Beyond Weight Adaptation: Feature-Space Domain Injection for Cross-Modal Ship Re-Identification

TL;DR

This paper introduces a novel feature-space domain injection method leveraging frozen vision foundation models and lightweight learnable modules to improve cross-modal ship re-identification without large paired datasets.

Contribution

It proposes Domain Representation Injection (DRI), a PEFT strategy that injects domain-specific features into frozen VFMs, achieving state-of-the-art results with minimal parameters.

Findings

Achieves SOTA performance on HOSS-ReID dataset.

Uses only 1.54M and 7.05M parameters for top results.

Demonstrates effectiveness of feature-space adaptation over weight-space methods.

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 https://github.com/TingfengXian/DRI.