A Partially-Supervised Reinforcement Learning Framework for Visual Active Search
Authors: Anindya Sarkar
Nathan Jacobs
Yevgeniy Vorobeychik
Venue: NeurIPS 2023 poster (2023)
Keywords: Visual Active Search
Reinforcement Learning

Abstract: Visual active search (VAS) has been proposed as a  modeling framework in which visual cues are used to guide exploration, with the goal of identifying regions of interest in a large geospatial area. Its potential applications include identifying hot spots of rare wildlife poaching activity, search-and-rescue scenarios, identifying illegal trafficking of weapons, drugs, or people, and many others. State of the art approaches to VAS include applications of deep reinforcement learning (DRL), which yield end-to-end search policies, and traditional active search, which combines predictions with custom algorithmic approaches. While the DRL framework has been shown to greatly outperform traditional active search in such domains, its end-to-end nature does not make full use of supervised information attained either during training, or during actual search, a significant limitation if search tasks differ significantly from those in the training distribution. We propose an approach that combines the strength of both DRL and conventional active search approaches by decomposing the search policy into a prediction module, which produces a geospatial distribution of regions of interest based on task embedding and search history, and a search module, which takes the predictions and search history as input and outputs the search distribution. In addition, we develop a novel meta-learning approach for jointly learning the resulting combined policy that can make effective use of supervised information obtained both at training and decision time. Our extensive experiments demonstrate that the proposed representation and meta-learning frameworks significantly outperform state of the art in visual active search on several problem domains.

A Partially Supervised Reinforcement Learning Framework for Visual Active Search
Anindya Sarkar Nathan Jacobs Yevgeniy Vorobeychik
{ (anindya, jacobsn, yvorobeychiki @wust edu, Department of Computer Science and Engineering Washington University in St. Louis
Abstract
Visual active search (VAS) has been proposed as a modeling framework in which visual cues are used to guide exploration, with the goal of identifying regions of interest a large geospatial area. Its potential applications include identifying hot spots of rare wildlife poaching activity. search-and-reseus scenarios, identifying illegal trafficking of weapons, drugs, or people, and many others. State the art approaches to VAS include applications of deep reinforcement learning (DRL), which yield end-to-end search policies, and traditional active search, which com- bines predictions with custom algorithmic approaches. While the DRL framework has been shown to greatly outperform traditional active search in such domains, its end-to-end nature does not make full use of supervised information attained either during training, or during actual search, a significant limitation if search tasks differ significantly from those in the training distribution. We propose an approach that combines the strength of both DRL and conventional active search by decomposing the search policy into prediction module, which produces geospatial distribution of regions of interest based on task embedding and search history, and search module, which takes the predictions and search history as input and outputs the search distribution. We develop novel meta-learning approach for jointly learning the resulting combined policy that can make effective use of supervised information obtained both at training and decision time. Our extensive experiments demon- strate that the proposed representation and meta-learning frameworks significantly outperform state of the art in visual active search on several problem domains.
Introduction
Consider scenario where a child is abducted and law enforcement needs to scan across hundreds of potential regions from helicopter for particular vehicle. An important strategy in such search and rescue portfolio is to obtain aerial imagery using drones that helps detect a target object of interest (e.g., the abductor's car) somewhat poor, making the detection problem extremely difficultt. Moreover, security officers can only inspect relatively few small regions to confirm search and rescue activity, doing so sequentially.
3 5. The quality of the resulting photographs, however, is generally
We can distill some key generalizable structure from this scenario: given a broad area image (often with relatively low resolution), sequentially query small areas within it (e.g., by sending security officers to the associated regions, on the ground) to identify as many target objects as possible. The number of queries Moreover, query results (e.g., detected search and rescue activity in a particular region) are highly informative about the locations of target objects in other regions, for example, due spatial correlation. We refer to this general modeling framework as visual active search (VAS). Numerous other scenarios share this broad structure, such identification of drug or human trafficking sites, anti-pon anti-poaching enforcement activities, identifying landmarks, and many others. Sarkar et al. [6]
make is typically limited, for example, by budget resource constraints.
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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recently proposed visual active search (VAS) framework for geospatial exploration, well as deep reinforcement learning (DRL) approach for learning search policy. However, the efficacy of this approach remains limited by its inability to adapt to search tasks that differ significantly from those the training data, with the DRL. approach struggling in such settings even when combined with test-time adaptation methods.
To gain intuition about the importance of do- main adaptivity in VAS, consider Figure !1 Suppose, we pre-train a policy by leverag- ing a fully annotated search tasks with large vehicle target class learn search pol- icy. Now the challenge in visual active search problem how utilize such a policy for our current task, that is to search for small car given a broad aerial image. As depicted in Figure an adaptive policy (right) initially makes mistake but then quickly adapts to the current task by efficiently leveraging in- formation obtained in response to queries to learning from its mistakes. In contrast, non-adaptive policy (left) keeps repeating its mistakes and ultimately fails to find the region containing the target object.
(a) Search strategy of non- (b) Search strategy of an adaptive policy pre- pre-trained adaptive policy pre-trained on large vehicle as a target. on large vehicle as target.
Comparative search strategy of non-adaptive and adaptive policy with small car as a target.
Figure I:
Indeed, traditional active search approaches have been designed precisely with such adaptivity in mind 7 8 9 [0] by combining an explicit machine learning model that predicts labels over inputs with custom algorithmic approaches aiming to balance exploration (which improves prediction efficacy) and exploitation (to identify
many actual objects of interest possible within limited budget). However, as Sarkar et al. [6] have shown, such approaches perform poorly VAS settings compared to DRL, despite the lack of effective domain adaptivity of the latter.
We combine the best of traditional active search and the state-of-the-ee DRL-based VAS approach by reinforcement learning framework for VAS (PSVAS), A PSVAS
developing a policy architecture comprised of two module: (i) task-specific prediction module that learns to predict the locations of the target object based on the task aerial image and labels resulting from queries during the search process, and (ii) a task-agnostic search module that learns a search policy given the predictions provided by the prediction module and prior search results. The key advantage of this decomposition is that it enables us to use supervised information observed at decision time to update the parameters of the prediction module, without changing the search module. Furthermore, to learn search policies that are effective in the context of evolving predictions during the search, we propose novel meta-learning approach to jointly learning the search module with the initialization parameters of the prediction module (used at the beginning each task). Finally, we generalize the original VAS framework to allow for multiple simultancous queries (a common setting in practice), and develop the PSVAS framework and a meta-learning approach for such settings.
In summary, we make the following contributions:
A novel partially supervised reinforcement learning (PSVAS) framework that enables effective adaptation of VAS search policies to out-of-distributio search tasks.
A novel meta-learning approach (MPS- VAS) to learn initialization parameters of the prediction module jointly with a search policy that is robust to the evolving predictions during a search task. generalization of the VAS problem domains in which we can make multiple simultaneous queries, and a variant of MPS-VA VAS that learns how to choose subset of queries to make in each search iteration.
An extensive experimental evaluation on two publicly available satellite imagery datasets, x View and DOTA, in a variety of unknown target settings, demonstrating that the proposed approaches significantly outperform all baselines. Our code is publicly available at this link
2 Preliminaries
We consider a generalization of the visual active search (VAS) problem proposed by Sarkar et al. ( The basic building block of VAS is task, which centers around acrial image divided into

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N grid cells, so that 2 = (,(0),(())) (M),, with each grid cell a subimage. Broadly, the goal to identify as many target objects of interest through iterative exploration of these grid cells as possible, subject to a budget constraint C. To this end, we represent the subset of grids containing the target object by associating each grid cell with binary label y()) {00, 1}, where y(u) = 1 if the grid cell j contains the target object, and 0 otherwise. The complete label vector associated with .....' ....""".. ()). When we are faced with the task decision time, we have no direct information about y. but when we query grid cell j. we obtain the ground truth label y0 for this cell. Moreover, if y = 1, we also acerue utility from exploring j. In the original variant of VAS, we can make a single query j in each time step. Here, we consider a natural generalization where we have R query resources (for example, R N patrol units identifying traps in a wildlife conservation setting), so that we can make R queries in each time step. We assume that R is constant for convenience; is straightforward to generalize our approach below R time-varying.
the task is y (y(*), .y(")
Let c(j, k) be the cost of querying grid cell A if we start in grid cell j. For the very first query, we can define a dummy initial grid cell d, so that cost function cdd, k) captures the initial query cost. Let q. denote the set of queries performed in step & by a query resource r. Our ultimate goal is to solve the following optimization problem:
max U (x; (q(T)) = ΣV {o
R Σ 120
(I)
S.L. ch sC SC.
Finally, we assume that we possess a collection of tasks (in this case, acrial images) for which we have annotated whether each grid cell contains the target object or not. This collection, which we will refer to as {(x., y/ }. is comprised of images x; with corresponding grid cell labels ys, where
each 2; is composed of N elements (x) (1) x (2)
(N) representing the cells in the image, and (1) (2) y1 )
each y contains! N corresponding labels (v, 1/
The central technical goal in VAS is to learn an effective search policy that maximizes the total number of targets discovered, on average, for sequence of tasks æ on which we have no prior direct experience, given a set of resources R. exploration cost function c(j. k), and total exploration budget C. Sarkar al. [6] proposed a deep reinforcement learning (DRL) approach in which they learned a search policy π o, B) that outputs at each time step t the grid we should explore at the next step + 1, given the task æ, remaining budget B, and information produced from the sequence of previous queries encoded into an observation vector o with o05) 22y( if j has been explored, and o(d) otherwise. The reward function for this DRL approach naturally captured by R(z, o, = =y())
Note that active search (including VAS) is qualitatively distinct from active learning I 12 L in the latter, the goal is solely to learn to predict well, that the entire query process serves the goal of improving predictions. In active search, in contrast, we aim to learn a search policy that balances exploration (improving our ability predict where target objects are) and exploitation (actually finding such objects) within a limited budget. Indeed, it has been shown that active learning approaches are not competitive in the VAS context (6)
3 Partially-Supervised Reinforcement Learning Approach for VAS
The DRL approach for VAS proposed by Sarkar al. 6 end-to-end, producing a policy that is only partly adaptive to observations o made during exploration for each task. In particular, this end-to-end policy aims to capture the tension between exploration and exploitation fundamental in active search 12, without explicitly representing the central aim of exploration, which is to improve our ability to predict which grid cells in fact contain the target object. In conventional active search, in contrast, this is directly represented by learning a prediction function fz2(1) that is updated each time grid j is explored during the search process, so that exploration directly impacts our ability to predict target locations, and thereby make better query choices in the future. While the end-to-end approach implicitly learns this, it would only do so effectively so long as tasks we face at prediction time are closely related to those used in training. However, does not take advantage of the supervised information we obtain about which grid cells actually contain the target object, either at training or
3

