Beyond Strict Pairing: Arbitrarily Paired Training for High-Performance Infrared and Visible Image Fusion

• URL: http://arxiv.org/abs/2603.21820v1
• Date: Mon, 23 Mar 2026 11:03:10 GMT
• Title: Beyond Strict Pairing: Arbitrarily Paired Training for High-Performance Infrared and Visible Image Fusion
• Abstract summary: Infrared and visible image fusion(IVIF) combines complementary modalities while preserving natural textures and salient thermal signatures.<n>Existing solutions rely on extensive sets of rigidly aligned image pairs for training.<n>We propose UnPaired and Arbitrarily Paired Training Paradigms (UPTP and APTP) for high-performance IVIF.
• Score: 60.85657185619169
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Infrared and visible image fusion(IVIF) combines complementary modalities while preserving natural textures and salient thermal signatures. Existing solutions predominantly rely on extensive sets of rigidly aligned image pairs for training. However, acquiring such data is often impractical due to the costly and labour-intensive alignment process. Besides, maintaining a rigid pairing setting during training restricts the volume of cross-modal relationships, thereby limiting generalisation performance. To this end, this work challenges the necessity of Strictly Paired Training Paradigm (SPTP) by systematically investigating UnPaired and Arbitrarily Paired Training Paradigms (UPTP and APTP) for high-performance IVIF. We establish a theoretical objective of APTP, reflecting the complementary nature between UPTP and SPTP. More importantly, we develop a practical framework capable of significantly enriching cross-modal relationships even with severely limited and unaligned training data. To validate our propositions, three end-to-end lightweight baselines, alongside a set of innovative loss functions, are designed to cover three classic frameworks (CNN, Transformer, GAN). Comprehensive experiments demonstrate that the proposed APTP and UPTP are feasible and capable of training models on a severely limited and content-inconsistent infrared and visible dataset, achieving performance comparable to that of a dataset 100$\times$ larger in SPTP. This finding fundamentally alleviates the cost and difficulty of data collection while enhancing model robustness from the data perspective, delivering a feasible solution for IVIF studies. The code is available at \href{https://github.com/yanglinDeng/IVIF_unpair}{\textcolor{blue}{https://github.com/yanglinDeng/IVIF\_unpair}}.

