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broadly consistent with our observations on the xView |
dataset, with VAS outperforming all baselines by ∼40− |
80%, with the greatest improvement typically coming with |
a higher search budget C. |
5.3. Visualization of V AS Strategy |
In figure 5, we demonstrate the sequential behavior of a |
pretrained V AS policy during inference. We have shaded |
the grid such that darker indicates higher probability. The |
darkest grid at each step is the target to be revealed. In the |
first row we have V AS searching for large vehicles . In step |
1, V AS looks at the roof of a building, which looks very |
similar to a large vehicle in the overhead view. Next in step |
3, it searches a grid with a large air conditioner which also |
looks similar to a large vehicle . Having viewed these twoconfusers, V AS now learns that the rest of the grids with |
building roofs likely contain no large vehicles . It is impor- |
tant to note that this eliminates a large portion of the middle |
of the image from consideration as it is entirely roof tops. |
In step 5, it moves to an area which is completely different, |
a road where it finds a large vehicle . V AS now aggressively |
begins examining grids with roads. In steps 7 through 13 |
it searches roads discovering large vehicle . Finally in step |
15 it explores to a parking lot containing a large vehicle . In |
our middle example we have V AS targeting small cars . In |
step 1, V AS targets a road and fails to find a car. In step |
3, it searches another road in a different region and finds a |
car. Having explored regions with prominent major roads |
it moves to a parking lot in step 5 and finds a car. It now |
searches a similar parking lot in step 7. Having explored |
grids with parking lots it goes back to searching minor roads |
for the duration of its search. V AS does not visit a parking |
lot in the north east corner, but this parking lot is visually |
much different from the other two (i.e. it’s not rectangular). |
In our bottom example we have V AS searching for ships . |
In step 1, V AS searches near a harbor. Having found a ship |
it begins exploring similar harbor regions. In step 3 and |
5 it searches other parts of the same harbor finding ships . |
In steps 7-9, it searches areas similar to the harbor with- |
outships . V AS now learns that ships are not likely present |
in the rest of the dock and explores different regions leav- |
ing the rest of the dock unexplored. These three examples |
demonstrate V AS’s tendency for the explore-exploit behav- |
ior typical of reinforcement learning algorithms. Addition- |
ally, we note that V AS has an ability to eliminate large areas |
that would otherwise confuse standard greedy approaches. |
5.4. Efficacy of Test-Time Adaptation |
One of the important features of the visual active search |
problem is that queries actually allow us to observe partial |
information about target labels at inference time . Here, we |
evaluate how our approaches to TTA that take advantage |
of this information perform compared to the baseline VAS |
without TTA , as well as state-of-the-art TTA baselines dis- |
cussed in Section 4.2 (where FixMatch is adapted to also |
take advantage of observed labels). |
Consider first the case where there is no difference be- |
tween training and test distribution over classes. As before |
we consider xView and DOTA for analysis. The results are |
presented in Figure 6, and show a consistent pattern. The |
TTT approach performs the worst, followed by (out adap- |
tation of) FixMatch , which is only slightly better than TTT. |
Stepwise TTA outperforms both TTT andFixMatch , albeit |
slightly, and Online TTA is, somewhat surprisingly much |
better than all others (this is surprising since it has a lower |
frequency of model update compared to Stepwise TTA ). |
Finally, we consider a TTA setting in which the domain |
exhibits a non-trivial distributional shift at inference time. |
7 |
step 1 |
step 3 |
step 5 |
step 7 |
step 9 |
step 11 |
step 13 |
step 15 |
Figure 5: Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using V AS for different target types. |
Figure 6: Comparative results of TTA methods on V AS framework. |
xView (top; small car target): (left) N=48, (right) N=99. DOTA |
(bottom; large vehicle target): (left) N=36, (right) N=64. |
In this case, we would expect the conventional TTT andFix- |
Match methods to be more competitive, as they have been |
specifically designed to account for distribution shift. We |
model distribution shift by training the search policy using |
one target object, and then applying it in the decision con- |
text for another target object. Specifically, for xView, we |
usesmall car as the target class during training, and build- |
ingas the target class at test time. Similarly, on the DOTA |
dataset we use large vehicle as the target class at training |
time, and use ship as the target at test time. |
The results for the TTA setting with distribution shift are |
presented in Table 6 and 7 for the xView and the DOTA |
dataset respectively, where we also add a comparison to the |
VAS without TTA of any kind. We observe that the results |
here remain consistent, with the proposed Online TTA out- |
performing the other approaches, with Stepwise TTA yield- |
ing the second-best performance.Table 6: Comparative results on xView dataset with small car andBuild- |
ingas the target class during training and inference respectively. |
Method C=25 C=50 C=75 |
without TTA (N=30) 5.28 8.58 11.42 |
TTT [27] (N=30) 5.30 8.61 11.45 |
FixMatch [26] (N=30) 5.31 8.62 11.47 |
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