# Modality-Aware Infrared and Visible Image Fusion with Target-Aware Supervision

arXiv: https://arxiv.org/abs/2509.11476 · HTML: https://mlsanity.com/paper/2509.11476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 17 of 17)

- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption](https://mlsanity.com/paper/2501.10761/) (2025)
- [BALF: Simple and Efficient Blur Aware Local Feature Detector](https://mlsanity.com/paper/2211.14731/) (2022)
- [Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data](https://mlsanity.com/paper/2505.22521/) (2025)
- [Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods](https://mlsanity.com/paper/2408.04268/) (2024)
- [AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Way](https://mlsanity.com/paper/2409.14577/) (2024)
- [Gated Multimodal Graph Learning for Personalized Recommendation](https://mlsanity.com/paper/2506.00107/) (2025)
- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer](https://mlsanity.com/paper/2105.06049/) (2021)
- [Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration](https://mlsanity.com/paper/2504.00950/) (2025)
- [Transformer-based Selective Super-Resolution for Efficient Image Refinement](https://mlsanity.com/paper/2312.05803/) (2023)
- [NeRF-Based defect detection](https://mlsanity.com/paper/2504.00270/) (2025)
- [Image Matching Filtering and Refinement by Planes and Beyond](https://mlsanity.com/paper/2411.09484/) (2024)
- [Optimization of bi-directional gated loop cell based on multi-head attention mechanism for SSD health state classification model](https://mlsanity.com/paper/2506.14830/) (2025)
- [Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017](https://mlsanity.com/paper/2506.19877/) (2025)

## Built on by (most cited first, 1 of 1)

- [Event Detection in Videos: A Framework for the Development of New Methods](https://mlsanity.com/paper/2607.04372/) (2026)

## Abstract (arXiv)

Infrared and visible image fusion (IVIF) is a fundamental task in multi-modal perception that aims to integrate complementary structural and textural cues from different spectral domains. In this paper, we propose FusionNet, a novel end-to-end fusion framework that explicitly models inter-modality interaction and enhances task-critical regions. FusionNet introduces a modality-aware attention mechanism that dynamically adjusts the contribution of infrared and visible features based on their discriminative capacity. To achieve fine-grained, interpretable fusion, we further incorporate a pixel-wise alpha blending module, which learns spatially-varying fusion weights in an adaptive and content-aware manner. Moreover, we formulate a target-aware loss that leverages weak ROI supervision to preserve semantic consistency in regions containing important objects (e.g., pedestrians, vehicles). Experiments on the public M3FD dataset demonstrate that FusionNet generates fused images with enhanced semantic preservation, high perceptual quality, and clear interpretability. Our framework provides a general and extensible solution for semantic-aware multi-modal image fusion, with benefits for downstream tasks such as object detection and scene understanding.