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Eagle Vision Transformer (EViT): A Bionic Backbone Network Inspired by Eagle's Bi-Fovea Visual System


Core Concepts
The authors propose a novel Eagle Vision Transformer (EViT) that combines the advantages of convolution and vision transformers, inspired by the unique bi-fovea physiological structure and visual properties of eagle eyes. EViT exhibits highly competitive performance and computational efficiency across various computer vision tasks.
Abstract
The authors present the Eagle Vision Transformer (EViT), a novel backbone network for computer vision tasks that is inspired by the bi-fovea visual system of eagle eyes. Key highlights: The authors propose a Bi-Fovea Self-Attention (BFSA) module that simulates the shallow and deep fovea of eagle vision, enabling the network to learn feature representations from coarse to fine. They introduce a Bi-Fovea Feedforward Network (BFFN) that mimics the hierarchical and parallel information processing of the biological visual cortex. The authors design a Bionic Eagle Vision (BEV) block that combines BFSA and BFFN, and use it to build a general pyramid backbone network family called EViTs. Experiments show that EViTs achieve highly competitive performance and computational efficiency compared to other state-of-the-art vision transformer models across tasks like image classification, object detection, and semantic segmentation. The authors demonstrate the potential of combining eagle vision with vision transformers, and show that EViTs can bring significant performance improvements in computer vision.
Stats
EViT-Tiny achieves 79.9% top-1 accuracy on ImageNet-1K with 1.91 GFLOPs. EViT-Base achieves 83.9% top-1 accuracy on ImageNet-1K with 6.35 GFLOPs. EViT-Large achieves 84.9% top-1 accuracy on ImageNet-1K with 12.5 GFLOPs. EViT-Small and EViT-Base outperform other backbones by at least 0.4% and 0.5% in object detection and instance segmentation on COCO 2017. EViT-Small and EViT-Base achieve 46.1% and 48.5% mIoU on ADE20K semantic segmentation, outperforming PVT by at least 0.9%.
Quotes
"Benefiting from biological eagle vision, we propose a novel Bi-Fovea Self-Attention (BFSA). It used to simulate the shallow and deep fovea of eagle vision, prompting the network to learn the feature representation of targets from coarse to fine." "Taking inspiration from neuroscience, we continue the bi-fovea structure design principle of eagle vision, introduce a Bi-Fovea Feedforward Network (BFFN), and design a Bionic Eagle Vision (BEV) block based on the BFSA and BFFN." "Following the hierarchical design concept, we propose a general and efficient pyramid backbone network family called EViTs. In terms of computational efficiency and performance, EViTs show significant competitive advantages compared with other counterparts."

Key Insights Distilled From

by Yulong Shi,M... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2310.06629.pdf
EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention

Deeper Inquiries

How can the bi-fovea design principle of eagle vision be further extended or generalized to other neural network architectures beyond vision transformers

The bi-fovea design principle of eagle vision can be further extended or generalized to other neural network architectures beyond vision transformers by incorporating the concept of hierarchical and parallel information processing. This design principle can be applied to various types of neural networks, such as recurrent neural networks (RNNs) and graph neural networks (GNNs), to enhance their ability to capture global dependencies and local details in data. By structuring the network in a way that mimics the shallow and deep fovea of eagle vision, models can learn feature representations from coarse to fine, improving their performance in tasks that require both global context and fine-grained details. Additionally, the idea of combining attention mechanisms with convolutional layers inspired by the bi-fovea structure can be applied to different network architectures to enhance their capabilities in processing complex data patterns.

What are the potential limitations or drawbacks of the BFSA and BFFN modules, and how could they be addressed in future work

The potential limitations or drawbacks of the BFSA and BFFN modules include the risk of overfitting due to the complex structure and increased number of parameters introduced by these modules. To address this, regularization techniques such as dropout or weight decay can be applied during training to prevent overfitting. Additionally, the computational complexity of the BFSA and BFFN modules may pose challenges in terms of efficiency, especially when scaling up to larger models or datasets. One way to mitigate this is to explore more efficient implementations or optimizations of the BFSA and BFFN modules, such as using sparse attention mechanisms or reducing the number of parameters in the feedforward networks. Furthermore, conducting thorough hyperparameter tuning and model optimization can help improve the performance and efficiency of the BFSA and BFFN modules in future work.

Given the inspiration from eagle vision, are there other biological visual systems that could provide insights for improving computer vision models

Inspired by the success of eagle vision, other biological visual systems that could provide insights for improving computer vision models include the visual systems of other animals with unique visual capabilities. For example, the compound eyes of insects, such as bees and dragonflies, could offer insights into designing models with enhanced spatial resolution and motion detection capabilities. Additionally, the visual systems of nocturnal animals, like owls and cats, could inspire the development of models that are robust to low-light conditions and capable of detecting subtle visual cues. By studying and drawing inspiration from a diverse range of biological visual systems, researchers can explore novel approaches to improving computer vision models in various domains and applications.
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