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Triput with N Crids
Task Specific Prediction Module
IX1
2D Max
IXI
ResNet 34 (frozen)
Conv
Dx14x14
Flatten
DXTM7
DNA
NEW
MLP
Past Search
Tile
Reshape
Outcomes
Tile
Task Agnostic Search Module
[n_) off) sor. D
xaho
Flatten
Remaining Query Budget
Tile
MLP
Update
Figure 2: The PSVAS policy network architecture.
test time, potentially reducing the efficacy of learning as well as ability to adapt when task distribution changes.
To address this issue, we propose a novel
reinforcement learning approach for visual active search, which refer to PSVAS, In PSVAS, the search policy
obtain is
composition of two modules: 1) the task-specific prediction module fo (z. o) and 2) the task-agnostic search module (p, o, B), where @ and are trainable parameters, where p (z. 32, o) is the vector of predicted probabilities with p(i) the predicted probability of a target in grid cell j (see Figure 2 and Supplement Section A for further details about the policy network architecture). Conceptually, fo information gathered during
makes predictions based solely on the task x and the prediction-rele the search o, while g relies solely on the information relevant to the search itself: predicted locations of objects p. observations acquired during the search o, and remaining budget B. The search policy is B).
then the composition of these modules, T T B) go
Although in principle we can still train the policy æ above end-to-end (effectively collapsing the composition), a key advantage of PSVAS is that it enables us to directly make use of supervised information about true labels y as they are observed either at training or decision time, using these to update fe in both cases. In prior work that relies solely on end-to-end policy representation, there was no straightforward way take advantage of this information. Specifically, we train the policy using the following objective function:
CPARVS LR. AL
where À is a hyperparameter, We represent LRL loss as follows: M
ΣΣ V --
1 clanc(a-1.9))) V log (100,))(a o, B;)
Where M is the number of example search task seen during training and R. is the discounted cumulative reward defined as RQ -I= with discount factor y [0, 1]. Note that the RL loss in this approach also updates the parameters of the prediction module, 0, in an end-to-end fashion. We also represents LACE as follows: LBCE ΣM - (vi log(p:) (1 y:) log(1 pt)).
The PSVAS algorithm is more formally presented in Algorithm T The combined RI. and supervised loss yields a balance between using supervised information to improve the quality of initial predictions the beginning of the episode and ensuring that these serve the goal of producing the best episode- level policies. However, it is still crucial to note that fo serves solely an instrumental role in the process, with learning a search policy that effectively adapts each task the primary goal. Consequently, we ultimately wish to jointly learn 9s and fe in such way that fo facilitates adaptive search as it is updated during a search task at decision time. To address this, we propose a meta- learning approach, MPS- VAS, that learns Go along with an initial parametrization On of so for each task. At the beginning of each task, then, is initialized to Og. and then updated as labels are observed after each query.
At the high level, MPS- VAS trains over series of episodes, where each episode corresponds to a fully labeled training task (I,, y:) and budget constraint C (we vary the budget constraints during training). We begin an episode with the current prediction function parameters 0, 0 and search policy parameters ø. We fix Is over the length of the episode, and use it to generate sequence of queries (since the policy is stochastic, it naturally induces exploration). After observing the label y()

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Algorithm The PSVAS algorithm.
Require: A search task instance (a,,):); budget constraint C; Prediction function (/) with current parameters Oz. i.e. fa,; Search policy (g) with current parameters @i, i... 96.i
1: Initialize o [O...0]; B C; == 0
2: while B" > do
3:
(2).oo))
4: j - Sample Unespioood Gride) Do; (Ü. O B')]
5:
Query grid cell with index and observe true label y(o).
Obtain reward R² y(d), Update of to oy with o(6) 2y and update B' to B with
B = B² -ck,j) (assuming we query k'th grid at (t (t-11] (t).
7:
Collect transition tuple (7) step i.e., -' = (state (I,, B¹), action reward = R², next state
(0,,0"") ).
8:
1+1
9: end while
10: Update the prediction and search policy parameters. i... 0g and o; using (Cpevas) based the collected transition tuples (r') and the collected labels (y(²) throughout the episode.
11: Return updated prediction and search policy parameters, ic., Pee and o..1 respectively.
for each queried grid cell j during the episode, we update fo, using standard supervised (binary cross-entropy) loss. At the completion the episode (once we have exhausted the search budget C), we update the policy parameters, as well the initialization prediction function parameters by combining RL and supervised loss. For the search module, we use the accumulated sum of rewards RG Σ, ( over grids explored during the episode, with the RL loss Lu based on the REINFORCE algorithm [14]. For the prediction module, we use the collected labels y()) during the episode and the standard binary cross-entropy loss. Finally, the MPS- VAS loss also explicitly trades off the RL and supervised loss: LMPPARVS (CRL. XL ) The proposed meta- meta-learning approach thus explicitly trains the policy to account for the evolution of the prediction during the episode. The full MPS-VAS VAS is presented more formally in Algorithm
Algorithm 2 The MPS- VAS meta-learning algorithm.
Require: A search task instance (æs,3i): budget constraint C; Prediction function (/) with current parameters 0f 0,, i... fop: Search policy (g) with current parameters du. i.e. Io.i
1: Initialize 0 [0...0]; ; B =C; == 0
2: while B" do
3: Jo (x;,,0)
j j- Sample Unerplored Grady[gaa (Ú. B¹)]
5: Query grid cell with index and observe true label y(
6:
Update 8 to 85+1 using Lace loss between ÿ and pscudo label j. defined
y
of is Observed
glt
(1) Unobserved.
Obtain reward IR y(d), Update of to c1 with of.) 2y and update B' to B with B = B c(k,j) (assuming we query k'th grid at (t (6-1] 1)).
8:
Collect transition tuple r step I, i.e., r' = state (Ix, 0", BB), action = reward = R², next state
(o, 4e1 ).
9:
1+1
10: end while
11: Update search policy parameters é; using (CRr. based the collected transition tuples (r*) throughout the episode and update initial prediction function parameters O using (Lecs) based on the collected labels
(i) throughout the episode.
12: Return updated prediction and search policy parameters, i.c., 0+t1 and di+1 respectively.
The search policy x produces probability distribution over grid cells. However, since in our setting no advantage can gained by querying previously queried grid cells, we simply renormalize probability distribution induced by и over the remaining grid cells, both during training and at decision time. Note that during inference, both PSVAS and MPS- VAS framework, we freeze the parameters of the search module (ø) and update the parameters of the prediction module (0) after
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observing query outcomes at each step using LBCE loss between predicted label and pseudo label as shown in step (6) of Algorithm
The discussion thus far effectively assumed that R = 1, that is, we make only a single query in each search time step. Next, describe the generalization of our approach when have multiple query resources Rt > 1. First, note that the prediction module fa is unaffected, since the number of query resources only pertain the actual search strategy 9á- One way to handle R queries is to simply sample the search module (which is stochastic) iteratively R times without replacement during training, and greedily choose the most probable grid cells to query decision time. We refer to this as MPS- VOS-TOPK. However, since the underlying problem is now combinatorial (the choice of R queries out of N grid cells), such greedy policy may fail to capture important interdependencies among such search decisions.
To address this, propose novel policy architecture which is designed to learn how to optimize such a heuristic greedy approach for combinatorial grid cell selection in a way that is non-myopic and accounts for the interdependent effects of sequential greedy choices. Specifically, let eb be vector corresponding to the grid cells, with 3 = 1 if grid cell j has either been queried in the past (during previous search steps), has been already chosen be queried in the current search step, and 35 0 otherwise. Thus, & encodes the choices that have already been made, and enables the policy to learn the best next sequential choice to make using a greedy procedure through the same RL framework that we described above. We refer to this approach as MPS- VAS-MQ (in reference to multiple queries).
Experiments
4.1 Experiment Setup
Since the goal of active search is to maximize the number of target objects identified, we use average number of targets identified through exploration (ANT) as our evaluation metric.
We consider two ways of generating query costs: (i) j) = 1 for all i,j, where C is just the number of queries, and (ii) j) is based Manhattan distance between i and j. Most of the results present in the main paper reflect the Manhattan distance based setting; we also report the results for uniform query costs in the Supplement. We use À = 0.1 in all the experiments. We present the details of policy architecture and hyper-parameter details for each different experimental settings in the Supplement.
Baselines We compare the proposed PSVAS and MPS-VAS policy learning framework to the following baselines.
Random Search (RS), in which unexplored grid cells are selected uniformly at random.
Conventional Active Search (AS) proposed by Jiang et. al. [9] using a low-dimensional feature representation for each image grid from the same feature extraction network as in our approach. Greedy Classification (GC), which we train classifier dhe to determine whether grid contains a target object. We then prioritize the search in grids with the highest probability of containing the target object until the search budget is depleted.
Active Learning (AL), in which the first grid is selected randomly for querying, and the remaining grids are chosen using a of-te-o--the-as art active learning approach by Yoo et al. [33 until the search budget is saturated.
Greedy Selection (GS), proposed by Uzkent et al. [5), that trains a policy Φas to assign probability of zooming into each grid cell j. We use this policy to select grids greedily until the search budget exhausted.
End-to-end visual active search (E2EVAS), the state-of-the-art approach for VAS proposed by Sarkar et al. [6].
When dealing with multi-query scenario, we compare the effectiveness of MPS- VAS-TOPK and VAS-VVASMQ.
Datasets We evaluate the proposed approach using two datasets: x View [66 and DOTA 17
x View a satellite imagery dataset which consists of large satellite images representing 60 categories, with approximately 3000 pixels in each dimensions. We use 67% and 33% of the large satellite

