Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention
Panoptic segmentation in complex scenes remains challenging because of occlusions, yet modern approaches often neglect occlusion modelling. In this paper, we propose \textbf{P}osition \textbf{E}mbedding \textbf{M}odulation with \textbf{O}cclusion-\textbf{L}evel \textbf{A}ttention (PEMOLA), a novel occlusion-aware module that can be seamlessly integrated into transformer-based panoptic segmentation. To obtain occlusion cues, we train an occlusion classifier on the COCO-OLAC dataset. The classifier derives the occlusion-level attention, which serves as spatial guidance, while the occlusion label
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- LinkedLinked via arxiv author · 85%Wenbo Wei →
“Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention”
- LinkedLinked via arxiv author · 85%Wenjun Wang →
“Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention”
- LinkedLinked via arxiv author · 85%Shan Raza →
“Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention”
- LinkedLinked via arxiv author · 85%Abhir Bhalerao →
“Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention”