视觉地点识别模型识别的是地点还是条件？干扰项增强评估与条件抑制
Are Visual Place Recognition Models Recognizing Places or Conditions? Distractor-Augmented Evaluation and Condition Suppression
AI总结：
该研究针对视觉地点识别模型易受干扰项影响的问题，提出干扰项增强评估方法与条件抑制技术，发现干扰项鲁棒性与标准检索性能不同，可通过抑制条件信息提升模型性能。
AI中文摘要：
长期视觉地点识别（VPR）的典型评估方式是将不同条件下的查询图像与数据库图像匹配，但众包地图数据库可能混杂不同条件，包含与查询图像条件相似但对应不同地点的图像。存在此类干扰项时，方法可能依据条件相似性而非地点身份检索。本文认为该易感性源于VPR方法的判别性使其描述子编码光照、天气、季节外观等信息。因此，本文引入干扰项增强召回率（DAR）以分离并量化干扰项的影响，提出条件抑制以去除VPR描述子中的条件信息。在11种方法和6个数据集上，DAR@1下的方法排名与召回率@1（R@1）下的排名不同；采用INLP和LEACE作为条件抑制方法通常可提升DAR@1且不降低R@1。由此可见，干扰项鲁棒性与标准检索性能不同，可通过抑制条件信息加以提升。
英文摘要：
Long-term Visual Place Recognition (VPR) is typically evaluated by matching queries from one condition against a database from another. Crowdsourced map databases, however, may mix conditions and include images that resemble the query in condition but depict different places. In the presence of these distractors, a method may retrieve by condition similarity rather than place identity. We argue that this susceptibility arises because the discriminability of VPR methods allows them to encode information such as illumination, weather, and seasonal appearance in their descriptors. We therefore introduce Distractor-Augmented Recall (DAR) to isolate and quantify the effect of distractors, and propose condition suppression to remove condition information from VPR descriptors. Across eleven methods and six datasets, method rankings under DAR@1 differ from those under Recall@1 (R@1), while applying INLP and LEACE as condition suppression methods generally improves DAR@1 without reducing R@1. Thus, distractor robustness is distinct from standard retrieval performance and can be improved by suppressing condition information.
