set representing occlusion spatial relationships. The network learns to simultaneously identify all of the targets and occlusions that make up the triplet set and group them together using associative embedding clustering. Additionally, we contribute the KORD dataset, which is a novel and challenging dataset for occlusion spatial relationships among targets. We demonstrate the effectiveness of our DOORD-AEC method using this dataset.