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.

Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction. Deep learning has extremely powerful in extracting features, and watermarking algorithms based on deep learning have attracted widespread attention. Most existing methods use $3\times 3$ small kernel convolution to extract image features and embed the watermarking. However, the effective perception fields for small kernel convolution are extremely confined, so the pixels that each watermarking can affect are restricted, thus limiting the performance of the watermarking. To address these problems, we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions. It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a high-dimensional space by $1\times 1$ convolution to achieve adaptability in the channel dimension. Subsequently, the modification of the embedded watermarking on the cover image is extended to more pixels. Because the magnitude and convergence rates of each loss function are different, an adaptive loss weight assignment strategy is proposed to make the weights participate in the network training together and adjust the weight dynamically. Further, a high-frequency wavelet loss is proposed, by which the watermarking is restricted to only the low-frequency wavelet sub-bands, thereby enhancing the robustness of watermarking against image compression. The experimental results show that the peak signal-to-noise ratio (PSNR) of the encoded image reaches 40.12, the structural similarity (SSIM) reaches 0.9721, and the watermarking has good robustness against various types of noise.