SRD2-VPR: Semantics-Enforced Feature Aggregation With Query Rejection for Visual Place Recognition

Visual place recognition is a fundamental task essential for applications like visual localization and loop closure detection. Existing methods perform well under controlled environments, but often fail in scenarios with significant domain shifts, such as drastic day-to-night transitions and severe occlusions. This limitation arises because existing approaches are globally optimized without explicit supervision for out-of-distribution (OOD) adaptation and overlook semantics as a complementary modality for improving OOD robustness via local context refinement. To address this, we propose a dual-branch network that jointly optimizes feature attention and feature description under semantic guidance, achieving improved OOD adaptation with overhead comparable to existing methods. The feature attention branch is guided by semantically-informed context richness, while the feature description branch is supervised through inter-class repelling and intra-class re-ranking. Additionally, we introduce a simple yet effective query rejection module that leverages the learned attention to assess an image’s informativeness, allowing it to exclude queries that lack place-representative context. Extensive experiments demonstrate that our method raises the average Recall@1 and Recall@5 by 3.5 and 3.9 percentage points over its state-of-the-art counterpart, and accelerates feature matching by 28% for downstream visual localization without performance degradation.