Entity-Guided Multi-Task Learning for Infrared and Visible Image Fusion

• URL: http://arxiv.org/abs/2601.01870v1
• Date: Mon, 05 Jan 2026 08:00:03 GMT
• Title: Entity-Guided Multi-Task Learning for Infrared and Visible Image Fusion
• Authors: Wenyu Shao, Hongbo Liu, Yunchuan Ma, Ruili Wang,
• Abstract summary: We propose a novel fusion approach named Entity-Guided Multi-Task learning for infrared and visible image fusion (EGMT)<n>A principled method is proposed to extract entity-level textual information from image captions generated by large vision-language models.<n>A parallel multi-task learning architecture is constructed, which integrates image fusion with a multi-label classification task.<n>An entity-guided cross-modal interactive module is also developed to facilitate the fine-grained interaction between visual and entity-level textual features.
• Score: 14.3937321254743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing text-driven infrared and visible image fusion approaches often rely on textual information at the sentence level, which can lead to semantic noise from redundant text and fail to fully exploit the deeper semantic value of textual information. To address these issues, we propose a novel fusion approach named Entity-Guided Multi-Task learning for infrared and visible image fusion (EGMT). Our approach includes three key innovative components: (i) A principled method is proposed to extract entity-level textual information from image captions generated by large vision-language models, eliminating semantic noise from raw text while preserving critical semantic information; (ii) A parallel multi-task learning architecture is constructed, which integrates image fusion with a multi-label classification task. By using entities as pseudo-labels, the multi-label classification task provides semantic supervision, enabling the model to achieve a deeper understanding of image content and significantly improving the quality and semantic density of the fused image; (iii) An entity-guided cross-modal interactive module is also developed to facilitate the fine-grained interaction between visual and entity-level textual features, which enhances feature representation by capturing cross-modal dependencies at both inter-visual and visual-entity levels. To promote the wide application of the entity-guided image fusion framework, we release the entity-annotated version of four public datasets (i.e., TNO, RoadScene, M3FD, and MSRS). Extensive experiments demonstrate that EGMT achieves superior performance in preserving salient targets, texture details, and semantic consistency, compared to the state-of-the-art methods. The code and dataset will be publicly available at https://github.com/wyshao-01/EGMT.

Related papers

• Towards Unified Semantic and Controllable Image Fusion: A Diffusion Transformer Approach [99.80480649258557]
  DiTFuse is an instruction-driven framework that performs semantics-aware fusion within a single model.<n>Experiments on public IVIF, MFF, and MEF benchmarks confirm superior quantitative and qualitative performance, sharper textures, and better semantic retention.
  arXiv  Detail & Related papers  (2025-12-08T05:04:54Z)
• TeSG: Textual Semantic Guidance for Infrared and Visible Image Fusion [55.34830989105704]
  Infrared and visible image fusion (IVF) aims to combine complementary information from both image modalities.<n>We introduce textual semantics at two levels: the mask semantic level and the text semantic level.<n>We propose Textual Semantic Guidance for infrared and visible image fusion, which guides the image synthesis process.
  arXiv  Detail & Related papers  (2025-06-20T03:53:07Z)
• Embedding and Enriching Explicit Semantics for Visible-Infrared Person Re-Identification [31.011118085494942]
  Visible-infrared person re-identification (VIReID) retrieves pedestrian images with the same identity across different modalities.<n>Existing methods learn visual content solely from images, lacking the capability to sense high-level semantics.<n>We propose an Embedding and Enriching Explicit Semantics framework to learn semantically rich cross-modality pedestrian representations.
  arXiv  Detail & Related papers  (2024-12-11T14:27:30Z)
• EntityCLIP: Entity-Centric Image-Text Matching via Multimodal Attentive Contrastive Learning [35.87830182497944]
  In this paper, we work towards the textbfEntity-centric textbfImage-textbfText textbfMatching (EITM) problem.<n>The challenge of this task mainly lies in the larger semantic gap in entity association modeling.<n>We devise a multimodal attentive contrastive learning framework to adapt EITM problem, developing a model named EntityCLIP.
  arXiv  Detail & Related papers  (2024-10-23T12:12:56Z)
• ARMADA: Attribute-Based Multimodal Data Augmentation [93.05614922383822]
  Attribute-based Multimodal Data Augmentation (ARMADA) is a novel multimodal data augmentation method via knowledge-guided manipulation of visual attributes. ARMADA is a novel multimodal data generation framework that: (i) extracts knowledge-grounded attributes from symbolic KBs for semantically consistent yet distinctive image-text pair generation. This also highlights the need to leverage external knowledge proxies for enhanced interpretability and real-world grounding.
  arXiv  Detail & Related papers  (2024-08-19T15:27:25Z)
• Image Fusion via Vision-Language Model [91.36809431547128]
  We introduce a novel fusion paradigm named image Fusion via vIsion-Language Model (FILM) FILM generates semantic prompts from images and inputs them into ChatGPT for comprehensive textual descriptions. These descriptions are fused within the textual domain and guide the visual information fusion. FILM has shown promising results in four image fusion tasks: infrared-visible, medical, multi-exposure, and multi-focus image fusion.
  arXiv  Detail & Related papers  (2024-02-03T18:36:39Z)
• From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion [66.33467192279514]
  We introduce a text-guided multi-modality image fusion method that leverages the high-level semantics from textual descriptions to integrate semantics from infrared and visible images. Our method not only produces visually superior fusion results but also achieves a higher detection mAP over existing methods, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2023-12-31T08:13:47Z)
• A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking [17.847936914174543]
  Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia. We formulate multimodal entity linking as a neural text matching problem where each multimodal information (text and image) is treated as a query. This paper introduces a dual-way enhanced (DWE) framework for MEL.
  arXiv  Detail & Related papers  (2023-12-19T03:15:50Z)
• Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
  Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph. We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
  arXiv  Detail & Related papers  (2023-07-19T02:11:19Z)
• HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval [13.061063817876336]
  We propose a novel Hierarchical Graph Alignment Network (HGAN) for image-text retrieval. First, to capture the comprehensive multimodal features, we construct the feature graphs for the image and text modality respectively. Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module. Finally, the ultimate image and text features are further refined through three-level similarity functions to achieve the hierarchical alignment.
  arXiv  Detail & Related papers  (2022-12-16T05:08:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.