SimPLR: A Simple and Plain Transformer for Scaling-Efficient Object Detection and Segmentation

• URL: http://arxiv.org/abs/2310.05920v3
• Date: Fri, 15 Mar 2024 16:47:19 GMT
• Title: SimPLR: A Simple and Plain Transformer for Scaling-Efficient Object Detection and Segmentation
• Authors: Duy-Kien Nguyen, Martin R. Oswald, Cees G. M. Snoek,
• Abstract summary: We show that a transformer-based detector with scale-aware attention enables the plain detector SimPLR' whose backbone and detection head are both non-hierarchical and operate on single-scale features. Compared to the multi-scale and single-scale state-of-the-art, our model scales much better with bigger capacity (self-supervised) models and more pre-training data.
• Score: 49.65221743520028
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 hand-crafted components and simplifying the architecture with transformers, multi-scale feature maps and/or pyramid design remain a key factor for their empirical success. In this paper, we show that this reliance on either feature pyramids or an hierarchical backbone is unnecessary and a transformer-based detector with scale-aware attention enables the plain detector `SimPLR' whose backbone and detection head are both non-hierarchical and operate on single-scale features. We find through our experiments that SimPLR with scale-aware attention is plain and simple, yet competitive with multi-scale vision transformer alternatives. Compared to the multi-scale and single-scale state-of-the-art, our model scales much better with bigger capacity (self-supervised) models and more pre-training data, allowing us to report a consistently better accuracy and faster runtime for object detection, instance segmentation as well as panoptic segmentation. Code will be released.

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