Semantic Reinforced Attention Learning for Visual Place Recognition
This work addresses the problem of robust visual place recognition for applications like robotics and autonomous navigation, presenting an incremental improvement by bridging rule-based and data-driven attention approaches.
The paper tackled the challenge of highlighting task-relevant visual cues in large-scale visual place recognition by proposing SRALNet, which integrates semantic priors with data-driven fine-tuning to improve attention mechanisms, and demonstrated superior performance over state-of-the-art methods on city-scale benchmark datasets.
Large-scale visual place recognition (VPR) is inherently challenging because not all visual cues in the image are beneficial to the task. In order to highlight the task-relevant visual cues in the feature embedding, the existing attention mechanisms are either based on artificial rules or trained in a thorough data-driven manner. To fill the gap between the two types, we propose a novel Semantic Reinforced Attention Learning Network (SRALNet), in which the inferred attention can benefit from both semantic priors and data-driven fine-tuning. The contribution lies in two-folds. (1) To suppress misleading local features, an interpretable local weighting scheme is proposed based on hierarchical feature distribution. (2) By exploiting the interpretability of the local weighting scheme, a semantic constrained initialization is proposed so that the local attention can be reinforced by semantic priors. Experiments demonstrate that our method outperforms state-of-the-art techniques on city-scale VPR benchmark datasets.