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Table 1: ANT comparisons when trained with small car as target on x View in single-query setting,
Test with Helicapter as Target
Test with SB as Target
Test with Building as Target
Method
C 2G
C 50
C 7G
C= 25
C 50
C 75
C 25
C 50
C 75
RS
0.16
0.39
0.72
0.35
0.71
1.02
2.41
3.56
4.62
GC
0.29
0.55
0.93
0.52
0.95
1.21
3.94
5.14
6.61
GS
0.41
0.68
1.08
0.61
1.26
4.51
5.80
6.82
AL 1
0,27
0.54
0.92
0.52
0.93
1.18
3.92
5.12
6.60
0.25
0.51
1.20
6,34
E2EVAS G
0.53
0.83
1.25
0.67
1.10
1.30
5.85
9.26
11.96
O
0.54
0.84
1,26
0.68
1,10
1.32
5.86
9.26
11.98
PSVAS
0.87
1,28
0,93
1,66
6.81
10.53
MPS-VAS
0.92
1.13
1,38
1,07
1.67
2.10
6,83
10.59
13.64
CC as Target
with SC Target
with Helipad as Target
Method
C 25
C
50
75
C= 25
C = 50
C 75
C 25
C= 50
75
RS
0.57
1.18
1.72
1.80
3.40
5.11
0.19
0.42
0.72
GC
1.05
2.14
2.64
7.04
1.04
GS
[65]
1.12
1.97
2.48
3.35
5.39
7.52
0.54
0.93
1.12
AL
[B
1.04
1.77
2.12
2.62
4,88
7.03
0.40
0.76
1.03
AS
g
1.02
1.61
2,03
2.43
4.61
6.95
0.38
0.75
0.98
E2EVAS 6
1.43
2.31
2.98
4.73
7.43
9.59
0.81
1.20
1.46
OnlineTTA i
1.43
2.33
2.99
4.75
7.44
9.59
0.83
1.21
1.46
PSVAS
1.62
2.49
3.14
5.51
8.33
10.52
0.91
1.22
1.47
MPS-VAS
1.74
2.64
3,47
5.55
8.40
10.69
0.96
1.30
1.63
images to train and test the policy network respectively. DOTA is also satellite imagery dataset. We rescale the original 3000 x 3000pr images to 1200 1200pz.
4.2 Single Query Setting
We begin by considering setting with single query resource, as in most prior work. We first evaluate the proposed method on the x View dataset with varying search budget C e (25, 50, 75) and the number of equal sized grid cells N 49. We train the policy with small car as target and evaluate the performance of the policy with the following target classes Small Car (SC), Helicopter, Sail Boat (SB), Construction Cite (CC), Building, and Helipad. As the dataset contains variable size images, we take random crops of 3500 x 3500 for N = 49, ensuring equal grid cell sizes. We present the results with different values of N in the Supplement. The results with N 49 are presented in Table [ We observe significant improvements in performance of the proposed PSVAS approach compared to all baselines in each different target setting, ranging from 3 to 25% improvement relative to the most competitive E2EVAS method. The significance of utilizing supervised information of true labels y, which are observed after each query at inference time, is supported by the obtained results. This highlights the effectiveness of the PSVAS framework, which enables to update task-specific prediction module f by leveraging such crucial information in an efficient manner. The experimental outcomes also indicates two consistent trends. In each target setting, the overall search performance improves as C increases, and the relative advantage of MPS- MPS-VAS over PSVAS increases, as it is better able to exploit the observed outcomes and in turn improve the policy further for the larger search budget C. We also observe that the extent of improvement in performance is greater when there is greater difference between the target class used in training and the used during inference. For example, when the target class is a sail boat, the improvement in performance of MPS- VAS in comparison PSVAS ranges between 15% to 35%. However, if the target class a small car, the improvement in performance of MPS-VAS is only between 1% to 2%. Considering all different target settings, performance improvement of MPS- VAS in comparison to PSVAS, ranges from 1% to 35%. The results demonstrate the efficacy of the MPS-VAN VAS framework in learning a search policy that enables adaptive search. Figure demonstrates the exploration strategies of different policies that are trained on small car as the target class and test on sail boat as the target. The figure showcases the different exploration behaviors exhibited by each policy in response to the target class, highlighting the impact of the proposed adaptive search framework on the resulting exploration strategies. In Table 2 we present similar results on the DOTA dataset with N as 64. Here, we train the policy with Large Vehicle as target and evaluate the policy with the following target classes: Ship, Large Vehicle (LV), Harbor, Helicopter, Plane, and Roundabout (RB). We observe that MPS-VAL VAS approach significantly outperforms all other baseline methods across different target scenarios. This shows the importance of MPS-VAS framework for deploying visual active search when search tasks differ significantly from those that are used for training.
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Table 2: ANT comparisons when trained with large vehicle as target on DOTA in single-query setting.
Test with Ship as Target
Test with L.V as Target
Test with Harbor as Target
Method
C 25
50
C 75
C 25
C 50
C 75
C 25
C 50
C. 76
Random
1.08
2.11
3.06
1.48
2.96
3.91
1.41
2.72
3.90
GC
1.52
3.04
4.19
2:59
3.77
5.48
1.90
3.77
5.02
GS L5
1.79
2.72
4.10
4.14
5.87
AL 13
1.51
3.02
4,18
2.57
3.74
5.47
1,89
3,74
5.01
VAS
2.45
4.37
5.87
5.33
8.47
10.51
3.12
5.04
6,82
O
2.46
5,33
8.47
10.52
3,12
5,06
6883
PSVAS
2.46
5.33
8.52
5.24
MPS-VAS
3,08
5.25
7.13
5.34
8.53
10.63
3.82
6.77
9.00
Helicopter Target
with Target
Roundabout Targel
Method
C
C 50
c 75
C
C= 75
25
C
Random
0.27
0.46
0.71
1.37
2:55
3.62
1.47
2.53
3.81
GC
GS 15
0.42
0.66
1.01
2.26
3,56
4.71
1.91
3.24
4.68
AL F3
0.37
1.94
3.14
3,99
2.82
4.21
9S
0.34
0.53
0.82
1,89
3,06
3.92
1.55
2.71
4.06
E2EVAS 8
0.47
0.72
1.05
3.07
4,87
6.34
3.05
4,94
6.34
OnlineTTA TA
0.48
0.72
3,08
4.89
6,34
4,95
6.36
PSVAS
0.50
0.73
1.10
3.09
4,96
6,38
3.09
4.96
6.39
MIS-VAL VAS
0.63
1.03
3.46
5.57
7.71
3.46
5.57
7.71
step
step
step
Figure 3: Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using E2EVAS (top row), PSVAS (middle row), and MPS-VAS (bottom row).
4.3 Multi Query Setting
Next, we evaluate the proposed MPS- VAS-MO approach on the View and DOTA dataset multi query setting. In Table B we present the results with varying search budget C {25, 50, 75} and the number of equal sized grid cells N 49. We consider perform in different target settings. We observe two general consistent trends across different target settings. First, the search performance of MPS-VAS performance of MPS-VAS-MA VAS-MQ in multi query settings, due to the fact that in single query setting the task-specific prediction module updated times more frequently than in the multi-query setting. Second, across various target settings, the performance improvement of MPS- MPS-VAS-ME VAS-MQ over MPS- VAS- ranges from 0.08% to 3.5%. Table we present similar result with the number of grid cells N 64 and train the policy with large vehicle as the target class. We report the results with different values of in the Supplement. Here the improvement of MPS VAS-MQ over PPSS-VSSTOOP up to 3.5% across different target settings, suggesting that there is added value from learning to capture interdependence in greedy search decisions.
in all the experiments we
single query setting always better than the
4.4 Effect of À Search Performance
We perform experiments with different choices of À and found À = 0.1 to be the best choice across all different experimental setup. comparison, here we report the results the case when we train the policy with different values of À using small car target class and test the policy with

