mlsanityData to 7 Oct 2026

MogaNet: Multi-order Gated Aggregation Network

arXiv 2211.03295 ↗First posted 7 Nov 2022cs.CV cs.AIcited by 59

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Abstract (arXiv)

By contextualizing the kernel as global as possible, Modern ConvNets have shown great potential in computer vision tasks. However, recent progress on 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 https://github.com/Westlake-AI/MogaNet.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). Builds on 131 of its 158 references that are in the map. First posted on arXiv 7 Nov 2022.

Builds on

131 of 158 references.

References · 131

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50 most cited of 131 All 131 (JSON)

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Citing papers per year: 2023: 8; 2024: 18; 2025: 24; 2026: 9.

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  31. 2025A2Mamba: Attention-augmented State Space Models for Visual Recognitioncited by 1
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  36. 2024RecConv: Efficient Recursive Convolutions for Multi-Frequency Representationscited by 0
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  38. 2025Expressive yet Efficient Feature Expansion with Adaptive Cross-Hadamard Productscited by 0

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