SlowGuess/NeurIPS_2023_OCR huggingface dataset:
To verify the effect of the proposed historical prompt ensemble strategy, we calculate the mAP of whether apply prompt ensemble under different loss function. We conduct the experiment on DA-Pro on Cityscapes  $\rightarrow$  FoggyCityscapes scenarios. Table 8 shows the effect of our historical prompt ensemble strategy. It improves the performance of all forms of learnable prompts without any inference overhead. In particular, the best case of improvement is  $0.4\%$  for the domain-adaptive prompt.
Table 8: The influence  $(\%)$  of the historical prompt ensemble on Cross-Weather adaptation scenario Cityscapes  $\rightarrow$  Foggy Cityscapes. Total number of tokens is set to 16.
Historical Prompt Ensemble	Ls,{s} + Lt,{t}	Ls,{t}	Ls,{s,t} + Lt,{s,t}	Lent
54.4	54.8	55.2	55.5
✓	54.7	55.0	55.6	55.9
Gain	0.3	0.2	0.4	0.4