Title: Cube Kernel: Enabling Local Gradient Flow Across Channels in CNNs for Robust and Efficient Building Segmentation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Cube Kernel, channel-wise convolution, inter-channel gradient flow, convolutional neural networks, building rooftop extraction, semantic segmentation, remote sensing

Abstract: Understanding inter-band and cross-channel relationships is fundamental to human color perception and object recognition. However, conventional CNN-based image processing fails to propagate local gradients across channels during backpropagation, limiting cross-channel interactions until the summation stage. To address this limitation, we develop Cube Kernel, a plug-and-play convolutional operator that enforces local cross-channel gradient coupling by mapping channels onto a finer spatial lattice through group-based re-construction. To further enhance flexibility in feature grouping, we introduce a learnable router that adaptively reorders channels and emphasizes task-relevant representations. This is combined with a spatial attention mechanism, which effectively suppresses redundant responses introduced during reconstruction. Across multiple CNN-based and Transformer-based backbones, Cube Kernel delivers consistent improvements on the WBD, WHU, and Inria datasets. ConvNeXt-U-Cube achieves an F1 score of 90.42% and an mIoU of 82.63% on Inria, surpassing recent state-of-the-art methods with reductions of 9.2% and 20.8% in parameters and GFLOPs, respectively. Ablations isolate the effect of re-construction, the router, and attention, and analysis shows stronger inter-channel decorrelation. Owing to its lightweight design, compatibility with diverse architectures, and ability to be stacked across layers, Cube Kernel is highly implantable and can serve as a strong default operator for structured channel mixing in dense prediction. All code will be made publicly available.

TL;DR: We propose Cube Kernel, a lightweight plug-and-play convolutional operator that enforces local cross-channel gradient coupling, achieving state-of-the-art building extraction performance with fewer parameters and FLOPs.