SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Figure S29: Zero-shot recognition (RareAct): In-distribution vs. out-of-distribution performance comparison per action class. For optimal viewing, please zoom in and rotate 90 degrees to the left  $\mathfrak{D}$ . The models tend to generalize poorly on unusual actions. Moreover, compared to other zero-shot setups, we notice the maximum variability in model performance across different action classes.
Figure S30: Confusion matrices for open-set recognition, using UCF101 as closed-set and HMDB as open-set. The x-axis represents the ground truth and the y-axis represents the predicted labels. We highlight known classes with green and unknown classes with red. Among all the methods  $v$ -SimSiam and  $v$ -MAE show worse performance in identifying 'unknown' classes from HMDB as 'unknown'.
Figure S31: Confusion matrices of open-set recognition, using Kinetics400 as closed-set and HMDB as open-set. The x-axis represents the ground truth and the y-axis represents the predicted labels. We highlight known classes with green and unknown classes with red. The results exhibit that all the video models struggle in identifying 'unknown' classes from HMDB as 'unknown'. This is likely due to their over-confident predictions.
Figure S32: Confusion matrices of open-set recognition, using Kinetics400 as closed-set and UCF as open-set. The x-axis represents the ground truth and the y-axis represents the predicted labels. We highlight known classes with green and unknown classes with red. The results exhibit that all the video models struggle in identifying 'unknown' classes from UCF as 'unknown'. This is likely due to their over-confident predictions.