Visual　Place　Recognition　(VPR)　is　aimed　at　predicting　the　location　of　a　query　image　by　referencing　a　database　of　geotagged　images.　For　VPR　task,　often　fewer　discriminative　local　regions　in　an　image　produce　important　effects　while　mundane　background　regions　do　not　contribute　or　even　cause　perceptual　aliasing　because　of　easy　overlap.　However,　existing　methods　lack　precisely　modeling　and　full　exploitation　of　these　discriminative　regions.　In　this　paper,　we　propose　the　Focus　on　Local　(FoL)　approach　to　stimulate　the　performance　of　image　retrieval　and　re-ranking　in　VPR　simultaneously　by　mining　and　exploiting　reliable　discriminative　local　regions　in　images　and　introducing　pseudo-correlation　supervision.　First,　we　design　two　losses,　Extraction-Aggregation　Spatial　Alignment　Loss　(SAL)　and　Foreground-Background　Contrast　Enhancement　Loss　(CEL),　to　explicitly　model　reliable　discriminative　local　regions　and　use　them　to　guide　the　generation　of　global　representations　and　efficient　re-ranking.　Second,　we　introduce　a　weakly-supervised　local　feature　training　strategy　based　on　pseudo-correspondences　obtained　from　aggregating　global　features　to　alleviate　the　lack　of　local　correspondences　ground　truth　for　the　VPR　task.　Third,　we　suggest　an　efficient　re-ranking　pipeline　that　is　efficiently　and　precisely　based　on　discriminative　region　guidance.　Finally,　experimental　results　show　that　our　FoL　achieves　the　state-of-the-art　on　multiple　VPR　benchmarks　in　both　image　retrieval　and　re-ranking　stages　and　also　significantly　outperforms　existing　two-stage　VPR　methods　in　terms　of　computational　efficiency.　Copyright　©　2025,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.