---

Table 3: ANT comparisons when trained with small car as target on View in multi-query setting.
Test with Helicopter as Target
Test with SB as Target
Test with Building as Target
Method
C 25
C- 50
C 75
C 25
C= 50
C 75
C 25
C 50
C 75
MPS. VAS-TOPP TOPK
0.55
0.75
1.12
0.97
1.38
1.93
6.68
10.35
13.30
MPS-VAS-MQ
0.63
0.83
1.17
0.97
1.40
1.97
6.82
10.48
13.31
Test with CC Target
Test with SC Target
Test with Helipad Target
Method
C 75
C
C
25
50
C
MPS-VAS- VAS-TOPK
1.63
2.59
3,36
5.41
8.28
10.30 10.38
0.81
1.07
1.23
MPS-VAS-MQ
1.67
2.60
3,38
5.47
8.34
0.84
1.14
1.30
Table 4: ANT comparisons when trained with large vehicle as target on DOTA in multi-query setting.
Test with Ship as Target
Test with LV as Target
Test with Harbor as Target
Method
C 25
C- 50
C 75
C. 25
C 50
C 75
C 25
C - 50
C - 75
MPS VAS TOPK MPS-VAS-MQ
3.03
5.14
6.83
5.32
8.46
10.56
3.69
6.49
8.71
3.05
5.18
6.88
5.33
8,50
10.61
3,72
6.59
8.75
Test with Helicopter Target
Test with Plane Target
Test with RB Target
Method
C -
C
C- 75
C - 25
C
C
C 25
C - 50
C
MPS- VAS TOPK
0.57
0.96
1.39
3,38
5.43
7.64
3,29
5.43
7.62
MPS-VAS-MQ
0.60
1.01
1.46
3,41
5.48
7.66
3,38
5.50
7.66
small car, building, and sail boal as target on x XView. We evaluate the policy with varying search budgets C e {25, 50, 75) and the number of equal sized grid cells N = 49. In table we provide the result for the PSVAS framework with small car as target. In ta we provide similar result for the MPS-VA framework. Our empirical findings across all the experimental settings are quite consistent, and justify the choice of X = 0.1.
Table 5: ANT comparisons when trained with small car as target for different values of À using the PSVAS Framework.
Test with Small Car as Target
Test with Building as Target
Test with Sail Beat as Target
A
C 25
C= 5U
C 75
C= 25
C= 50
C= 75
C 25
C= 50
C= 75
0.001
4.96
7.75
9.74
6.08
9.64
12.35
0.74
1.43
0.01
5,02
7,87
9.96
6.37
9.95
12.77
0.88
1.19
1.54
0.1
5,51
8.33
10.52
10.53
13.44
0.93
1.66
1.0
5.10
7.98
10.04
6.39
10.16
12.81
0.89
1.20
1.59
Table 6: ANT comparisons when trained with small car as target for different values of À using the MPS-VAS Framework.
with Small Car as Target
Test with Building as Target
Test with Sail as Target
A
C 25
C 50
C 75
C - 25
C 50
C 75
- 25
C - 50
C - 75
0.001
4.99
7,82
9.90
6.15
9.74
12.44
0.83
1.22
1.53
5.06
7,93
10.09
1.87
0.1
5,55
8,40
10.69
6.83
10.59
13.64
1,07
1.67
2.10
1.0
5.12
8.01
10.12
6.46
10.21
12.96
1.01
1.52
1.90
4.5 Effectiveness of Task Specific Prediction Module on Search Performance
We analyse the importance of the task-specific prediction module in Sections D.I and D.2 of Supplementary Material by freezing the prediction module parameters during inference time. Here, we additionally analyse the efficacy of the task task-speeific prediction module by setting À while training the policy. We call the resulting policy USVAS (Un-Supervised VAS). We observe a significant drop in performance across all settings, demonstrating the importance of the Supervised prediction module in order to learn an effective search policy. Specifically, in the following table we present the results when the policy is trained with small car on x View as a target, while the performance of the policy is evaluated for the following target classes: Small Car (SC), Helicopter, SailBoat (SB), Construction Site (CS), Building, and Helipad. We evaluate the policy with varying search budgets Ce E (25, 50, 75} and the number of equal sized grid cells N 49.

---

Table 7: ANT comparisons when trained with small car as target on x View.
Test with Small Car as Target
Test with Building as Target
Test with Sail Boat us Target
Method
C 25
C- 50
C 75
C= 25
C= 50
C 75
C 25
C= 50
C= 75
USVAS
4.77
7,46
9.61
5.86
9.37
12.05
0.64
1.08
1.27
PSVAS
5,51
8.33
10:52
6.81
10.53
13.44
0.93
1.23
1.66
MPS-VAS
5.55
8.40
10.69
10.59
13.64
1,07
2.10
Test with Helicopper as Target
Tost with CS as Target
Test with Helipad as Target
Method
C = 26
C- 50
C 75
C 25
C- 50
C- 75
C 25
C- 50
C- 75
USVAS
0.53
0.84
1.19
1.44
2.27
2.99
0.80
1.16
1.42
PSVAS
0.87
1.08
1.28
1.62
2.49
3.14
0.91
1.22
1.47
VAS
0.92
1.13
1.38
2.64
3,47
1.63
Related Work
RL for Visual Navigation RL has found broad applicability in visual navigation tasks 18
T9 20
21
While these tasks share some similarities at high level, such as requiring a sequence of visual navigation steps based on local view of the environment, they often do not involve search budget constraints and rely on a predetermined kinematic model of motion. In contrast, our approach involves observing the full environment, albeit potentially at lower resolution, and sequentially determining which regions to query without being constrained to particular kinematic model. This highlights the distinctive nature of our approach compared traditional visual navigation tasks and demonstrates the potential value of active search strategies in addressing budget-constrained settings.
Active Search Active Search was first introduced by Garnett et al. 7 as a means to discover members of valuable and rare classes rather than solely focusing on learning an accurate model as in Active Learning [1I] Subsequently, Jiang et al.
p 22 proposed efficient nonmyopic active search techniques and incorporated search cost into the problem. Sarkar et al. [6] demonstrate that prior active search techniques do not scale well in high-dimensional visual space, and instead propose a DRL based visual active search framework for geo-spatial broad area search. However, the efficacy of the proposed approach by Sarkar et al. 6 can be limited when the search task varies between training and testing, which is often the case in real-world applications. In this work, we propose novel framework that enables efficient and adaptive search in any previously unseen search task. Meta Learning The concept of meta- leaanne..,,ng, has consistently attracted attention in the field of machine learning (3 24 25 26 Finn et al. [25) present Model Agnostic Mleta-Learning, a technique that utilizes SGD updates rapidly adapt to new tasks. This approach, based on gradient-based meta- learning, can be seen as learning an effective parameter initialization, enabling the network to achieve good performance with just few gradient updates. Wortsman et al. 22 introduces a self-adaptive visual navigation approach, which has the ability to learn and adapt to novel environments without the need for explicit supervision. Our work is significantly different than all these prior works we observe true label at each step during search and hence the main challenge is how to leverage the supervised information in order to learn an efficient adaptive search policy.
Foveated Processing of Large Images Several studies have investigated the utilization of low- resolution images for guiding the selection of high-resolution regions to process (8 29
32]
33
35 with some employing reinforcement learning techniques 36 15] to improve this process.
However, our work differs significantly, as we focus on selecting sequence of regions to query, where each query provides the true label, instead of a higher resolution image region. These labels are crucial for guiding further search and serving as an ultimate objective. As such, our approach tackles a unique challenge that differs from existing methods that rely on low-resolution imagery.
Conclusions
We present a novel approach for visual active search in which we decompose the search policy into a prediction and search modules. This decomposition enables us to combine supervised and reinforce- ment learning for training, and make use of supervised learning even during execution. Moreover, we propose a novel meta-learning framework to jointly learn a policy and initialization parameters for the supervised prediction module. Our findings demonstrate the significance of the proposed frameworks for conducting efficient search, particularly in real-world situations where search tasks may differ significantly from those utilized during policy training. We hope our framework will find its applicability in many practical scenarios ranging from human trafficking to animal poaching.
10

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Acknowledgments
This research was partially supported by the NSF (IIS- 19051995558 IIS-1903207, and IIS-2214141), ARO (W91INF-18-1-020) Amazon, NVIDIA, and the Taylor Geospatial Institute managed by Saint Louis University.
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A Partially Supervised Reinforcement Learning Framework for Visual Active Search: Supplementary Material
A Policy Network Architecture and Hyperparameter Details
Recall that the policy network æ is composed of two parts (1) a task specific prediction module, and (2) a task-agnostic search module. The task specific prediction module consists of an encoder e(æ:1) that maps the aerial image x to a low-dimensional latent feature representation 2, and a grid prediction network p(z, o; k) that predicts the probabilities of grids containing a target by leveraging the latent semantic feature z and the outcomes of previous search queries o. Note that the task specific prediction module is represented as fax, o, f) p(z e(2; 17), o; k), where 0 = (17, r)). Following (6), we use frozen ResNet-34, pre-trained on ImageNet, followed by learnable x convolution layer with ReLU activation as a feature extraction component of the task specific prediction module that we refer as encoder e(.). We then combine the latent semantic feature Z with the previous query information We apply the tiling operation in order to convert into representation with the same dimensions as the extracted features 2, enabling us to effectively apply channel-wist concatenation of latent image feature and auxiliary state feature while preserving the grid specific spatial and query related information. This combined representation is then fed to a grid prediction network comprises of a 1 1 convolution layer, flattening, and a MLP block consists of 2 fully connected layer with ReLU activations. Note that the output of grid prediction network is of dimension N. We finally apply sigmoid activation to each output neuron convert them into probability value representing the probability of the grids containing target. The proposed policy architecture is depicted in figure of the main paper.
We re-shape the output of task specific prediction module by converting it back from 1D to 2D of shape (m x n) N before feeding it to the task agnostic search module g(.) that takes the following three inputs: (1) the reshaped 2D output of the task specific prediction module, which is the probabilities of grids containing target; (2) the remaining search budget B, which is scalar but we apply tiling to the scalar budget B to transform it to match the size of the reshaped 2D output of the task specific prediction module; (3) we also apply the tiling operation to o in way that allows us to concatenate the features (z, o, B) along the channels dimension to finally obtain the combined input task agnostic search module. The task agnostic search module
representation that serves is composed of flattening, a MLP block consists of fully connected layer with ReLU activations, and final softmax layer to convert the output to probability distribution that guides in selecting the grid to query next.
In Table 8, we detail the architecture of task specific prediction module (f) of PSVAS policy network. In Table D] we detail the architecture of task agnostic search module (g) of PSVAS policy network. Note that, the task specific prediction module and task agnostic search module remains unchanged in MPS- VAS framework.
Table 8: Task Specific Prediction Module Architecture with number of grid cell N (m n)
Layers
Configuration
o/p Feature Map size
Input
RGB Image
3 x 3500 x 3500
Encoder
ResNet-33
512 x 14 x 14
Convl
Channel:N; kernel size:1 x
N x 14 x 14
2D MaxPool
Pooling size:2 x 2
N x 7 x 7
Tile1
Grid State (o)
N x 7 7
Channelwise Concat
Conv1,Tilel
(2N) x 7
Conv2
Channel:3; kernel size: 1 x
x x 7
Flattened
Conv2
147
FC1+ReLU
(147- > 2N)
2N
FC2+Sigmoid
(2N- N)
N
14