Related papers

• FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning [5.979900839748232]
  This paper proposes a framework for efficient collaboration of large models on the edge.<n>FedOPAL adapts the visual prompts as feature prompts, actively correcting the feature distribution of heterogeneous data to a linearly separable space.<n> Experimental results show that FedOPAL not only significantly outperforms the original analytical methods on several benchmarks, but also achieves accuracy comparable to state-of-the-art iterative methods while maintaining zero server-side training costs.
  arXiv  Detail & Related papers  (2026-07-09T11:28:31Z)
• Focus When Necessary: Adaptive Routing and Collaborative Grounding for Training-Free Visual Grounding [56.38477453373368]
  LazyMCoT is a dynamic and training-free framework that adaptively allocates visual grounding efforts based on sample difficulty.<n>LazyMCoT rivals training-based approaches by simultaneously improving reasoning accuracy and reducing average inference latency.
  arXiv  Detail & Related papers  (2026-06-15T03:17:44Z)
• WRF4CIR: Weight-Regularized Fine-Tuning Network for Composed Image Retrieval [58.85945491755373]
  Composed Image Retrieval (CIR) task aims to retrieve target images based on reference images and modification texts.<n>Current CIR methods rely on fine-tuning vision-language pre-trained models.<n>We introduce WRF4CIR, a Weight-Regularized Fine-tuning network for CIR.
  arXiv  Detail & Related papers  (2026-04-07T08:23:39Z)
• Experts-Guided Unbalanced Optimal Transport for ISP Learning from Unpaired and/or Paired Data [48.3649059876439]
  We introduce an unsupervised training framework capable of training arbitrary ISP architectures in both unpaired and paired modes.<n>Our framework provides robustness to outliers in the target sRGB data, allowing it to discount atypical samples that would be prohibitively costly to map.<n>Our experiments show that while our framework, when trained in paired mode, exceeds the performance of the original paired methods across all metrics, our unpaired mode concurrently achieves quantitative and qualitative performance that rivals, and in some cases surpasses, the original paired-trained counterparts.
  arXiv  Detail & Related papers  (2025-12-05T11:27:36Z)
• Don't Just Fine-tune the Agent, Tune the Environment [25.7349297100143]
  Supervised fine-tuning on synthetic data leads to overfitting.<n>Standard reinforcement learning struggles with a critical cold-start problem and training instability.<n>Our work presents a paradigm shift from supervised fine-tuning on static trajectories to dynamic, environment-based exploration.
  arXiv  Detail & Related papers  (2025-10-11T12:35:15Z)
• Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training [36.64849664688883]
  Trajectory-Distilled GFlowNet (TD-GFN) is a novel proxy-free training framework.<n>It learns dense, transition-level edge rewards from offline trajectories via inverse reinforcement learning.<n>It significantly outperforms a broad range of existing baselines in both convergence speed and final sample quality.
  arXiv  Detail & Related papers  (2025-05-26T15:12:22Z)
• LSP-ST: Ladder Shape-Biased Side-Tuning for Robust Infrared Small Target Detection [4.5138645285711165]
  We propose Ladder Shape-Biased Side-Tuning (LSP-ST), a novel approach that introduces a shape-aware inductive bias to facilitate effective adaptation beyond texture cues.<n>With only 4.72M learnable parameters, LSP-ST achieves state-of-the-art performance on multiple infrared small target detection benchmarks.
  arXiv  Detail & Related papers  (2025-04-20T04:12:38Z)
• Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning of Vision Language Models [42.75418134743927]
  Reason-RFT is a two-stage reinforcement fine-tuning framework for visual reasoning.<n>First,Supervised Fine-Tuning (SFT) with curated CoT data activates the reasoning potential of Vision-Language Models (VLMs)<n>Second, reinforcement learning based on Group Relative Policy Optimization (GRPO) generates multiple reasoning-response pairs to enhance adaptability to domain shifts.
  arXiv  Detail & Related papers  (2025-03-26T17:38:06Z)
• CoLLM: A Large Language Model for Composed Image Retrieval [76.29725148964368]
  Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query.<n>We present CoLLM, a one-stop framework that generates triplets on-the-fly from image-caption pairs.<n>We leverage Large Language Models (LLMs) to generate joint embeddings of reference images and modification texts.
  arXiv  Detail & Related papers  (2025-03-25T17:59:50Z)
• UniRGB-IR: A Unified Framework for Visible-Infrared Semantic Tasks via Adapter Tuning [34.727262809777095]
  We propose UniRGB-IR, a scalable and efficient framework for RGB-IR semantic tasks.<n>Our framework comprises three key components: a vision transformer (ViT) foundation model, a Multi-modal Feature Pool (SFI) module, and a Supplementary Feature (SFI) module.<n> Experimental results on various RGB-IR semantic tasks demonstrate that our method can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-04-26T12:21:57Z)
• Disentangled Representation Learning with Transmitted Information Bottleneck [57.22757813140418]
  We present textbfDisTIB (textbfTransmitted textbfInformation textbfBottleneck for textbfDisd representation learning), a novel objective that navigates the balance between information compression and preservation.
  arXiv  Detail & Related papers  (2023-11-03T03:18:40Z)
• ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training [65.68511423300812]
  We propose ProgFed, a progressive training framework for efficient and effective federated learning. ProgFed inherently reduces computation and two-way communication costs while maintaining the strong performance of the final models. Our results show that ProgFed converges at the same rate as standard training on full models.
  arXiv  Detail & Related papers  (2021-10-11T14:45:00Z)
• When Relation Networks meet GANs: Relation GANs with Triplet Loss [110.7572918636599]
  Training stability is still a lingering concern of generative adversarial networks (GANs) In this paper, we explore a relation network architecture for the discriminator and design a triplet loss which performs better generalization and stability. Experiments on benchmark datasets show that the proposed relation discriminator and new loss can provide significant improvement on variable vision tasks.
  arXiv  Detail & Related papers  (2020-02-24T11:35:28Z)

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.