DC-VLAQ: Query-Residual Aggregation for Robust Visual Place Recognition

• URL: http://arxiv.org/abs/2601.12729v1
• Date: Mon, 19 Jan 2026 05:19:56 GMT
• Title: DC-VLAQ: Query-Residual Aggregation for Robust Visual Place Recognition
• Abstract summary: We propose DC-VLAQ, a representation-centric framework that integrates the fusion of complementary VFMs and robust global aggregation.<n>We show that DC-VLAQ consistently outperforms strong baselines and achieves state-of-the-art performance.
• Score: 22.709465759219327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the central challenges in visual place recognition (VPR) is learning a robust global representation that remains discriminative under large viewpoint changes, illumination variations, and severe domain shifts. While visual foundation models (VFMs) provide strong local features, most existing methods rely on a single model, overlooking the complementary cues offered by different VFMs. However, exploiting such complementary information inevitably alters token distributions, which challenges the stability of existing query-based global aggregation schemes. To address these challenges, we propose DC-VLAQ, a representation-centric framework that integrates the fusion of complementary VFMs and robust global aggregation. Specifically, we first introduce a lightweight residual-guided complementary fusion that anchors representations in the DINOv2 feature space while injecting complementary semantics from CLIP through a learned residual correction. In addition, we propose the Vector of Local Aggregated Queries (VLAQ), a query--residual global aggregation scheme that encodes local tokens by their residual responses to learnable queries, resulting in improved stability and the preservation of fine-grained discriminative cues. Extensive experiments on standard VPR benchmarks, including Pitts30k, Tokyo24/7, MSLS, Nordland, SPED, and AmsterTime, demonstrate that DC-VLAQ consistently outperforms strong baselines and achieves state-of-the-art performance, particularly under challenging domain shifts and long-term appearance changes.

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