Object detection and segmentation with a simple transformer using scale-aware attention with SimPLR
Object detection and segmentation with a simple transformer using scale-aware attention with SimPLR
SimPLR: A Simple and Plain Transformer for Object Detection and Segmentation
arXiv paper abstract https://arxiv.org/abs/2310.05920
arXiv PDF paper https://arxiv.org/pdf/2310.05920.pdf
The ability to detect objects in images at varying scales has played a pivotal role in the design of modern object detectors.
Despite considerable progress in removing handcrafted components using transformers, multi-scale feature maps remain a key factor for their empirical success, even with a plain backbone like the Vision Transformer (ViT).
... show that this reliance on feature pyramids is unnecessary and a transformer-based detector with scale-aware attention enables the plain detector `SimPLR' whose backbone and detection head both operate on single-scale features.
The plain architecture allows SimPLR to effectively take advantages of self-supervised learning and scaling approaches with ViTs, yielding strong performance compared to multi-scale counterparts.
... SimPLR indicates better performance than end-to-end detectors (Mask2Former) and plain-backbone detectors (ViTDet), while consistently being faster ...
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