ACFn

ACFn, short for Adaptive Convolutional Feature Network, is a family of neural network architectures used in computer vision. The approach extends standard convolutional neural networks by allowing convolutional operations to adapt to the input data, producing spatially varying filters or sampling locations. The goal is to better capture geometric variations and complex textures compared with fixed, shared kernels.

Most implementations introduce a secondary module that predicts per-location filter weights, offsets for sampling points, or attention-based gates. These predictions condition the main feature extractor, enabling receptive fields that vary with content. The resulting networks can be built by replacing some convolutional layers with adaptive counterparts and may incorporate multi-scale branches to handle objects at different sizes.

Advantages include improved accuracy on tasks with high geometric variability and potential parameter efficiency, since adaptive filters can represent diverse patterns without stacking many layers. Drawbacks include higher computational cost, more challenging optimization, and sensitivity to training data quality. Research in ACFn often compares against standard CNN baselines and other adaptive approaches such as deformable convolutions.

Applications of ACFn span image classification, object detection, and semantic segmentation. In practice, ACFn modules are integrated into existing architectures such as residual networks or encoder–decoder structures, where they can yield performance gains with modest increases in compute.

See also: convolutional neural networks, deformable convolution, dynamic filter networks, attention mechanisms.