# AFINet: Attentive Feature Integration Networks for Image Classification

Source: [AFINet: Attentive Feature Integration Networks for Image Classification](<https://www.sota2.com/research/paper/68fa69e71e209131cea24be9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:07:38.446Z

Authors: Xinglin Pan, Jing Xu, Yu Pan, liangjian Wen, WenXiang Lin, Kun Bai, Zenglin Xu

Published: 2021-05-10T13:40:11.000Z

## Abstract

Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Recent advanced models in CNNs, such as ResNets, mainly focus on the skip connection to avoid gradient vanishing. DenseNet designs suggest creating additional bypasses to transfer features as an alternative strategy in network design. In this paper, we design Attentive Feature Integration (AFI) modules, which are widely applicable to most recent network architectures, leading to new architectures named AFI-Nets. AFI-Nets explicitly model the correlations among different levels of features and selectively transfer features with a little overhead.AFI-ResNet-152 obtains a 1.24% relative improvement on the ImageNet dataset while decreases the FLOPs by about 10% and the number of parameters by about 9.2% compared to ResNet-152.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2105.04354>)

[PDF](<https://arxiv.org/pdf/2105.04354.pdf>)