Asymmetric Non-local Neural Networks (ANN)

Asymmetric Non-local Neural Networks (ANN) represent a critical evolution in computer vision architecture, specifically designed to address the computational bottleneck of standard non-local blocks. While traditional non-local modules calculate dense affinity matrices resulting in quadratic complexity (O(N^2)), ANN utilizes two key modules—the Asymmetric Pyramid Non-local (APNB) and the Fusion Video Non-local (AFNB) blocks. These modules significantly reduce the number of keys and values by sampling a subset of representative points through spatial pyramid pooling. By 2026, ANN's architectural principles have become foundational in the deployment of real-time semantic segmentation and video action recognition models on edge devices. The architecture allows for the capture of long-range spatial and temporal dependencies without the prohibitive memory overhead previously associated with self-attention in visual tasks. It serves as a middle ground between the exhaustive global context of transformers and the local efficiency of traditional CNNs, making it a preferred choice for high-resolution medical imaging and autonomous vehicle perception systems.