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module for the large vehicle target class on DOTA. |
Method C=25 C=50 C=75 |
VAS-DINO (N=36) 4.56 6.75 8.03 |
VAS-ViT (N=36) 4.60 6.82 8.09 |
VAS (N=36) 4.63 6.79 8.07 |
VAS-DINO (N=64) 5.27 8.44 10.45 |
VAS-ViT (N=64) 5.31 8.51 10.48 |
VAS (N=64) 5.33 8.47 10.51 |
E. More Visual Illustration of V AS and the |
Most Competitive Greedy Selection base- |
line Method |
In this section, we provide additional visualization of |
comparative exploration behaviour of V AS and and the most |
competitive greedy selection baseline approach. In figure 7, |
we compare the search strategy with large vehicle as a tar- |
get class. In figure 8, we compare the behaviour with small |
caras a target class. In figure 9, we analyze the exploration |
behaviour with ship as a target class. |
These additional visualizations again justify the efficacy |
of V AS over the strongest baseline method. |
Figure 7: Comparison of policies learned using VAS(left) and the greedy |
selection baseline method (right). |
Figure 8: Comparison of policies learned using VAS(left) and the greedy |
selection baseline method (right). |
Figure 9: Comparison of policies learned using VAS(left) and the greedy |
selection baseline method (right). |
F. Assessment of V AS and other Competitive |
Baseline Methods with a Different Evalua- |
tion Metric |
We additionally compare the search performance of V AS |
with all the baseline methods using a metric, which we call |
Effective Success Rate (ESR) . A na ¨ıve way to evaluate the |
proposed approaches is to simply use success rate , which is |
is the fraction of total search steps Kthat identify a target |
object. However, if Kexceeds the total number of target |
objects in x, normalizing by Kis unreasonable, as even a |
perfect search strategy would appear to work poorly. Con- |
sequently, we propose effective success rate (ESR) as the |
efficacy metric, defined as follows: |
ESR=#Targets Discovered |
min{#Targets , K }(4) |
Thus, we divide by the number of targets one can possibly |
discover given a search budget K, rather than simply the |
search budget. |
F.1. Results on the xView Dataset with ESR as Eval- |
uation Metric |
We initiate our analysis by assessing the proposed |
methodologies using the xView dataset, for varying search |
13 |
budgets K∈{12,15,18}and number of grid cells N∈ |
{30,48,99}. We also consider two target classes for our |
search: small car andbuilding . As the dataset contains |
variable size images, take random crops of 2500×3000 for |
N=30,2400×3200 pixels for N=48, and 2700×3300 for |
N=99, thereby guarantees uniform grid cell dimensions |
across the board. |
Table 16: ESR comparisons for the small car target class |
on the xView dataset. |
Method K=12 K=15 K=18 |
random search (N=30) 0.598 0.632 0.704 |
greedy classification (N=30) 0.619 0.675 0.718 |
greedy selection [30] (N=30) 0.627 0.684 0.729 |
VAS (N=30) 0.766 0.826 0.861 |
random search (N=48) 0.489 0.517 0.558 |
greedy classification (N=48) 0.512 0.551 0.589 |
greedy selection [30] (N=48) 0.524 0.568 0.596 |
VAS (N=48) 0.694 0.722 0.741 |
random search (N=99) 0.336 0.369 0.378 |
greedy classification (N=99) 0.365 0.384 0.405 |
greedy selection [30] (N=99) 0.376 0.395 0.418 |
VAS (N=99) 0.564 0.587 0.602 |
Table 17: ESR comparisons for the building target class on |
the xView dataset. |
Method K=12 K=15 K=18 |
random search (N=30) 0.663 0.681 0.697 |
greedy classification (N=30) 0.701 0.734 0.767 |
greedy selection [30] (N=30) 0.708 0.740 0.786 |
VAS (N=30) 0.854 0.886 0.912 |
random search (N=48) 0.526 0.547 0.556 |
greedy classification (N=48) 0.548 0.569 0.585 |
greedy selection [30] (N=48) 0.552 0.574 0.604 |
VAS (N=48) 0.677 0.716 0.738 |
random search (N=99) 0.443 0.462 0.483 |
greedy classification (N=99) 0.460 0.482 0.504 |
greedy selection [30] 0.469 0.488 0.514 |
VAS (N=99) 0.654 0.676 0.690 |
The results are presented in Table 16 for the small car |
class and in Table 17 for the building class. We see sig- |
nificant improvements in performance of the proposed VAS |
approach compared to all baselines, ranging from ∼15–50% |
improvement relative to the most competitive state-of-the- |
art method, greedy selection .F.2. Results on the DOTA Dataset with ESR as Eval- |
uation Metric |
We also conduct our experiments on the DOTA dataset. |
We use large vehicle andship as our target classes. In both |
cases, we also report results with non-overlapping pixel |
grids of size 200×200and150×150(N=36andN=64, |
respectively). We again use K∈{12,15,18}. |
Table 18: ESR comparisons for the large vehicle target |
class on the DOTA dataset. |
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