Fig. 2
Detection performance evaluation of DAMM. (A) Schematic representation of detection evaluation procedures for two use cases: one with no further fine-tuning of model parameters (zero-shot) and another that incorporates a limited set of newly annotated examples for fine-tuning the model parameters (few-shot); \(\theta\) represents model parameters. (B) Mask AP75 evaluation of DAMM across five unique datasets sourced from the AER Lab. The DAMM pretraining dataset may have contained frames from these five video datasets as both were sourced in-house. Each evaluation dataset contains 100 examples, with up to 50 allocated for training and 50 for testing. The mean and standard deviation of Mask AP75 are shown for each dataset across 0, 20, and 50 shot scenarios. Results are based on five randomly initialized train-test shuffles. Of note, standard deviation bars that are visually flat denote a deviation of 0. (C) Using the same approach as in (B), but for datasets collected outside the AER Lab. These datasets feature experimental setups that DAMM has not encountered during pretraining.
