Core Concepts
The author introduces PEM, a transformer-based architecture addressing efficiency challenges in image segmentation tasks by leveraging prototype-based cross-attention and an efficient multi-scale feature pyramid network.
PEM demonstrates outstanding performance in both semantic and panoptic segmentation tasks on various datasets.
Abstract
The content introduces PEM, a novel architecture for image segmentation that focuses on efficiency. It proposes a prototype-based cross-attention mechanism and an efficient multi-scale feature pyramid network to improve performance while reducing computational demands. The architecture is benchmarked on Cityscapes and ADE20K datasets, showcasing superior results compared to existing models. PEM offers a significant step towards efficient image segmentation methodologies with high performance.
Key points:
- Introduction of Prototype-based Efficient MaskFormer (PEM) for image segmentation.
- Focus on efficiency through prototype-based cross-attention and multi-scale feature pyramid network.
- Benchmarking on Cityscapes and ADE20K datasets with outstanding results.
- Contribution to the field of efficient image segmentation methodologies.
Stats
"PEM demonstrates outstanding performance on every task and dataset."
"PEM exhibits outstanding performance, showcasing similar or superior results compared to the computationally expensive baselines."
"PEM achieves a mIoU of 79.9 at 13.8 FPS."
"PEM attains a PQ of 38.5 at 35.7 FPS."