SlowGuess/NeurIPS_2023_OCR huggingface dataset:
The showcased visualizations (10, 11, 12, 13, 14) in all these examples demonstrate the superiority of our PSVAS and MPS-VAS framework compared to the E2EVAS baseline, especially in scenarios where search tasks vary from those employed in policy training.
F Analyzing Search Performance Across Multiple Trials
Here, we compare the search performance of E2EVAS, PSVAS, and MPS-VAS across multiple trials. In Figure 15, we present the results when the polices are trained with small car as the target and evaluate the performance under Manhattan distance based query cost  $\mathcal{C} = 25$  with the following target classes: Small Car (SC), Helicopter, Sail Boat (SB), Construction Cite (CC), Building, and Helipad. In figure 16, we present similar results with Manhattan distance based query cost budget  $\mathcal{C} = 50$ . In figure 17, we also present similar results with Manhattan distance based query cost budget  $\mathcal{C} = 75$ .
In Figure 18, we present the results when the polices are trained with large vehicle as the target and evaluate the performance under Manhattan distance based query cost  $\mathcal{C} = 25$  with the following target
Figure 11: Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using E2EVAS (top row), PSVAS (middle row), and MPS-VAS (bottom row). Note that during the training phase, all these policies are trained with large vehicle as the target, while evaluation is conducted using ship as the target.
Figure 12: Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using E2EVAS (top row), PSVAS (middle row), and MPS-VAS (bottom row). Note that during the training phase, all these policies are trained with large vehicle as the target, while evaluation is conducted using ship as the target.
Figure 13: Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using E2EVAS (top row), PSVAS (middle row), and MPS-VAS (bottom row). Note that during the training phase, all these policies are trained with large vehicle as the target, while evaluation is conducted using plane as the target.
classes: Ship, large vehicle (LV), Harbor, Helicopter, Plane, and Roundabout. In figure 19, we present similar results with Manhattan distance based query cost budget  $\mathcal{C} = 50$ . In figure 20, we also present similar results with Manhattan distance based query cost budget  $\mathcal{C} = 75$ .
Figure 14: Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using E2EVAS (top row), PSVAS (middle row), and MPS-VAS (bottom row). Note that during the training phase, all these policies are trained with large vehicle as the target, while evaluation is conducted using plane as the target.
Figure 15: Comparative Search Performance of E2EVAS, PSVAS, MPS-VAS under Distance Based Query Cost  $(\mathcal{C} = 25)$ .
Figure 16: Comparative Search Performance of E2EVAS, PSVAS, MPS-VAS under Distance Based Query Cost  $(\mathcal{C} = 50)$ .
G Search Performance Comparisons Across Datasets
Our experimental outcomes indicate that it is possible to apply our method directly across different datasets without requiring any further modifications or hyperparameter tuning. In the following table 27, we demonstrate this by presenting results of training on one dataset for one target class while evaluating on another dataset and for another target class. We use the number of equal sized grid cells  $N = 64$  and varying search budgets  $\mathcal{C} = \{25, 50, 75\}$ .
Figure 17: Comparative Search Performance of E2EVAS, PSVAS, MPS-VAS under Distance Based Query Cost  $(\mathcal{C} = 75)$ .
Figure 18: Comparative Search Performance of E2EVAS, PSVAS, MPS-VAS under Distance Based Query Cost  $(\mathcal{C} = 25)$ .
Figure 19: Comparative Search Performance of E2EVAS, PSVAS, MPS-VAS under Distance Based Query Cost  $(\mathcal{C} = 50)$ .
Figure 20: Comparative Search Performance of E2EVAS, PSVAS, MPS-VAS under Distance Based Query Cost  $(\mathcal{C} = 75)$ .
Table 27: ANT comparisons when trained with large vehicle on DOTA as target and evaluated with small car, building, and sail boat as target class from xView.
Test with Small Car as Target	Test with Building as Target	Test with Sail Boat as Target
Method	C = 25	C = 50	C = 75	C = 25	C = 50	C = 75	C = 25	C = 50	C = 75
E2EVAS [6]	4.22	6.73	8.07	5.12	8.24	10.50	0.48	0.56	0.92
Online TTA [6]	4.23	6.75	8.10	5.14	8.27	10.53	0.49	0.57	0.95
PSVAS	4.95	7.74	9.45	6.10	9.45	12.31	0.89	1.05	1.54
MPS-VAS	5.07	7.92	9.73	6.18	9.68	12.83	1.02	1.39	1.91