OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning

Image fusion is a crucial technique in the field of computer vision, and its goal is to generate high-quality fused images and improve the performance of downstream tasks. However, existing fusion methods struggle to balance these two factors. Achieving high quality in fused images may result in lower performance in downstream visual tasks, and vice versa. To address this drawback, a novel LVM (large vision model)-guided fusion framework with Object-aware and Contextual COntrastive learning is proposed, termed as OCCO. The pre-trained LVM is utilized to provide semantic guidance, allowing the network to focus solely on fusion tasks while emphasizing learning salient semantic features in form of contrastive learning. Additionally, a novel feature interaction fusion network is also designed to resolve information conflicts in fusion images caused by modality differences. By learning the distinction between positive samples and negative samples in the latent feature space (contextual space), the integrity of target information in fused image is improved, thereby benefiting downstream performance. Finally, compared with eight state-of-the-art methods on four datasets, the effectiveness of the proposed method is validated, and exceptional performance is also demonstrated on downstream visual task.

VideoFusion: A Spatio-Temporal Collaborative Network for Mutli-modal Video Fusion and Restoration

Compared to images, videos better align with real-world acquisition scenarios and possess valuable temporal cues. However, existing multi-sensor fusion research predominantly integrates complementary context from multiple images rather than videos. This primarily stems from two factors: 1) the scarcity of large-scale multi-sensor video datasets, limiting research in video fusion, and 2) the inherent difficulty of jointly modeling spatial and temporal dependencies in a unified framework. This paper proactively compensates for the dilemmas. First, we construct M3SVD, a benchmark dataset with $220$ temporally synchronized and spatially registered infrared-visible video pairs comprising 153,797 frames, filling the data gap for the video fusion community. Secondly, we propose VideoFusion, a multi-modal video fusion model that fully exploits cross-modal complementarity and temporal dynamics to generate spatio-temporally coherent videos from (potentially degraded) multi-modal inputs. Specifically, 1) a differential reinforcement module is developed for cross-modal information interaction and enhancement, 2) a complete modality-guided fusion strategy is employed to adaptively integrate multi-modal features, and 3) a bi-temporal co-attention mechanism is devised to dynamically aggregate forward-backward temporal contexts to reinforce cross-frame feature representations. Extensive experiments reveal that VideoFusion outperforms existing image-oriented fusion paradigms in sequential scenarios, effectively mitigating temporal inconsistency and interference.

Language-Depth Navigated Thermal and Visible Image Fusion

Depth-guided multimodal fusion combines depth information from visible and infrared images, significantly enhancing the performance of 3D reconstruction and robotics applications. Existing thermal-visible image fusion mainly focuses on detection tasks, ignoring other critical information such as depth. By addressing the limitations of single modalities in low-light and complex environments, the depth information from fused images not only generates more accurate point cloud data, improving the completeness and precision of 3D reconstruction, but also provides comprehensive scene understanding for robot navigation, localization, and environmental perception. This supports precise recognition and efficient operations in applications such as autonomous driving and rescue missions. We introduce a text-guided and depth-driven infrared and visible image fusion network. The model consists of an image fusion branch for extracting multi-channel complementary information through a diffusion model, equipped with a text-guided module, and two auxiliary depth estimation branches. The fusion branch uses CLIP to extract semantic information and parameters from depth-enriched image descriptions to guide the diffusion model in extracting multi-channel features and generating fused images. These fused images are then input into the depth estimation branches to calculate depth-driven loss, optimizing the image fusion network. This framework aims to integrate vision-language and depth to directly generate color-fused images from multimodal inputs.

Learning a Unified Degradation-aware Representation Model for Multi-modal Image Fusion

All-in-One Degradation-Aware Fusion Models (ADFMs), a class of multi-modal image fusion models, address complex scenes by mitigating degradations from source images and generating high-quality fused images. Mainstream ADFMs often rely on highly synthetic multi-modal multi-quality images for supervision, limiting their effectiveness in cross-modal and rare degradation scenarios. The inherent relationship among these multi-modal, multi-quality images of the same scene provides explicit supervision for training, but also raises above problems. To address these limitations, we present LURE, a Learning-driven Unified Representation model for infrared and visible Image Fusion, which is degradation-aware. LURE decouples multi-modal multi-quality data at the data level and recouples this relationship in a unified latent feature space (ULFS) by proposing a novel unified loss. This decoupling circumvents data-level limitations of prior models and allows leveraging real-world restoration datasets for training high-quality degradation-aware models, sidestepping above issues. To enhance text-image interaction, we refine image-text interaction and residual structures via Text-Guided Attention (TGA) and an inner residual structure. These enhances text's spatial perception of images and preserve more visual details. Experiments show our method outperforms state-of-the-art (SOTA) methods across general fusion, degradation-aware fusion, and downstream tasks. The code will be publicly available.

Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond

Multi-modality image fusion, particularly infrared and visible image fusion, plays a crucial role in integrating diverse modalities to enhance scene understanding. Early research primarily focused on visual quality, yet challenges remain in preserving fine details, making it difficult to adapt to subsequent tasks. Recent approaches have shifted towards task-specific design, but struggle to achieve the ``The Best of Both Worlds'' due to inconsistent optimization goals. To address these issues, we propose a novel method that leverages the semantic knowledge from the Segment Anything Model (SAM) to Grow the quality of fusion results and Establish downstream task adaptability, namely SAGE. Specifically, we design a Semantic Persistent Attention (SPA) Module that efficiently maintains source information via the persistent repository while extracting high-level semantic priors from SAM. More importantly, to eliminate the impractical dependence on SAM during inference, we introduce a bi-level optimization-driven distillation mechanism with triplet losses, which allow the student network to effectively extract knowledge at the feature, pixel, and contrastive semantic levels, thereby removing reliance on the cumbersome SAM model. Extensive experiments show that our method achieves a balance between high-quality visual results and downstream task adaptability while maintaining practical deployment efficiency.

CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond

Infrared and visible image fusion (IVIF) is increasingly applied in critical fields such as video surveillance and autonomous driving systems. Significant progress has been made in deep learning-based fusion methods. However, these models frequently encounter out-of-distribution (OOD) scenes in real-world applications, which severely impact their performance and reliability. Therefore, addressing the challenge of OOD data is crucial for the safe deployment of these models in open-world environments. Unlike existing research, our focus is on the challenges posed by OOD data in real-world applications and on enhancing the robustness and generalization of models. In this paper, we propose an infrared-visible fusion framework based on Multi-View Augmentation. For external data augmentation, Top-k Selective Vision Alignment is employed to mitigate distribution shifts between datasets by performing RGB-wise transformations on visible images. This strategy effectively introduces augmented samples, enhancing the adaptability of the model to complex real-world scenarios. Additionally, for internal data augmentation, self-supervised learning is established using Weak-Aggressive Augmentation. This enables the model to learn more robust and general feature representations during the fusion process, thereby improving robustness and generalization. Extensive experiments demonstrate that the proposed method exhibits superior performance and robustness across various conditions and environments. Our approach significantly enhances the reliability and stability of IVIF tasks in practical applications.

Adaptive Illumination-Invariant Synergistic Feature Integration in a Stratified Granular Framework for Visible-Infrared Re-Identification

Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in applications such as search and rescue, infrastructure protection, and nighttime surveillance. However, it faces significant challenges due to modality discrepancies, varying illumination, and frequent occlusions. To overcome these obstacles, we propose \textbf{AMINet}, an Adaptive Modality Interaction Network. AMINet employs multi-granularity feature extraction to capture comprehensive identity attributes from both full-body and upper-body images, improving robustness against occlusions and background clutter. The model integrates an interactive feature fusion strategy for deep intra-modal and cross-modal alignment, enhancing generalization and effectively bridging the RGB-IR modality gap. Furthermore, AMINet utilizes phase congruency for robust, illumination-invariant feature extraction and incorporates an adaptive multi-scale kernel MMD to align feature distributions across varying scales. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, achieving a Rank-1 accuracy of $74.75\%$ on SYSU-MM01, surpassing the baseline by $7.93\%$ and outperforming the current state-of-the-art by $3.95\%$.

MATCNN: Infrared and Visible Image Fusion Method Based on Multi-scale CNN with Attention Transformer

While attention-based approaches have shown considerable progress in enhancing image fusion and addressing the challenges posed by long-range feature dependencies, their efficacy in capturing local features is compromised by the lack of diverse receptive field extraction techniques. To overcome the shortcomings of existing fusion methods in extracting multi-scale local features and preserving global features, this paper proposes a novel cross-modal image fusion approach based on a multi-scale convolutional neural network with attention Transformer (MATCNN). MATCNN utilizes the multi-scale fusion module (MSFM) to extract local features at different scales and employs the global feature extraction module (GFEM) to extract global features. Combining the two reduces the loss of detail features and improves the ability of global feature representation. Simultaneously, an information mask is used to label pertinent details within the images, aiming to enhance the proportion of preserving significant information in infrared images and background textures in visible images in fused images. Subsequently, a novel optimization algorithm is developed, leveraging the mask to guide feature extraction through the integration of content, structural similarity index measurement, and global feature loss. Quantitative and qualitative evaluations are conducted across various datasets, revealing that MATCNN effectively highlights infrared salient targets, preserves additional details in visible images, and achieves better fusion results for cross-modal images. The code of MATCNN will be available at https://github.com/zhang3849/MATCNN.git.

Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption

Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representation. Since 2018, the field has entered the deep learning era, with an increasing variety of approaches introducing a range of networks and loss functions to enhance visual performance. However, challenges such as data compatibility, perception accuracy, and efficiency remain. Unfortunately, there is a lack of recent comprehensive surveys that address this rapidly expanding domain. This paper fills that gap by providing a thorough survey covering a broad range of topics. We introduce a multi-dimensional framework to elucidate common learning-based IVIF methods, from visual enhancement strategies to data compatibility and task adaptability. We also present a detailed analysis of these approaches, accompanied by a lookup table clarifying their core ideas. Furthermore, we summarize performance comparisons, both quantitatively and qualitatively, focusing on registration, fusion, and subsequent high-level tasks. Beyond technical analysis, we discuss potential future directions and open issues in this area. For further details, visit our GitHub repository: https://github.com/RollingPlain/IVIF_ZOO.

A2RNet: Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion

Infrared and visible image fusion (IVIF) is a crucial technique for enhancing visual performance by integrating unique information from different modalities into one fused image. Exiting methods pay more attention to conducting fusion with undisturbed data, while overlooking the impact of deliberate interference on the effectiveness of fusion results. To investigate the robustness of fusion models, in this paper, we propose a novel adversarial attack resilient network, called $\textrm{A}^{\textrm{2}}$RNet. Specifically, we develop an adversarial paradigm with an anti-attack loss function to implement adversarial attacks and training. It is constructed based on the intrinsic nature of IVIF and provide a robust foundation for future research advancements. We adopt a Unet as the pipeline with a transformer-based defensive refinement module (DRM) under this paradigm, which guarantees fused image quality in a robust coarse-to-fine manner. Compared to previous works, our method mitigates the adverse effects of adversarial perturbations, consistently maintaining high-fidelity fusion results. Furthermore, the performance of downstream tasks can also be well maintained under adversarial attacks. Code is available at https://github.com/lok-18/A2RNet.