GeoSieveNet: An Image Classification Network with Geometry-Adaptive Sampling and Hierarchical Feature Sifting

Standard convolution samples features on a fixed grid whose locations do not adapt to the input content. We develop the Geometry-Adaptive Sampling and Hierarchical Feature-Sifting Network (GeoSieveNet) based on a 34-layer residual network (ResNet-34). The rotational deformable convolution module (RDCM) assigns predefined offsets with different orientations to individual channel groups and combines them with input-predicted dynamic offsets to adjust sampling locations. Its feature preservation module (FPM) fuses responses from the fixed-grid and rotational deformable branches. Hierarchical gated attention (HGA) combines directional statistics with local convolutional responses to generate spatial gating weights within each channel group. GeoSieveNet achieved classification accuracies of 96.95%, 81.86%, 94.71%, and 89.79% on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, respectively. Across three independent runs, these results exceeded ResNet-34 under the same training settings by 0.66, 3.06, 4.73, and 5.19 percentage points, respectively. All paired tests were significant at the 0.001 level. Ablation experiments attributed gains of 0.36–1.89 percentage points to FPM alone, with larger gains on datasets exhibiting greater geometric variation. At the same floating-point operation budget as ResNet-34, GeoSieveNet retained an accuracy advantage of 0.54–3.96 percentage points.