CANDIDATE-BOX SELECTION MATTERS FOR CROSS- DOMAIN FEW-SHOT OBJECT DETECTION

Cross-domain few-shot object detection (CD-FSOD) aims to transfer knowledge from abundant source-domain pretraining to an unseen target domain with only a few labeled samples, while overcoming domain shift and limited target supervision. Grounding DINO has become a mainstream baseline, which selects a fixed number of high-confidence candidate boxes by semantic matching before decoder refinement. In this paper, unlike current works, we identify the selected candidate boxes as an underexplored bottleneck: under domain shift, high-quality boxes may be ranked below the selection budget and discarded, while top-ranked boxes may fail to enclose objects. By comparing in-domain and cross-domain cases, we find that this issue is more severe in cross-domain settings and trace it to vision-language misalignment in candidate selection rather than candidate generation. Motivated by this finding, we propose Support-Conditioned Local Evidence (SCLE), a lightweight and plug-and-play refinement of candidate selection. SCLE constructs foreground and background prototypes from few support samples, builds a local evidence map, and evaluates candidates by in-box purity and inside-outside contrast. A fusion-quota strategy then combines semantically ranked and local-evidence-ranked candidates to better match the target-domain distribution without modifying the detector architecture, adding learnable parameters, or using external data. SCLE can be plugged into different Grounding-DINO-based methods. Extensive experiments validate its effectiveness and demonstrate consistent improvements across state-of-the-art CD-FSOD approaches