---

Table 9: Task Agnostic Search Module Architecture with number of grid cell N (m n)
Layers
Configuration
o/p Feature Map size
Input 1
2D Reshape of Task Specific Prediction Module Output
1xmxn
Input 2: Tile2
Grid State (o)
1xmxn
Input 3: Tile3
Query Budget Left (B)
1 IXmxX n
Input: Channelwise Concat
Input 1, Input 2, Input
(3) m X
Flattened
Input: Channelwise Concat
(3) m x
FCI+ReLU
(K 2N)
2N
FC2+Softmax
(2N N)
N
In MPS-VAS VAS-MQ framework, the network architecture of task specific prediction module remains unaltered, but the additional dependence of task agnostic search module (t) on sp enforce slight modification of architecture as detailed Table 10
Table 10: Task Agnostic Search Module Architecture in multi query setting with number of grid cell N (m n
Layers
Configuration
o/p Feature Map size
Input 1
2D Reshape of Task Specific Prediction Module Output
1 1xmxx mn
Input 2: Tile2
Grid State (o)
1xmxn
Input 3: Tile3
Query Budget Left (B)
1xmxn
Input 4: Tile4
Encoded Locations the queried Grid cells (1)
1 1xmxn n
Input: Channelwise Concat
Input Input 2, Input 3, Input 4
(4) m
Flattened
Input: Channelwise Concat
(4) m x
FC1+ReLU
(n) 2N)
2N
FC2+Softmax
(2N- N)
N
We use learning rate of 10"4, batch size of 16, number of training epochs 200, and the Adam optimizer to train the policy network in all experimental settings. During Inference, in all experimental settings, we update the parameters of task specific prediction module f after each query step using a learning rate of 10-4 and the Adam optimizer. We use 1 NVidia A A100 and 3 GeForce GTX 1080Ti GPU servers for all our experiments.
B Results with Uniform Query Cost
B.1 Single Query Setting
Here we present the results by considering setting with a single query resource and query costs (ii,j) = for all i,j. where is the number of queries. We evaluate PSVAS and MPS-VAS on the xView dataset with varying search budget и {12, 15, 18) and the number of grid cells N 49. We train the policy with small car as the target and test the performance the policy with the following target classes Small Car (SC), Helicopter, Sail Boat (SB), Construction Cite (CC), Building, and Helipad. The results are presented in Table [I We observe noticeable improvement in performance of the proposed PSVAS approach compared all baselines in each different target setting, ranging from approximately 0.50 to 52.0% relative to the most competitive E2EVAS baseline. In Table 12 we report the results on DOTA dataset with N 64. In this setting, we train the policy with large vehicle as the target and evaluate the performance with the following target classes Ship, large vehicle (LV), Harbor, Helicopter, Plane, and Roundlaoout. Here, we notice significant improvement performance of PSVAS compared to all the baselines including E2EVAS, ranging from approximately 3.5 to 25.0%. The effectiveness of PSVAS framework becomes evident it allows us to efficiently update the task task-specific prediction module by leveraging the crucial supervised information. We
15

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also observe consistent trend, i.e., the performance of MPS- VAS is significantly better than PSVAS across different target settings, ranging from approximately 0.6 60.0%. The significance of the MPS- VAS framework becomes apparent when deploying visual active search in scenarios where the search tasks differ substantially from those encountered during training.
Table 11:
ANT comparisons when trained with small car target x xView in single-query setting.
Test with Helicopter Target
with Target
Test with Building Target
Method
C 15
18
C
= 15
C=
12
15
C
RS
0.41
0.52
0.65
0.62
0.83
0.93
4,74
6.05
7.11
GC
5.45
GS
[15
0.47
0.61
0.84
0.78
0.96
1.03
5.68
6.87
8.01
0.43
7.63
AS
9
0.44
0.57
0,75
0,70
0.89
0.96
5.32
6.38
7,44
E2RVAS x
0.50
0.63
0.92
0.83
1.06
1.10
7.29
8.78
10.14
Onl
0.50
0.64
0,93
0.84
7.29
8.79
10.15
PSVAS
0.91
0.95
1.08
0.97
1.13
1.37
7.30
8.81
10.28
1.23
7.32
Teat with CC as Target
Test with SC Target
Test with Helipad as Target
Method
C
C=
18
C=
15
C
C
C 15
C
1.19
GC
1.42
1.86
2.19
4.06
4.98
6.03
0.51
0.65
0.83
GS 15
1.61
2.01
2.33
4.59
5,54
6.71
0.56
0.74
0.96
AL
1B
1.41
1.85
2.17
4.03
4,96
6.022
0.51
0.63
0.82
AS
2
1.40
1.74
2.09
3.96
4,92
5.97
0.47
0.59
0.77
E2EVAS X
1.74
2.46
5.80
0,90
1.23
OnlineTTAA 6
1.75
2.12
2.46
5.81
7.03
8.15
0.91
1.06
1,23
PSVAS
1,86
2.25
2.61
5.94
7.10
8.19
1,02
1.26
MPS-VAS
1.97
2.35
2.76
5.99
7.16
8.24
1.07
1.16
1.37
Table 12: ANT comparisons when trained with large vehicle as target on DOTA in single-query setting.
Test with Ship as Target
Test with LV as Target
Test with Harbor as Target
Method
C 12
C 15
C = 18
C 12
C 15
C 18
C 12
C 15
C 18
Random
2.41
3,02
3.95
3.40
4,03
5,14
3.17
3.93
4.78
GC
2.82
3.44
4,27
3.87
4.59
5.55
3.48
4.25
4,98
GS
AL 13
2,81
3.42
4,26
3.85
4,54
5,51
3.47
4.25
4,97
2.57
E2EVAS x
3.57
4.42
5.15
6.30
7.65
8.90
4.28
5.21
6.09
OnlineTT1A
3.57
4.43
5.15
6.31
7.67
8.90
4,30
5.22
6.10
PSVAS
3.60
4.51
5.23
6.50
7.86
9.22
4.61
5.72
6.87
MPS-VAS
3.79
4.75
5.58
6.51
7,88
9.24
4.90
6.23
7.38
with Helicopter Target
with Plane Target
Test Roundabout Target
Method
C 15
C 18
C 12
C= 18
12
C
18
Random
0.66
0.73
0.82
2.91
3.94
4.74
2.66
3,59
4.37
GC
GS
0.75
0.87
0.97
3.47
4.56
5.25
2.99
3,96
4.73
13
3.79
AS 9
0.68
0.78
0.86
3.16
4.21
4.97
2,82
3,74
4.51
F2EVAS 6
0.78
0.96
1.18
4,02
5.07
5.90
4,00
5.05
5,88
OnlineTTA
0.78
4,02
5.07
5.06
5,88
PSVAS
0.95
1.21
1.49
4.33
5.32
6.44
4.33
5.36
6.41
1.37
5.58
6.73
B.2 Multi Query Setting
In Table [3] we present the results of MPS- VAS-MQ and compare its performance with MPS- VAS-TOPE with varying search budget C E {12, 15, 18) and the number of grid cell N=49, Here, we train the policy with small car as the target and evaluate the performance of the policy with the following target classes Small Car (SC), Helicopter, Suil Boal (SB), Construction Cite (CC), Building, and Helipad. table 14, we present similar results with the number of grid cell N 64. In this setting, train the policy with Large Vehicle as the target and evaluate the policy with the
16

