# One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion

arXiv: https://arxiv.org/abs/2502.19854 · HTML: https://mlsanity.com/paper/2502.19854/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 61 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (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)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion](https://mlsanity.com/paper/2303.06840/) (2023)
- [Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation](https://mlsanity.com/paper/2308.02097/) (2023)
- [Equivariant Multi-Modality Image Fusion](https://mlsanity.com/paper/2305.11443/) (2023)
- [LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images](https://mlsanity.com/paper/2304.05172/) (2023)
- [Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusion](https://mlsanity.com/paper/2403.16387/) (2024)
- [FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba](https://mlsanity.com/paper/2404.09498/) (2024)
- [CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion](https://mlsanity.com/paper/2211.10960/) (2022)
- [Task-Customized Mixture of Adapters for General Image Fusion](https://mlsanity.com/paper/2403.12494/) (2024)
- [Image Fusion via Vision-Language Model](https://mlsanity.com/paper/2402.02235/) (2024)
- [CrossFuse: A Novel Cross Attention Mechanism based Infrared and Visible Image Fusion Approach](https://mlsanity.com/paper/2406.10581/) (2024)
- [Generative-based Fusion Mechanism for Multi-Modal Tracking](https://mlsanity.com/paper/2309.01728/) (2023)
- [Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking](https://mlsanity.com/paper/2201.08673/) (2022)
- [TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion](https://mlsanity.com/paper/2312.14209/) (2023)
- [An Accelerated Correlation Filter Tracker](https://mlsanity.com/paper/1912.02854/) (2019)
- [FusionBooster: A Unified Image Fusion Boosting Paradigm](https://mlsanity.com/paper/2305.05970/) (2023)
- [MMDRFuse: Distilled Mini-Model with Dynamic Refresh for Multi-Modality Image Fusion](https://mlsanity.com/paper/2408.15641/) (2024)
- [Conti-Fuse: A Novel Continuous Decomposition-based Fusion Framework for Infrared and Visible Images](https://mlsanity.com/paper/2406.04689/) (2024)

## Built on by (most cited first, 30 of 32)

- [A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking](https://mlsanity.com/paper/2505.19858/) (2025)
- [VideoFusion: A Spatio-Temporal Collaborative Network for Multi-modal Video Fusion](https://mlsanity.com/paper/2503.23359/) (2025)
- [AWM-Fuse: Multi-Modality Image Fusion for Adverse Weather via Global and Local Text Perception](https://mlsanity.com/paper/2508.16881/) (2025)
- [Collaborating Vision, Depth, and Thermal Signals for Multi-Modal Tracking: Dataset and Algorithm](https://mlsanity.com/paper/2509.24741/) (2025)
- [Revisiting Generative Infrared and Visible Image Fusion Based on Human Cognitive Laws](https://mlsanity.com/paper/2510.26268/) (2025)
- [GrFormer: A Novel Transformer on Grassmann Manifold for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2506.14384/) (2025)
- [A Tri-Modal Dataset and a Baseline System for Tracking Unmanned Aerial Vehicles](https://mlsanity.com/paper/2511.18344/) (2025)
- [FusionRegister: Every Infrared and Visible Image Fusion Deserves Registration](https://mlsanity.com/paper/2603.07667/) (2026)
- [EvaNet: Towards More Efficient and Consistent Infrared and Visible Image Fusion Assessment](https://mlsanity.com/paper/2604.02896/) (2026)
- [Text-Guided Channel Perturbation and Pretrained Knowledge Integration for Unified Multi-Modality Image Fusion](https://mlsanity.com/paper/2511.12432/) (2025)
- [MambaVF: State Space Model for Efficient Video Fusion](https://mlsanity.com/paper/2602.06017/) (2026)
- [Multi-Modal Image Fusion via Intervention-Stable Feature Learning](https://mlsanity.com/paper/2603.23272/) (2026)
- [Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion](https://mlsanity.com/paper/2604.08924/) (2026)
- [Rethinking Few-Shot Image Fusion: Granular Ball Priors Enable General-Purpose Deep Fusion](https://mlsanity.com/paper/2504.08937/) (2025)
- [Causality-Driven Infrared and Visible Image Fusion](https://mlsanity.com/paper/2505.20830/) (2025)
- [UTA-Sign: Unsupervised Thermal Video Augmentation via Event-Assisted Traffic Signage Sketching](https://mlsanity.com/paper/2508.20594/) (2025)
- [Neurodynamics-Driven Coupled Neural P Systems for Multi-Focus Image Fusion](https://mlsanity.com/paper/2509.17704/) (2025)
- [SSVIF: Self-Supervised Segmentation-Oriented Visible and Infrared Image Fusion](https://mlsanity.com/paper/2509.22450/) (2025)
- [Direction-aware multi-scale gradient loss for infrared and visible image fusion](https://mlsanity.com/paper/2510.13067/) (2025)
- [Text-Routed Sparse Mixture-of-Experts Model with Explanation and Temporal Alignment for Multi-Modal Sentiment Analysis](https://mlsanity.com/paper/2512.22741/) (2025)
- [CAWM-Mamba: A unified model for infrared-visible image fusion and compound adverse weather restoration](https://mlsanity.com/paper/2603.02560/) (2026)
- [ReDiffuse: Rotation Equivariant Diffusion Model for Multi-focus Image Fusion](https://mlsanity.com/paper/2603.21129/) (2026)
- [Beyond Strict Pairing: Arbitrarily Paired Training for High-Performance Infrared and Visible Image Fusion](https://mlsanity.com/paper/2603.21820/) (2026)
- [FTPFusion: Frequency-Aware Infrared and Visible Video Fusion with Temporal Perturbation](https://mlsanity.com/paper/2604.01900/) (2026)
- [MAVFusion: Efficient Infrared and Visible Video Fusion via Motion-Aware Sparse Interaction](https://mlsanity.com/paper/2604.01958/) (2026)
- [Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion](https://mlsanity.com/paper/2605.00461/) (2026)
- [Fusion in Your Way: Aligning Image Fusion with Heterogeneous Demands via Direct Preference Optimization](https://mlsanity.com/paper/2605.06049/) (2026)
- [Text-Driven Fusion for Infrared and Visible Images: Achieving Image Scene Adaptation on Hyperbolic Space](https://mlsanity.com/paper/2606.15104/) (2026)
- [Multi-modality Image Fusion under Adverse Weather: Mask-Guided Feature Restoration and Interaction](https://mlsanity.com/paper/2606.26812/) (2026)
- [UHD-MFF: Shattering Barriers in Multi-Focus Ultra-High-Definition Image Fusion via Learnable Lookup Tables](https://mlsanity.com/paper/2606.31242/) (2026)

## Abstract (arXiv)

Advanced image fusion methods mostly prioritise high-level missions, where task interaction struggles with semantic gaps, requiring complex bridging mechanisms. In contrast, we propose to leverage low-level vision tasks from digital photography fusion, allowing for effective feature interaction through pixel-level supervision. This new paradigm provides strong guidance for unsupervised multimodal fusion without relying on abstract semantics, enhancing task-shared feature learning for broader applicability. Owning to the hybrid image features and enhanced universal representations, the proposed GIFNet supports diverse fusion tasks, achieving high performance across both seen and unseen scenarios with a single model. Uniquely, experimental results reveal that our framework also supports single-modality enhancement, offering superior flexibility for practical applications. Our code will be available at https://github.com/AWCXV/GIFNet.