Abstract
Deep Geometric Moments (DGMs) have been shown to encode shape-aware representations in image features. In this work, we revisit the DGM framework through the lens of ConvNeXt, a modern convolutional network (ConvNet) architecture. By leveraging features extracted from ConvNeXt, we improve classification accuracy while further strengthening the geometric shape-awareness of DGMs. Our results demonstrate that features from modern ConvNet backbones serve as compatible stems for training Deep Geometric Moments, and that the learned representations remain tightly aligned with object geometry while exhibiting robustness to visual perturbations. We quantitatively characterize this shape awareness using geometric metrics such as the Hausdorff distance and the Average Symmetric Surface Distance (ASSD) complemented by Intersection over Union (IoU) to assess regional overlap. Furthermore, we conduct an extensive analysis of the invariance of the learned feature representations under diverse image perturbations, including changes in rotation, brightness, color, and scale. We posit that these shape-aligned features offer significant value not only for traditional computer vision tasks, such as object detection and image segmentation, but also for modern efficient training-free image and video editing methods.
Reza Rahimi Azghan
Grad Research Associate
I am a Ph.D. student at Arizona State University. I work as a Graduate Research Associate at Embedded Machine Intelligence Lab (EMIL) under the supervision of Dr. Hassan Ghasemzadeh.
Hassan Ghasemzadeh
Director
Hassan Ghasemzadeh is an Associate Professor of Biomedical Informatics at Arizona State University (ASU) and a Computer Science Adjunct Faculty at Washington State University (WSU).