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Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization — arXiv:2609.18139

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18139
License (as archived): CC0-1.0
Archived: 2026-10-15T16:16:21.439Z
SHA-256 of the archived text: 5014cf1875aecf28f1715d4be0723ef247e9cb7883d969800b2d30fa08e08b41
Stable URL of this page: https://metraipolis.com/documento/dc_bcd2401e24ef

How to cite

Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization — arXiv:2609.18139 — arXiv (en), https://arxiv.org/abs/2609.18139 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bcd2401e24ef (Metraipolis archive dc_bcd2401e24ef, sha256 5014cf1875aecf28f1715d4be0723ef247e9cb7883d969800b2d30fa08e08b41)

BibTeX

@misc{dcbcd2401e24ef,
  title = {Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization — arXiv:2609.18139},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18139},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bcd2401e24ef; sha256 5014cf1875aecf28f1715d4be0723ef247e9cb7883d969800b2d30fa08e08b41},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18139. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Cross-view object geo-localization (CVOGL) locates a target in satellite imagery using drone or street-view queries. Existing methods train separate detectors for each viewpoint, leading to parameter redundancy and impeding cross-view knowledge sharing. Moreover, top-ranked satellite candidates are often visually similar, so visual appearance and categorical labels alone are insufficient to resolve such ambiguity. To address these, we propose MVLGeo, an efficient framework designed to unify multiple viewpoints and reduce model redundancy. First, we introduce environmental contextual text from the query view as cues to distinguish visually similar candidates via Vision-Language Reranking (VL-Rerank). Second, we design a multi-view Mixture-of-Experts architecture (MV-MoE) with a shared encoder and view-specific experts to reduce redundancy and promote knowledge sharing, while cross-view contrastive learning aligns their representations for consistency. Third, we introduce an adaptive elliptical prior (ESAM-Prior) as auxiliary positional encoding for anisotropic geometric perception. Extensive experiments on the CVOGL benchmarks confirm that MVLGeo, as a unified model for multiple query viewpoints, achieves state-of-the-art performance, demonstrating robustness to input degradation and generalization across viewpoints. Code and models will be available on GitHub to facilitate future work.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-17 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.18139 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T15:45:19.752Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).