# FusionCounting: Robust visible-infrared image fusion guided by crowd counting via multi-task learning

## TL;DR

FusionCounting is a multi-task framework that simultaneously improves visible-infrared image fusion and crowd counting, especially effective in dense scenes, by leveraging joint learning, dynamic loss weighting, and adversarial training.

## Contribution

It introduces a unified multi-task learning approach integrating crowd counting into VIF, with novel dynamic loss weighting and adversarial training for robustness and performance.

## Key findings

- Enhanced fusion quality demonstrated on public datasets.
- Superior crowd counting accuracy achieved.
- Improved robustness against adversarial attacks.

## Abstract

Visible and infrared image fusion (VIF) is an important multimedia task in computer vision. Most VIF methods focus primarily on optimizing fused image quality. Recent studies have begun incorporating downstream tasks, such as semantic segmentation and object detection, to provide semantic guidance for VIF. However, semantic segmentation requires extensive annotations, while object detection, despite reducing annotation efforts compared with segmentation, faces challenges in highly crowded scenes due to overlapping bounding boxes and occlusion. Moreover, although RGB-T crowd counting has gained increasing attention in recent years, no studies have integrated VIF and crowd counting into a unified framework. To address these challenges, we propose FusionCounting, a novel multi-task learning framework that integrates crowd counting into the VIF process. Crowd counting provides a direct quantitative measure of population density with minimal annotation, making it particularly suitable for dense scenes. Our framework leverages both input images and population density information in a mutually beneficial multi-task design. To accelerate convergence and balance tasks contributions, we introduce a dynamic loss function weighting strategy. Furthermore, we incorporate adversarial training to enhance the robustness of both VIF and crowd counting, improving the model's stability and resilience to adversarial attacks. Experimental results on public datasets demonstrate that FusionCounting not only enhances image fusion quality but also achieves superior crowd counting performance.