---

following target classes: Ship, Large Vehicle (LV), Harbor, Helicopter, Plane, and Roundabout (RB). We consider K 3 in all these experiments. We observe consistent improvement in performance of MPS- VAS-MQ over MPS-VAS-TOPK across different target setting, ranging from approximately 0.1 to 15%. The experimental results indicate that there are additional benefits learning to capture the interdependence in greedy search decisions.
Table 13: ANT comparisons when trained with small car target on x View in multi-query setting.
with Helicopter Tagget
Test with SB as Target
with Building as Target
Method
C = 12
C= 15
C= 18
C=12
C= 15
C= 18
C=
C= 15
C 18
APS-VVS-TOOR
0.71
0.85
1.04
1.10
1.23
1.47
7.07
8.60
9.98
0.75
0.88
1.08
1.41
1.53
10.21
Test with CC as Target
Test with SC as Target
Test with Helipal as Target
Method
C 12
C= 15
C= 18
C= 12
C= 15
C= 18
C= 12
C= 15
C 18
MPS-VAS-TOON -OO-
1.89
2.09
2.50
6.92
7.98
0.82
0.93
1.10
MPS-VAS-MQ
1.95
2.27
2.68
5.97
7.09
8.16
1,03
1.09
1.23
Table 14: ANT comparisons when trained with large vehicle as target on DOTA in multi setting.
Test with Ship as Target
Test with L.V as Target
Test with Harbor as Target
Method
C - 12
C-
15
C 18
C 12
C- 15
C18
C 12
C 15
C= 18
MPS VAS TOPK MPS-VAS-MQ
3.72
4.66
5.49
6.09
7.29
8.54
4.76
6.14
7.31
3.74
4.69
5.54
6.36
7.64
8.79
4.78
6.20
7.32
Test with Helicopter Target
Test with Plane Target
Test with RB Target
Method
C
C 18
C= 12
C
C
C=
MPS-VAS -----V-SOPP
0.88
1.05
1.24
3,95
5.48
6.69
4.32
5.45
6.45
MPS-VAS-MQ
0.90
1.06
1.30
4.02
5.49
6.73
4.39
5.47
6.49
C Results with Different Number of grid cells
Here, we present the results of PSVAS and MPS-VAY and compare the performance with the most competitive E2EVAS approach for differrnt choices of N.
C.1 Results with Number of Grid cell N = 99
In this setting, we train the policy with small car as the target and evaluate the performance of the policy with the following target classes Small Car (SC), Helicopter, Sail Boat (SB), Construction Cite (CC), Building, and Helipad. In Table 15 we present the results with Manhattan distance based query cost in single query setting. The similar results with multi query setting are presented in Table 16 In Table [7 and [8] we present the results with uniform query cost single and multi query setting respectively. We notice very similar trend in performance as observed in the settings with other choices of N. Specifically, We observe PSVAS significantly outperforms E2EVAS across different target settings, and VPS-VAS further improves the search performance universally. These results highlights the effectiveness of our proposed PSVAS and MPS- -AS framework for visual active search in practical scenarios when search tasks differ from those that are used for policy training.
C.2 Results with Number of Grid cell N = 36
In this setting, we train the policy with large vehicle as the target and evaluate the performance with the following target classes: Ship, large vehicle (LV), Harbor, Helicopter, Plane, and Roundabout. In Table 19 we present the results with Manhattan distance based query cost in single query setting. The results with multi query setting are presented in Table In Table and and 22 we present the the results with uniform query cost in single and multi query setting respectively. We observe a consistent performance trend across various target settings. Specifically, PSVAS consistently outperforms E2EVAS in different target settings, and the introduction of MPS-VAS further enhances the search performance across the board. These results emphasize the effectiveness of our proposed PSVAS and
17

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Table 15: ANT comparisons when trained with small car as target on View in single-query setting.
Test with Helicapter as Target
Text with SB as Target
Test with Building as Target
Method
C 2G
C 50
C 75
C= 25
C 50
C 75
C 25
C 50
C 75
RS
0.01
0.09
0.14
0.23
0.34
0.61
1.41
2.51
3.84
E2EVAS
x
0.17
0.30
0.39
0.65
1.03
1.34
3.32
5.37
7.05
OnlineTIA
O
0.17
0.31
0.40
0.66
1.34
3.32
7.07
PSVAS
0.39
0.48
0.65
0.71
1.07
1.35
4.31
6.97
9,12
MPS-VAS
0.45
0.69
0.75
1.37
4.42
9.35
Teat with CC as Target
Test with SC Target
Test with Helipad as Target
Method
C=
C 75
C=
C=
C
C=
C 50
C
0.32
1.10
2.15
2.96
0.12
0.29
EZEVAS 6
0.61
1.03
1.41
2.72
4.42
5.78
0.39
0.44
0.56
6
0.63
1.04
1.41
2.72
4.43
5.79
0.39
0.45
0.56
PSVAS
0,98
2.19
3.12
6.40
0,46
0.74
MPS-VAS
1.01
1.77
2.28
3.34
5.31
6.74
0.51
0.66
0.86
Table 16: ANT comparisons when trained with small car target on View in multi-query setting.
Test with Helicopter Target
Test with SB as Target
with Building as Target
Method
C = 25
C 50
C= 75
C= 25
C= 50
C= 75
C= 25
C 50
C 75
MPS- POS-VVA-TOPX
0.40
0,51
0.62
0.69
0.98
1.30
4.29
6.84
8.66
MPS-VAS-MQ
0.42
0.53
0.66
0.71
1,03
1.32
4.33
8.78
Test with CC as Target
Test with SC as Target
Test with Helipad Target
Method
C
=
C= 75
C=
C=
C
C 25
C= 50
C=
-PS-VVS-TOPX
0.96
1.53
2.12
3.19
5.09
6,47
0.77
MPS- MA
0,98
1.65
2.17
3.25
5.12
6.55
0.47
0,61
0,82
Table 17: ANT comparisons when trained with small car as target on View in single-query setting.
Test with Helicopter as Target
Test with SB as Target
Test with Building as Target
Method
C 12
C 15
C= 18
C 12
C= 15
C= 18
C 12
C 15
C 18
RS
0.22
0.31
0.38
0.48
0.55
0.63
3.43
4.25
4.97
E2EVAS
x
0,31
0.39
0.43
0.80
1.05
1.30
5,23
6.37
7.41
O
0.44
0.80
5.24
6.38
7.43
PSVAS
0.43
0.48
0,51
0.83
1.09
1,33
5.34
6.41
7.52
MPS- VAS
0.47
0.50
0.54
0.84
1.11
1.39
5.44
6.69
7.75
Teat with CC a5 Target
Test with SC Target
Test with Heliped as Target
Method
C=
C 18
C=
C
C
C
C 15
C
0.78
1.02
1.17
3.12
4.45
0.25
0.41
EZEVAS 6
0,98
1.29
1.47
4.61
5.64
6.55
0.44
0.46
0,56
O
0.99
1.32
1.50
4.62
5.64
6.56
0.45
0.47
0.56
PSVAS
1,28
4.74
6.75
0.53
0.78
MPS-VAS
1.39
1.69
2.05
4.81
5.93
6.96
0.61
0.66
0.83
Table 18: ANT comparisons when trained with small car as target on View in multi-query setting.
Test with Helicopter as Target
Test with SB as Target
Test with Building as Target
Method
C = 12
C= 15
C= 18
C= 12
C= 15
C= 18
C= 12
C= 15
C 18
SPS-VAS-TOR
0.41
0.43
0.48
0.78
1.01
1.26
4.91
6:07
7.02
UPS-VAS-MQ
0.42
0.46
0.51
0.81
1.05
1.32
5.02
6.21
7.18
Test with CC as Target
Test with SC as Target
Test with Helipad Target
Method
C
C=
C= 18
C
C
C
C= 12
C
C
VAS-VOS MPS-VAK VAS-MQ
1.22
1.41
1.82
4.29
5.63
6.59
0.54
0.78
1.26
1,53
1.98
4.38
5.74
6.68
0.57
0.61
0.79
HAS framework for visual active search in real-world scenarios where the search tasks differ from the ones used during policy training.
18

---

Table 19: ANT comparisons when trained with LV target on DOTA in single-query setting.
Test with Ship as Target
Text with I. as Target
Test with Harhor as Target
Method
C 2G
C m 50
C 76
C= 25
C 50
C 75
C 25
C 50
C 75
RS
1.45
3.17
4.30
1.79
3.50
5.10
2.35
4.34
6.76
E2EVAS
x
2.69
4,50
5.88
4.63
6.79
8.07
4,22
6.92
9.06
OnlineTIA O
2.70
4.52
4.63
8.07
4.22
9.08
PSVAS
3.19
4,83
6,34
4.69
6.94
8.12
4.95
7.56
9,51
MPS-VAS
3,42
5.19
6.73
4,80
8.23
5.02
8.04
9,91
Test with Helioopter as Target
Test with Plane larget
Test with RB as Target
Method
C
C=
C 75
C
C
C
C=
C 50
C
0.60
1.96
2.33
4.34
6.62
0.64
1.06
1.80
EZEVAS 6
1.00
2,07
2,66
4.57
7.23
9.14
1.56
2.28
2.72
6
1.00
2.07
2.68
4.57
7.25
9.16
1.56
2.28
2.73
PSVAS
1,53
2.33
5,09
9.41
1.87
2.76
MPS-VAS
1.80
2.60
3,03
5.17
7,83
10.02
1.96
2.76
3.19
Table 20:
ANT comparisons when trained with LV as target on DOTA in multi-query setting.
Test with Ship Target
Test with LV Target
Tost with Harbor Target
Method
C =
C= 75
C= 25
C=
C= 25
C= 50
C=
HPS-VVS-TOO
3.33
5.14
6.70
4.64
6,83
7.79
4.96
7.91
9:75
MPS-VAS-MQ
3,38
5.17
6.71
4.65
6.92
8.00
4.99
7.98
9,83
Test with Helicopeer as Target
Tsst with Planc as Target
Test with RB as Target
Method
C
25
C- 50
C 75
C 25
C 50
C 75
C 25
C - 50
C 75
MPS VAS TOPK
1.34
2.42
2,88
5.08
7.63
9.66
1.76
2.68
3.02
MPS-VAS-MQ
1.37
2.43
2.91
5.15
7.75
9.95
1.82
2.72
3.11
Table 21:
ANT comparisons when trained with LV as target on DOTA in single-query setting.
Test with Ship Target
Test with LV Target
Tost with Harbor Target
Method
C=
C=
C=
C
15
2.92
3.34
3,99
3.44
4,08
5.19
4.17
5.04
5.92
EZEVAS 6
3.34
4.15
4.77
5.14
6.05
7.00
5.38
6.51
7.54
OnlineTTA i
3.36
4.15
4.79
5.14
6.06
7.01
5.40
6.52
7.55
PSVAS
3,48
4.37
5.15
5.23
7.12
5.57
7.78
MPS-VAS
3,85
4.69
5,38
5.25
6.11
7.14
5.71
6.95
8.15
Helicopter as Target
as Target
as Target
Method
C 12
C 15
18
C 12
C - 15
C 18
C 12
C 15
C 18
RS
1.03
1.52
1.77
4.05
5.11
6.12
1.25
1.54
1.91
E2EVAS
1.50
5.47
7.65
2.47
OnlineTTA @
1,50
1.88
2.16
5.47
6.61
7.68
todo
todo
todo
PSVAS
1.77
2.23
2.50
5.54
6.65
7.66
2.03
2.32
2.65
MPS-VAS
2.10
2.57
2.77
5.73
6.87
7.90
2.12
2.66
2.99
Table 22: ANT comparisons when trained with LV as target on DOTA in multi-query setting.
Test with Ship as Target
Test with LV as Targei
Test with Harbur us Targel
Method
C - 12
C 15
C 18
C 12
C 15
C 18
C 12
C 15
C 18
MPS-VAS- VAS-TOPK
3.84
4.64
5,28
5.14
6.01
6.51
5.65
6.84
7.93
MPS-VAS-MQ
3.81
4.64
5.35
5,22
6.05
6.68
5.66
6.89
8.04
Test with Helicopter Target
Test with Plane Turget
Text with RB Target
Method
C
C=
C 18
C
C
C
C
MPS-VAG VVS-TOPK
1.39
1.91
2.27
6.79
7.71
2.01
2.68
1.43
1.96
2.33
5.65
6,83
7,80
2,08
2.49
2.81
19

