The　focus　of　this　paper　is　on　Few-Shot　Counting　and　Detection　(FSCD),　a　task　that　involves　counting　and　localizing　target　objects　based　on　a　few　exemplar　bounding　boxes.　In　particular,　we　address　two　major　challenges　in　developing　a　FSCD　model:　the　high　cost　of　bounding　box　labeling　and　the　large　variations　in　object　appearance.　To　mitigate　the　former　issue,　we　propose　a　neighbor　distance-aware　mechanism　for　generating　pseudo　bounding　boxes.　This　mechanism　utilizes　neighboring　objects　as　context　to　estimate　the　location　and　size　of　the　target　object　without　requiring　training.　To　address　the　challenge　of　appearance　variation,　we　introduce　a　novel　query-guided　attention　module　that　enhances　the　visual　features　of　the　search　image　by　employing　multi-head　cross　attention　with　query　features.　The　module　is　designed　to　encourage　attentive　inspection　of　the　search　image　by　directing　the　model　to　focus　more　on　regions　that　share　similarities　with　the　target　objects.　We　integrate　the　query-guided　attention　module　into　the　Faster-RCNN　object　detection　model,　resulting　in　a　new　few-shot　object　detector　named　Counting-RCNN.　The　proposed　approach　outperforms　the　state-of-the-art　method　on　a　large-scale　FSCD147　dataset,　achieving　0.60　MAE,　5.36　RMSE,　and　13.01%　AP50　improvement.　©　2023　ACM.