iiANET: Inception Inspired Attention Hybrid Network for efficient Long-Range Dependency

• URL: http://arxiv.org/abs/2407.07603v2
• Date: Sat, 12 Apr 2025 11:32:38 GMT
• Title: iiANET: Inception Inspired Attention Hybrid Network for efficient Long-Range Dependency
• Authors: Haruna Yunusa, Qin Shiyin, Abdulrahman Hamman Adama Chukkol, Adamu Lawan, Abdulganiyu Abdu Yusuf, Isah Bello,
• Abstract summary: We present iiANET, an efficient hybrid visual backbone designed to improve the modeling of long-range dependen-cies.<n>The core innovation of iiANET is the iiABlock, a unified building block that in-tegrates global r-MHSA (Multi-Head Self-Attention) and convolutional layers in paral-lel.
• Score: 0.5497663232622965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent emergence of hybrid models has introduced a transformative approach to computer vision, gradually moving beyond conventional convolutional neural net-works and vision transformers. However, efficiently combining these two paradigms to better capture long-range dependencies in complex images remains a challenge. In this paper, we present iiANET (Inception Inspired Attention Network), an efficient hybrid visual backbone designed to improve the modeling of long-range dependen-cies. The core innovation of iiANET is the iiABlock, a unified building block that in-tegrates global r-MHSA (Multi-Head Self-Attention) and convolutional layers in paral-lel. This design enables iiABlock to simultaneously capture global context and local details, making it highly effective for extracting rich and diverse features. By effi-ciently fusing these complementary representations, iiABlock allows iiANET to achieve strong feature interaction while maintaining computational efficiency. Exten-sive qualitative and quantitative evaluations across various benchmarks show im-proved performance over several state-of-the-art models.

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