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D Effect of Inference Time Adaptation of Task Specific Prediction Module on Search Performance
D.1 Effect on PSVAS Framework
First, analyze the impact of inference time adaptation of task specific prediction module PSVAS framework across different target settings. To this end, we first train a policy using our proposed PSVAS approach and then during inference freeze the task specific prediction module along with task agnostic search module unlike PSVAS approach. We call the resulting policy as PSVAS- -F. In Table 23 compare the search performance of PSVAS and PSVAS-F with number of grid cell N 36 across different target settings. In Table 24, we present similar results with number of grid cell N 49. We observe a significant improvement in performance of PS compared to PSVAS SAS F across different target settings, justifying the importance of inference time adaptation of task specific prediction module after every query.
Table 23:
ANT comparisons when trained with as target on DOTA in single-query setting.
Test with Ship as Target
Test with LV as Target
Test with Harbor as Target
Method
C 25
C
50
C 75
C- 25
C 50
C 75
C 25
C 50
C 75
PSVAS-F
2.77
4.55
5.99
4.61
6.77
8.09
4.26
6.87
9.05
PSVAS
3.19
4,83
6.34
4.69
6,94
8.12
4.95
7.56
9.51
Test Helicopter as Target
as Target
as Target
Method
C= 25
C 50
75
C 25
C= 50
C= 75
C 25
C 50
C 75
PSVAS-F
1.02
2.03
2.64
4.62
7,26
9.16
1.57
2.29
2.72
1,53
5.09
2.76
Table 24:
ANT comparisons when trained with small car as target on View in single-query setting.
Test with Helisopter as Target
Test with SB as Target
Test with Building as Target
Method
C 25
C 50
C 75
C 25
C 50
C 75
C 25
C - 50
C 75
PSVAS-F
0,55
0.86
1.24
0.66
1.12
1.34
5,88
9.45
12.23
PSVAS
0.87
L,88
1,28
0.93
1,23
1.66
6.81
10.53
13.44
as Target
with Target
with Helipad as Target
Method
C 25
C
50
75
C
25
c 50
C= 75
C
C 50
C
PSVAS-F
1.45
2.30
3.01
4.84
7.56
9.65
0.82
1.20
1.46
1.47
In Figure 4 the distinct exploration strategy behaviors of PSVAS and PSVAS-F are depicted when both policies are trained with large vehicle as the target and tested with ship as the target. Out of total of 15 queries. PSVAS-F achieves 6 successful scarches, while PSVAS achieves 8 successful searches. Figure illustrates the contrasting exploration strategy behaviors between PSVAS and
stop
step 3
step 5
step
step 9
step 11
step 13
step 15
Figure 4: Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using PSVAS-F (top row), PSVAS (bottom row).
PSVAS-F in the case when both the policies trained with large vehicle the target and test
20

---

with plane as the target. We observe PSVAS-F yields 9 successful searches, while PSVAS yields 12 successful search out of 15 total query.
step
step 3
step 5
step
step 9
step 11
step 13
step 15
Figure 5: Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using PSVAS-F (top row), PSVAS (bottom row).
Figure illlsttrates the contrasting exploration strategy behaviors between PSVAS and PSVAS-F in the case when both the policies are trained with large vehicle as the target and test with roundabout the target. We observe PSVAS-F yields successful searches, while PSVAS yields successful search out of 15 total query.
step
step
step
step
step
Figure 6: Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using PSVAS-F (top row), PSVAS (bottom row).
D.2 Effect on MPS- VAS Framework
Next, we examine the influence of inference time adaptation of the task-specific prediction module on the MPS- VAS framework across various target settings. For this purpose, we train a policy using our proposed MPS-VAS approach. But during inference, we freeze both the task-specific prediction module and the task-agnostic search module, which differs from the standard MPS-VA approach. We refer the resulting policy as MPS- VAS-F. Table presents comparison of the search performance between MPS-VAS and MPS-VAS- F, considering a grid cell count of = 36, across various target settings. Similarly, in Table 25 we provide corresponding results with grid cell count of N 49. Across various target settings, we observe a notable enhancement in the performance of MPS-VA compared to VAS-F. This finding underscores the significance of adapting the task-specific prediction module during inference after each query, validating its importance on adaptive visual active search. Following Figures demonstrate the divergent exploration strategy behaviors exhibited by MPS- VAS and -AS-F.
Figure illustrates the contrasting exploration strategy behaviors of MPS-VA and MPS- VAS-F
when both policies are trained with a large vehicle as the target and tested with plane as the target. Among total of 15 queries, MPS- POS-VAS-F achieves 2 successful searches, while MPS- VAS achieves
21

---

Table 25:
ANT comparisons when trained with small car as target on View in single-query setting.
Test with Helicapter as Target
Test with SB as Target
Test with Building as Target
Method
C 2G
C 50
C 75
C= 25
C 50
C- 75
C 25
C 50
C 75
MPS. VAS-F
0.54
0.89
1.22
0.64
1.14
1.37
5.97
9.31
12.04
MPS-VAS
0.92
1.13
1,38
1.07
1.67
2.10
6,83
10.59
13.64
Test with CC Target
Test with SC Target
Test with Helipad Target
Method
C
C
C
50
C
MPS-VAS-F
1.37
2.33
3.05
4.82
7.46
9.56
0.82
1.24
1.41
MPS-VAS
1.74
2.64
3.47
5.55
8.40
10.69
0.96
1.30
1.63
Table 26:
ANT comparisons when trained with LV as target on DOTA in single-quer setting.
Test with Ship Target
Text with I.V Target
Test with Harbor Target
Method
C
C
75
C
C= 50
75
C
C= 50
MPS. VAS-F
2.69
4.50
5.88
4.63
6.79
8.07
4,22
6.92
9.06
MPS-VAS
3.42
5.19
6.73
4.80
7.08
8.23
5.02
8.04
9.91
Test with Helicopter as Target
Test with Plane as Target
Test with RB as Target
Method
C 25
C - 50
C 75
C 25
C 50
C 75
C - 25
C. - 50
C. 75
MPS- -AS-F
1.00
2.07
2,66
4.57
7.23 7,83
9.14 10.02
1.56
2.28
2.72
MPS-VAS
1.00
2.60
3.03
5.17
1,96
2,76
3.19
4 successful searches.
In Figure 8 the distinct exploration strategy behaviors of MPS-VAS and
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Figure 7: Query sequences, and corresponding heat maps (darker indicates higher probability). obtained using MPS- VAS-F (top row), MPS VAS (bottom row).
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Figure 8: Query sequences, and corresponding heat maps (darker indicates higher probability). obtained using MPS- (top row), MPS VAS (bottom row).
MPS-VAS-F are depicted when both policies are trained with a large vehicle as the target and tested with ship as the target. Out of total of 15 queries, MPS- VAS-F achieves 7 successful searches,
22

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Figure 9:
Query sequences, and corresponding heat maps (darker indicates higher probability), obtained using MPS-VAS-F (top row), MPS-VAS (bottom row).
while MPS-VAS achieves 9 successful searches. Figure showcases the contrasting exploration strategy behaviors MPS-VAS and MPS-VAS VAS-F when both policies trained with large vehicle as the target and tested with a roundabout as the target. Among a total of 15 queries, MPS- VAS-F achieves 6 successful searches, while MPS. VAS achieves 8 successful searches.
E
More Visualizations of Comparative Exploration Strategies of Different Approaches
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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 conducted using roundabout as the target.
Figure 10:
The showcased visualizations TO our PSVAS and MPS-VAS framework compared to the E2EVAS baseline, especially in scenarios where search tasks vary from those employed in policy training.
13 14h in all these examples demonstrate the superiority of
F Analyzing Search Performance Across Multiple Trials
Here, we compare the search performance of E2EVAS, PSVAS, and MPS-VAL across multiple trials. 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 C 25 with the following target classes: Small Car (SC), Helicopter, Sail Boat (SB), Construction Cite (CC), Building, and Helipad. we present similar results with Manhattan distance based query cost budget = 50. In also present similar results with Manhattan distance based query cost budget C 75.
In Figure 15
In figure 16
figure 17
In Figure T8
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 C 25 with the following target
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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 conducted using ship as the target.
Figure 11:
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UNIVER
UNVVES VHET
VAN
WAY
uerva
vena
sens
Wine
Vit
win
Whe
uerven
Weed
vervi
LOA
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, these policies are trained with large vehicle the target, while evaluation is conducted using ship as the target.
Step
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Figure 13: Query sequences, and corresponding heat maps (darker indicates higher probability). obtained using E2EVAS (top row). PSVAS (middle row), and MPS-VAL (bottom row). Note that during the training phase, all these policies are trained with large vehicle as the target, while evaluation conducted using plane 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 C 50. figure 20 we also present similar results with Manhattan distance based query cost budget C 75.

