DC-VLAQ: クエリ残差集約によるロバストな視覚的位置認識
DC-VLAQ: Query-Residual Aggregation for Robust Visual Place Recognition
DINOv2とCLIPの相補的特徴を残差補正で融合し、クエリへの残差応答で局所トークンを集約する視覚的位置認識フレームワークを提案。
詳しい要約
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著者: Hanyu Zhu, Zhihao Zhan, Yuhang Ming, Liang Li, Dibo Hou, Javier Civera, Wanzeng Kong
分類: cs.CV, cs.RO
原文アブストラクト
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.
