  By contextualizing the kernel as global as possible, Modern ConvNets have
shown great potential in computer vision tasks. However, recent progress on
\textit{multi-order game-theoretic interaction} within deep neural networks
(DNNs) reveals the representation bottleneck of modern ConvNets, where the
expressive interactions have not been effectively encoded with the increased
kernel size. To tackle this challenge, we propose a new family of modern
ConvNets, dubbed MogaNet, for discriminative visual representation learning in
pure ConvNet-based models with favorable complexity-performance trade-offs.
MogaNet encapsulates conceptually simple yet effective convolutions and gated
aggregation into a compact module, where discriminative features are
efficiently gathered and contextualized adaptively. MogaNet exhibits great
scalability, impressive efficiency of parameters, and competitive performance
compared to state-of-the-art ViTs and ConvNets on ImageNet and various
downstream vision benchmarks, including COCO object detection, ADE20K semantic
segmentation, 2D\&3D human pose estimation, and video prediction. Notably,
MogaNet hits 80.0\% and 87.8\% accuracy with 5.2M and 181M parameters on
ImageNet-1K, outperforming ParC-Net and ConvNeXt-L, while saving 59\% FLOPs and
17M parameters, respectively. The source code is available at
\url{https://github.com/Westlake-AI/MogaNet}.
