# TSJNet: A Multi-modality Target and Semantic Awareness Joint-driven Image Fusion Network

arXiv: https://arxiv.org/abs/2402.01212 · HTML: https://mlsanity.com/paper/2402.01212/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 71 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [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)
- [Neighborhood Attention Transformer](https://mlsanity.com/paper/2204.07143/) (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)
- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
- [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)
- [Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration](https://mlsanity.com/paper/2205.11876/) (2022)
- [Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models](https://mlsanity.com/paper/2301.08072/) (2023)
- [Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion](https://mlsanity.com/paper/2302.01392/) (2023)
- [DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem](https://mlsanity.com/paper/1804.07851/) (2018)
- [An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection](https://mlsanity.com/paper/2305.09999/) (2023)
- [TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning](https://mlsanity.com/paper/2112.01030/) (2021)
- [TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network](https://mlsanity.com/paper/2201.10147/) (2022)
- [Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion](https://mlsanity.com/paper/2308.03256/) (2023)
- [Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond](https://mlsanity.com/paper/2305.06720/) (2023)
- [Mutual-Guided Dynamic Network for Image Fusion](https://mlsanity.com/paper/2308.12538/) (2023)

## Built on by (most cited first, 10 of 10)

- [All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark](https://mlsanity.com/paper/2402.02090/) (2024)
- [WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection](https://mlsanity.com/paper/2507.18173/) (2025)
- [UMCFuse: A Unified Multiple Complex Scenes Infrared and Visible Image Fusion Framework](https://mlsanity.com/paper/2402.02096/) (2024)
- [Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion](https://mlsanity.com/paper/2409.01728/) (2024)
- [Spatial-Frequency Enhanced Mamba for Multi-Modal Image Fusion](https://mlsanity.com/paper/2511.06593/) (2025)
- [FlexiD-Fuse: Flexible number of inputs multi-modal medical image fusion based on diffusion model](https://mlsanity.com/paper/2509.09456/) (2025)
- [MambaTrans: Multimodal Fusion Image Translation via Large Language Model Priors for Downstream Visual Tasks](https://mlsanity.com/paper/2508.07803/) (2025)
- [SGDFuse: SAM-Guided Diffusion Model for High-Fidelity Infrared and Visible Image Fusion](https://mlsanity.com/paper/2508.05264/) (2025)
- [Bridging Multimodal Fusion and Expert Routing via Spectral Reliability Descriptors for Robust Object Detection](https://mlsanity.com/paper/2606.01173/) (2026)
- [FMRFusion: Frequency-Aware Multi-View Representation Learning for Heterogeneous Image Fusion](https://mlsanity.com/paper/2606.07985/) (2026)

## Abstract (arXiv)

This study aims to address the problem of incomplete information in unimodal images for semantic segmentation and object detection tasks. Existing multimodal fusion methods suffer from limited capability in discriminative modeling of multi-scale semantic structures and salient target regions, which further restricts the effective fusion of task-related semantic details and target information across modalities. To tackle these challenges, this paper proposes a novel fusion network termed TSJNet, which leverages the semantic information output by high-level tasks in a joint manner to guide the fusion process. Specifically, we design a multi-dimensional feature extraction module with dual parallel branches to capture multi-scale and salient features. Meanwhile, a data-agnostic spatial attention module embedded in the decoder dynamically calibrates attention allocation across different data domains, significantly enhancing the model's generalization ability. To optimize both fusion and advanced visual tasks, we balance performance by combining fusion loss with semantic losses. Additionally, we have developed a multimodal unmanned aerial vehicle (UAV) dataset covering multiple scenarios (UMS). Extensive experiments demonstrate that TSJNet achieves outstanding performance on five public datasets (MSRS, Mtextsuperscript3FD, RoadScene, LLVIP, and TNO) and our UMS dataset. The generated fusion results exhibit favorable visual effects, and compared to state-of-the-art methods, the mean average precision (mAP@0.5) and mean intersection over union (mIoU) for object detection and segmentation, respectively, improve by 7.97% and 10.88%.The code and the dataset has been publicly released at https://github.com/XylonXu01/TSJNet.