Title: Parameter-Efficient Invariance via Equivariant Object Detection Networks
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Equivariance, Object Detection, Rotation and Scaling Equivariant Datasets, MNIST Object Detection Datasets, C4 Rotation Group, S Scaling Semigroup

Abstract: Leading convolutional object detection networks like the YOLO series have proven highly effective in generalizing across feature symmetries such as rotation and scale, but often do so at the cost of increased parameter counts that complicate model training and inference.  Following recent work in the field of equivariant convolutions, this work seeks to exploit rotational and scale symmetries present in image data for parameter-efficient invariance.  We propose three object detection models based on the YOLOv9-t (Y9t) architecture that incorporate equivariant convolutions for rotation $\rm{C}_4$-Y9t, scale, $\rm{S}$-Y9t or both $\rm{C}_4\rm{S}$-Y9t.  To directly evaluate rotation- and scale-invariant methods in object detection networks, we propose the novel MNIST Object-Detection (MOD) datasets.  By incorporating equivariant methods into the Y9t network while maintaining a similar number of trainable parameters, mean average precision (mAP) is additively increased by 16.8\%, 15.2\%, and 3\% on unseen rotation, scale, and rotation-scale transformed partitions of the MOD datasets, respectively.  Further, the proposed models generalize to other object detection datasets (COCO 2017), with our best model increasing mAP by 4.9\%.

TL;DR: We propose three convolutional object-detection architectures incorporating rotation and scaling equivariance and demonstrate improved precision on eight complementary datasets.