text stringlengths 0 820 |
|---|
Illustration of V AS and the Most Competitive Greedy Se- |
lection baseline Method; (F) Assessment of V AS and other |
Baseline Methods with a Different Evaluation Metric; (G) |
Search Performance Comparison with Other Policy Learn- |
ing Algorithm (PPO); (H) Sensitivity Analysis of VAS; (I) |
Efficacy of TTA on Search Tasks involving Large Number |
of Grids; (J) Saliency map visualization of VAS; |
A. Performance of V AS under Uniform Query |
Cost |
In this section, we report the performance of V AS under |
uniform query cost. The results are presented in the follow- |
ing Table 8 for the small car target class and in Table 9 for |
thebuilding class from the xView dataset. We observe sig- |
nificant improvements in performance of the proposed V AS |
approach compared to all baselines, ranging from 11−25% |
improvement relative to the most competitive greedy selec- |
tionapproach. |
Table 8: ANT comparisons for the small car target class. |
Method C=12 C=15 C=18 |
random search (N=30) 4.57 5.66 6.85 |
greedy classification (N=30) 5.31 6.24 7.25 |
greedy selection [30] (N=30) 5.47 6.45 7.46 |
active learning [37] (N=30) 5.28 6.21 7.22 |
conventional AS [15] (N=30) 4.86 5.97 6.92 |
VAS (N=30) 6.03 7.24 8.24 |
random search (N=48) 3.80 4.97 5.98 |
greedy classification (N=48) 4.69 5.48 6.79 |
greedy selection [30] (N=48) 4.92 5.81 6.98 |
active learning [37] (N=48) 4.68 5.46 6.78 |
conventional AS [15] (N=48) 3.96 5.45 6.14 |
VAS (N=48) 5.62 6.81 7.86 |
random search (N=99) 3.12 3.61 4.45 |
greedy classification (N=99) 3.68 4.22 4.97 |
greedy selection [30] (N=99) 3.81 4.52 5.28 |
active learning [37] (N=99) 3.65 4.19 4.93 |
conventional AS [15] (N=99) 3.24 3.87 4.61 |
VAS (N=99) 4.61 5.64 6.55 |
We also present the results for large vehicle andship tar- |
get class from DOTA dataset in the following Table 10 and |
11 respectively. We see the proposed V AS performs notice- |
ably better than all baselines, ranging from 16–56% relativeTable 9: ANT comparisons for the building target class. |
Method C=12 C=15 C=18 |
random search (N=30) 5.54 7.18 8.58 |
greedy classification (N=30) 5.88 7.72 9.21 |
greedy selection [30] (N=30) 6.39 7.95 9.52 |
active learning [37] (N=30) 5.86 7.68 9.16 |
conventional AS [15] (N=30) 5.76 7.37 8.87 |
VAS (N=30) 7.56 9.02 10.41 |
random search (N=48) 4.97 6.41 7.66 |
greedy classification (N=48) 5.68 6.95 8.40 |
greedy selection [30] (N=48) 5.93 7.26 8.71 |
active learning [37] (N=48) 5.68 6.93 8.37 |
conventional AS [15] (N=48) 5.22 6.67 7.84 |
VAS (N=48) 6.85 8.29 9.65 |
random search (N=99) 4.35 5.37 6.44 |
greedy classification (N=99) 4.92 6.02 7.41 |
greedy selection [30] (N=99) 5.38 6.53 7.79 |
active learning [37] (N=99) 4.91 6.00 7.40 |
conventional AS [15] (N=99) 4.55 5.64 6.75 |
VAS (N=99) 6.75 8.27 9.46 |
to the state-of-the-art greedy selection approach. The exper- |
imental outcomes in different settings are qualitatively sim- |
ilar to the settings under Manhattan distance-based query |
cost. |
Table 10: ANT comparisons for the large vehicle target |
class. |
Method C=12 C=15 C=18 |
random search (N=36) 3.44 4.08 5.19 |
greedy classification (N=36) 3.95 4.62 5.56 |
greedy selection [30] (N=36) 4.18 4.86 5.89 |
active learning [37] (N=36) 3.92 4.60 5.54 |
conventional AS [15] (N=36) 3.71 4.22 5.28 |
VAS (N=36) 5.14 6.05 7.00 |
random search (N=64) 3.40 4.03 5.14 |
greedy classification (N=64) 3.87 4.59 5.55 |
greedy selection [30] (N=64) 3.99 4.77 5.67 |
active learning [37] (N=64) 3.85 4.54 5.51 |
conventional AS [15] (N=64) 3.61 4.12 5.26 |
VAS (N=64) 6.30 7.65 8.90 |
B. V AS Pseudocode |
We have included the pseudocode of our proposed Visual |
Active Search algorithm in table 1. |
C. Policy architecture, training hyperparame- |
ter, and the details of TTA |
In table 12, we detail the VAS policy architecture with |
number of target grids as N. We use a learning rate of 10−4, |
11 |
Table 11: ANT comparisons for the ship target class. |
Method C=12 C=15 C=18 |
random search (N=36) 2.69 3.38 4.46 |
greedy classification (N=36) 3.21 3.99 5.11 |
greedy selection [30] (N=36) 3.44 4.23 5.32 |
active learning [37] (N=36) 3.18 3.95 5.07 |
conventional AS [15] (N=36) 2.97 3.56 4.77 |
VAS (N=36) 4.58 5.34 6.23 |
random search (N=64) 2.54 3.01 4.21 |
greedy classification (N=64) 3.34 3.74 4.94 |
greedy selection [30] (N=64) 3.62 3.95 5.10 |
active learning [37] (N=64) 3.32 3.71 4.93 |
conventional AS [15] (N=64) 2.87 3.38 4.53 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.