SEALECT: Scene-Conditioned Pose Priors for Plausible Maritime Vessel Retrieval
Faisal Mohammad, Mohamed Younis
Abstract
We address scene-conditioned object retrieval for compositional augmentation in maritime imagery, where strong geometric constraints make appearance-only retrieval produce physically implausible vessel insertions. We propose SEALECT, a retrieval framework that jointly models visual compatibility, vessel pose, scale, and scene geometry through a unified score combining neural similarity, scene-conditioned pose priors, and observed-pose consistency. We construct a maritime scene-pose library and model the pose and scale priors nonparametrically, as a kernel that conditions each prior continuously on the scene with no learned parameters. Across seven maritime benchmarks, our trained scene-conditioned retriever exceeds the strongest appearance encoder (SigLIP 2) in recall (+0.14), and the training-free prior raises geometric plausibility further at negligible recall cost, more than halving the constraint violations of appearance retrieval and outperforming learned foreground-search baselines (GALA-style, DiscoFOS). In a blind study of end-to-end composites (BootPlace placement, DiffHarmony harmonization), SEALECT ranks first among six methods, ahead of random and GALA-style selection and statistically tied with CLIP, DiscoFOS, and DINO.