  Few-shot object detection (FSOD) aims to extract semantic knowledge from
limited object instances of novel categories within a target domain. Recent
advances in FSOD focus on fine-tuning the base model based on a few objects via
meta-learning or data augmentation. Despite their success, the majority of them
are grounded with parametric readjustment to generalize on novel objects, which
face considerable challenges in Industry 5.0, such as (i) a certain amount of
fine-tuning time is required, and (ii) the parameters of the constructed model
being unavailable due to the privilege protection, making the fine-tuning fail.
Such constraints naturally limit its application in scenarios with real-time
configuration requirements or within black-box settings. To tackle the
challenges mentioned above, we formalize a novel FSOD task, referred to as Test
TIme Few Shot DEtection (TIDE), where the model is un-tuned in the
configuration procedure. To that end, we introduce an asymmetric architecture
for learning a support-instance-guided dynamic category classifier. Further, a
cross-attention module and a multi-scale resizer are provided to enhance the
model performance. Experimental results on multiple few-shot object detection
platforms reveal that the proposed TIDE significantly outperforms existing
contemporary methods. The implementation codes are available at
https://github.com/deku-0621/TIDE
