# Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization

arXiv: https://arxiv.org/abs/2609.18139 · HTML: https://mlsanity.com/paper/2609.18139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 28 of its 41 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Placed in

- Mixture-of-experts / [Vision mixture-of-experts](https://mlsanity.com/moe/vision-mixture-of-experts/)

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://mlsanity.com/paper/2401.06066/) (2024)
- [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022)
- [From Sparse to Soft Mixtures of Experts](https://mlsanity.com/paper/2308.00951/) (2023)
- [TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization](https://mlsanity.com/paper/2204.00097/) (2022)
- [Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching](https://mlsanity.com/paper/2311.12751/) (2023)
- [Routers in Vision Mixture of Experts: An Empirical Study](https://mlsanity.com/paper/2401.15969/) (2024)
- [VAGeo: View-specific Attention for Cross-View Object Geo-Localization](https://mlsanity.com/paper/2501.07194/) (2025)
- [GeoX-Bench: Benchmarking Cross-View Geo-Localization and Pose Estimation Capabilities of Large Multimodal Models](https://mlsanity.com/paper/2511.13259/) (2025)
- [GeoBridge++: Fact-Guided Geo-Semantic Bridging for Unified Cross-View Geo-Localization](https://mlsanity.com/paper/2512.02697/) (2025)
- [Object-level Cross-view Geo-localization with Location Enhancement and Multi-Head Cross Attention](https://mlsanity.com/paper/2505.17911/) (2025)
- [SMGeo: Cross-View Object Geo-Localization with Grid-Level Mixture-of-Experts](https://mlsanity.com/paper/2511.14093/) (2025)
- [Learnable Query Aggregation with KV Routing for Cross-view Geo-localisation](https://mlsanity.com/paper/2512.23938/) (2025)
- [MOGeo: Beyond One-to-One Cross-View Object Geo-localization](https://mlsanity.com/paper/2603.13843/) (2026)
- [Anchor-free Cross-view Object Geo-localization with Gaussian Position Encoding and Cross-view Association](https://mlsanity.com/paper/2509.25623/) (2025)
- [Seeing the Unseen: Mask-Driven Positional Encoding and Strip-Convolution Context Modeling for Cross-View Object Geo-Localization](https://mlsanity.com/paper/2510.20247/) (2025)
- [A Parameter-Efficient Mixture-of-Experts Framework for Cross-Modal Geo-Localization](https://mlsanity.com/paper/2510.20291/) (2025)
- [SinGeo: Unlock Single Model's Potential for Robust Cross-View Geo-Localization](https://mlsanity.com/paper/2603.09377/) (2026)
- [From Horizontal to Rotated: Cross-View Object Geo-Localization with Orientation Awareness](https://mlsanity.com/paper/2603.14856/) (2026)
- [Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery](https://mlsanity.com/paper/2605.11654/) (2026)
- [Toward Calibrated Mixture-of-Experts Under Distribution Shift](https://mlsanity.com/paper/2606.20544/) (2026)
- [Beyond 2D Matching: A Unified Single-Stage Framework for Geometry-Aware Cross-View Object Geo-Localization](https://mlsanity.com/paper/2606.30576/) (2026)

## Abstract (arXiv)

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.