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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



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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