Abstract
Cross-Modality Ship Re-Identification (CMS Re-ID) is critical for achieving all-day and all-weather maritime target tracking, yet it is fundamentally challenged by significant modality discrepancies. Mainstream solutions typically rely on explicit modality alignment strategies; however, this paradigm heavily depends on constructing large-scale paired datasets for pre-training. To address this, grounded in the Platonic Representation Hypothesis, we explore the potential of Vision Foundation Models (VFMs) in bridging modality gaps. Recognizing the suboptimal performance of existing generic Parameter-Efficient Fine-Tuning (PEFT) methods that operate within the weight space, particularly on limited-capacity models, we shift the optimization perspective to the feature space and propose a novel PEFT strategy termed Domain Representation Injection (DRI). Specifically, while keeping the VFM fully frozen to maximize the preservation of general knowledge, we design a lightweight, learnable Offset Encoder to extract domain-specific representations rich in modality and identity attributes from raw inputs. Guided by the contextual information of intermediate features at different layers, a Modulator adaptively transforms these representations. Subsequently, they are injected into the intermediate layers via additive fusion, dynamically reshaping the feature distribution to adapt to the downstream task without altering the VFM's pre-trained weights. Extensive experimental results demonstrate the superiority of our method, achieving State-of-the-Art (SOTA) performance with minimal trainable parameters. For instance, on the HOSS-ReID dataset, we attain 57.9\% and 60.5\% mAP using only 1.54M and 7.05M parameters, respectively. The code is available at this https URL.
Abstract (translated)
跨模态船只再识别（CMS Re-ID）对于全天候海上目标追踪至关重要，但其核心挑战在于显著的模态差异。主流解决方案通常依赖于显式的模态对齐策略；然而，这种范式严重依赖于构建大规模配对数据集进行预训练。为了解决这一问题，在柏拉图表征假设的基础上，我们探讨了视觉基础模型（VFMs）在弥合模态差距方面的潜力。鉴于现有的通用参数高效微调（PEFT）方法在权重空间内操作时在容量有限的模型上表现不佳，我们将优化视角转向特征空间，并提出了一种新的PEFT策略，称为域表征注入（DRI）。具体而言，在保持VFMs完全冻结以最大限度地保留一般知识的同时，我们设计了一个轻量级、可学习的Offset Encoder来从原始输入中提取富含模态和身份属性的特定领域表示。基于不同层间中间特征的上下文信息，一个调制器会自适应地转换这些表示。随后，通过加性融合将它们注入到中间层，动态重塑特征分布以适应下游任务，而不改变VFMs的预训练权重。 广泛的实验结果表明了我们方法的优越性，在使用最少可训练参数的情况下达到了最先进的性能（SOTA）。例如，在HOSS-ReID数据集上，我们分别使用1.54M和7.05M个参数实现了57.9%和60.5%mAP。代码可在[提供链接处]获得。
URL
https://arxiv.org/abs/2512.20892
