# Infrared and Visible Image Fusion via Interactive Compensatory Attention Adversarial Learning

arXiv: https://arxiv.org/abs/2203.15337 · HTML: https://mlsanity.com/paper/2203.15337/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 57 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
- [NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models](https://mlsanity.com/paper/2007.00328/) (2020)
- [MDLatLRR: A novel decomposition method for infrared and visible image fusion](https://mlsanity.com/paper/1811.02291/) (2018)

## Built on by (most cited first, 4 of 4)

- [IAIFNet: An Illumination-Aware Infrared and Visible Image Fusion Network](https://mlsanity.com/paper/2309.14997/) (2023)
- [ShadowSense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection from RGB-Thermal Drone Imagery](https://mlsanity.com/paper/2310.16212/) (2023)
- [SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2309.14745/) (2023)
- [Entity-Guided Multi-Task Learning for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2601.01870/) (2026)

## Abstract (arXiv)

The existing generative adversarial fusion methods generally concatenate source images and extract local features through convolution operation, without considering their global characteristics, which tends to produce an unbalanced result and is biased towards the infrared image or visible image. Toward this end, we propose a novel end-to-end mode based on generative adversarial training to achieve better fusion balance, termed as textitinteractive compensatory attention fusion network (ICAFusion). In particular, in the generator, we construct a multi-level encoder-decoder network with a triple path, and adopt infrared and visible paths to provide additional intensity and gradient information. Moreover, we develop interactive and compensatory attention modules to communicate their pathwise information, and model their long-range dependencies to generate attention maps, which can more focus on infrared target perception and visible detail characterization, and further increase the representation power for feature extraction and feature reconstruction. In addition, dual discriminators are designed to identify the similar distribution between fused result and source images, and the generator is optimized to produce a more balanced result. Extensive experiments illustrate that our ICAFusion obtains superior fusion performance and better generalization ability, which precedes other advanced methods in the subjective visual description and objective metric evaluation. Our codes will be public at urlhttps://github.com/Zhishe-Wang/ICAFusion