  This report introduces an enhanced method for the Foundational Few-Shot
Object Detection (FSOD) task, leveraging the vision-language model (VLM) for
object detection. However, on specific datasets, VLM may encounter the problem
where the detected targets are misaligned with the target concepts of interest.
This misalignment hinders the zero-shot performance of VLM and the application
of fine-tuning methods based on pseudo-labels. To address this issue, we
propose the VLM+ framework, which integrates the multimodal large language
model (MM-LLM). Specifically, we use MM-LLM to generate a series of referential
expressions for each category. Based on the VLM predictions and the given
annotations, we select the best referential expression for each category by
matching the maximum IoU. Subsequently, we use these referential expressions to
generate pseudo-labels for all images in the training set and then combine them
with the original labeled data to fine-tune the VLM. Additionally, we employ
iterative pseudo-label generation and optimization to further enhance the
performance of the VLM. Our approach achieve 32.56 mAP in the final test.
