Wasserstein-Barycenter Parts for Robust Visual Retrieval

Visual retrieval increasingly has to operate under severe nuisance, where changes in sensor, viewpoint, or modality alter the pixels without changing the identity of the target. Such changes wash out texture and colour, while the layout of recurring local parts often survives. Part-based methods can discover these parts, but they typically summarise each part with a single mean vector, discarding useful distributional structure. We propose WBPRIOR, a lightweight plug-and-play part representation that brings a layout prior into retrieval. Each patch predicts a mean vector and a positive variance, defining a diagonal Gaussian, and each part is represented by the 2-Wasserstein barycenter of the Gaussians assigned to it. For diagonal Gaussians, this barycenter has an exact closed form, so every part retains both a mean vector and a learned scale vector with negligible additional aggregation cost. In a controlled matched study that changes only the aggregation rule, the slot mean degrades sharply on corrupted images, whereas the barycenter restores a useful part signal and provides substantially larger gains under corruption than on clean images. The same head improves the robustness of seven published cross-view retrievers, while standalone WBPRIOR is competitive with recent systems on drone geo-localisation and transfers to sketch retrieval and aerial-ground person re-identification. These results show that robust part-based retrieval depends not only on how parts are discovered, but also on how each part is represented.