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Open Access

ARTICLE

Dual-Stream Feature Decoupling and Temporal Variational Bayesian Inference for Ship Re-Identification with Incomplete Data

Wanhui Qiao1, Xiaorui Zhang1,*, Wei Sun2, Shiyu Zhou3, Kaibo Wang2
1 College of Computer and Information Engineering, Nanjing Tech University, Nanjing, China
2 School of Automation, Nanjing University of Information Science & Technology, Nanjing, China
3 School of Computer Science, Nanjing University of Information Science & Technology, Nanjing, China
* Corresponding Author: Xiaorui Zhang. Email: email
(This article belongs to the Special Issue: Advances in Image Recognition: Innovations, Applications, and Future Directions)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.077977

Received 21 December 2025; Accepted 13 March 2026; Published online 31 March 2026

Abstract

Ship re-identification (Re-ID) aims to match ship identities across disjoint camera views and separated time periods, which is critical for maritime target tracking and law enforcement. In real-world surveillance, variations in target distance and viewing angle frequently produce partial views and occlusions, leading to missing geometric components and fragmented appearance cues. Such incomplete observations substantially degrade the robustness and generalization of conventional single-frame methods that rely on global appearance representations. To address these challenges, this study proposes a new ship re-identification framework based on dual-stream feature decoupling and temporal variational Bayesian inference. The proposed method explicitly disentangles ship representations into appearance and structural streams, and leverages multi-frame temporal context to infer missing components and enhance discriminability under partial visibility. Specifically, a ResNet-based splitter trained adversarially against two discriminators is employed to decouple the input representation into separate feature streams. The decoupled streams are then modeled over time using a bidirectional LSTM (BiLSTM) together with a visibility-probability estimator. A graph-structured spatial prior, parameterized via a graph attention network (GAT), serves as the variational prior. Given sequential observations, the variational inference module estimates posterior distributions for missing components and performs probabilistic completion in the latent space. The framework is trained end-to-end using cross-entropy and triplet losses. Extensive experiments on the Ship-CH dataset demonstrate that our method achieves 85.67% mAP and 93.67% Rank-1 accuracy, exhibiting superior robustness under occlusion and partial visibility.

Keywords

BiLSTM; bayesian inference; feature decoupling; triplet loss; cross-entropy loss; ship re-identification

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