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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 conducted using plane the target.
Trained with Small Car as the target. 49, and Distance based Query Cast Budget (C) 25
Hellcopter on Target Sail Boat Tirget Building Tarpet
cC un Turget
SC Turget
Helppd as Target
68
333
3.
ANT
1.2
14
5.6
EJ000 PRAKS NSHAN
E2000 PUNDS NMV-W
12000 PUODS MEM-o
13000 mosSS Mipass
13000 mo0ta Wiaass
13EA00 HOW0 MPPANS
Figure 15: Comparative Search Performance of E2EVAS. PSVAS, MPS- VAS under Distance Based Query Cost (C 25).
Trained with Small the target. and Distance based
Cost Budget
50
23
ANT
3.8
0.0
Figure 16: Comparative Search Performance of E2EVAS, PSVAS, MPS-MPSS-VVV. under Distance Based Query Cost (C 50).
G Search Performance Comparisons Across Datasets
Our experimental outcomes indicate that it possible to apply our method directly across different datasets without requiring any further modifications or hyperparameter tuning. In the following (able[7] we demonstrate this by presenting results of training on one dataset for one target class while evaluating another dataset and for another target class. We use the number of equal sized grid cells 64 and varying search budgets C = (25, 50, 75).
25

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Trained with Small Car as the target. 49, and Distance based Query Cost Budget (C)
75
Helicopter as Target
12
Sall Thrget
Building Thrget
CC Thrget
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Hellpad as Target
140
3.
13.25
10.6
15.4
1/5
3.2
1440
1
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12.00
Figure 17: Comparative Search Performance of E2EVAS, PSVAS, MPS-VAS VAS under Distance Based Query Cost (C 75).
Trained
based Hellepptr Strget
Sirget
Was Target
Merter
Plane Target
Target
ANT
Figure 18: Comparative Search Performance of E2EVAS, PSVAS, MPS-VAS under Distance Based Query Cost (C 25).
Large targer, Distanccl based Quary Cost Budger
ANT
COPAA
CSPAA
PPLLE
ERRAM
Figure 19: Comparative Search Performance of E2EVAS, PSVAS, MPS-VPPS-AAAA under Distance Based Query Cost (C 50).
Tolned with Large Vehi de the tanget, 64, and Distance based Duery Cost Bucget (C) larget
tarper
serbor larget
Hediesper largen
Target
ANT
Figure 20: Comparative Search Performance of E2EVAS, PSVAS, MPS-VAS under Distance Based Query Cost (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 View.
Test with Small Car as Target
Test with Building as Target
Test with Sail Boat as Tirgel
Method
C= 25
C 50
C 75
C 25
C 50
C 75
C 25
C 50
C = 75
EZEVAS 6
4.22
6.73
8.07
5.12
8.24
10.50
0.48
0.56
0.92
Online TTA
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
26


Reviews:
[{"review_id": "thpArGhy7N", "reviewer": "Reviewer_yVuL", "summary": "The paper addresses visual active search problem for geospatial exploration. For an effective adaptation of search policies to out-of-distribution tasks, the paper proposes partially supervised reinforcement learning framework. A meta-learning approach is also proposed that focuses on jointly learning a policy and initialization parameters for the supervised prediction module. Experiments are presented on two datasets.", "questions": "Please see weaknesses.", "limitations": "NA", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "+ Proposed partially supervised reinforcement learning framework allows effective adaptation of search policies to out-of-distribution tasks.\n+ Proposed meta-learning framework focuses on effective learning of supervised prediction module\n+ Experiments show promising results", "weaknesses": "-- Not sure of the reproducibility of this work. Also, no code is provided in the supplementary.\n-- If there is fully labeled training task, why imitation learning (IL) is not adopted? IL and RL loss can be used together for the task. \n-- It will be good to provide the formulation of L_BCE and L_RL loss in the paper? ", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your comments. We are encouraged to see that you appreciated our contribution. We respond to all the comments below. \n\n> **Q1**:  Not sure of the reproducibility of this work. Also, no code is provided in the supplementary.\n\n**A1**: With anonymity being a primary concern, we have chosen not to release the code at this point. Nevertheless, we are committed to open-sourcing the GitHub link upon acceptance, inclusive of all model weights, to facilitate the reproduction of all our experimental results.\n\n> **Q2**: why imitation learning (IL) is not adopted? IL and RL loss can be used together for the task.\n\n**A2**: We wish to clarify that for imitation learning to be applied, we would need examples of search demonstrations by people, which we do not have.  Rather, our labels provide information about object locations across grid cells.  The reason this is not useful for an imitation learning paradigm is that the search task is fundmentally about handling unknown object locations, leveraging previous observations about these, and trading off exploration and exploitation.  Consequently, in our setting the only way we can make use of imitation learning is to learn to greedily choose a location most likely to contain an object.  And, in fact, such a greedy approach is one of our baselines (we refer to it as greedy classification (GC)), and performs rather poorly (e.g., in Table 1, it is one of the weakest baselines).\n\n> **Q3**: It will be good to provide the formulation of L_BCE and L_RL loss in the paper?\n\n**A3**: Thank you for the suggestion. We thought the formulation of binary cross-entropy loss ($L_{BCE}$) and REINFORCE loss ($L_{RL}$) is well-known and not necessary to include. But, following your suggestion we will include this in the revision."}, {"review_id": "dYiIHWVHxy", "reviewer": "Reviewer_6bym", "summary": "The paper proposes a partially supervised reinforcement learning framework (PSVAS) for visual active search tasks (VAS). Specifically, it decompose previous end-to-end deep RL method for VAS into a task-specific prediction module and a task-agnostic search module. In such way, the prediction module can be supervised with observed ground truth during training or inference time. Further, a meta-learning setting is added to adapt to different tasks quickly. Results on xView and DOTA datasets show the proposed methods and variants outperform previous baselines. ", "questions": "\nCan authors address the weaknesses part. \n", "limitations": "Whether it is possible to apply such method across different datasets is not discusses. ", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The idea to utilize the target-location labels as additional supervision signal besides RL loss is easy to follow and makes sense. The method shows much better adaptation capability when applying to search target that is not trained on, demonstrating the advantages of decomposing the problem into prediction and searching separately. The meta-learning variants and multi-query variants further enhanced the performance. Overall the method is techniquely sound and empirically proved. ", "weaknesses": "\nHow different balancing factor lambda between RL and BCE loss would affect the performance is worth to investigate in ablation study section. \n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the thoughtful comments and suggestions. We are inspired to see that you found our overall method is techniqually sound and empirically proved. We respond to all the comments below.\n\n> **Q1**: How different balancing factor lambda between RL and BCE loss would affect the performance is worth to investigate in ablation study section.\n\n**A1**: We performed experiments with different choices of $\\lambda$ and found $\\lambda$ = 0.1 to be the best choice across all different experimental setup. For comparison, here we report the results in the case when we train the policy with different values of $\\lambda$ using small car as a target class and test the policy with small car, building, and sail boat as target on xView. We evaluate the policy with varying search budgets C ∈ {25, 50, 75} and the number of equal sized grid cells N = 49. \n\nIn the following table, we provide the result for the **PSVAS** framework with **small car** as target.\n\n| $\\lambda$  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|0.001| 4.96 | 7.75 | 9.74 |\n| 0.01  | 5.02  | 7.87  | 9.96  |\n| 0.1  | **5.51**  | **8.33**  | **10.52**  |\n| 1.0  | 5.10  | 7.98  | 10.04  |\n\nIn the following table, we provide the result for **MPS-VAS** framework with **small car** as target.\n\n| $\\lambda$  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|0.001| 4.99 | 7.82 | 9.90 |\n| 0.01  | 5.06  | 7.93  | 10.03  |\n| 0.1  | **5.55**  | **8.40**  | **10.69**  |\n| 1.0  | 5.12  | 8.01  | 10.12  |\n\nIn the following table, we provide the result for **PSVAS** framework with **building** as target.\n\n| $\\lambda$  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|0.001| 6.08 | 9.64 | 12.35 |\n| 0.01  | 6.37  | 9.95  | 12.77  |\n| 0.1  | **6.81**  | **10.53**  | **13.44**  |\n| 1.0  | 6.39  | 10.16  | 12.81  |\n\nIn the following table, we provide the result for **MPS-VAS** framework with **building** as target.\n\n| $\\lambda$  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|0.001| 6.15 | 9.74 | 12.44 |\n| 0.01  | 6.41  | 10.09  | 12.89  |\n| 0.1  | **6.83**  | **10.59**  | **13.64**  |\n| 1.0  | 6.46  | 10.21  | 12.96  |\n\nIn the following table, we provide the result for **PSVAS** framework with **sail boat** as target.\n\n| $\\lambda$  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|0.001| 0.74 | 1.12 | 1.43 |\n| 0.01  | 0.88  | 1.19  | 1.54  |\n| 0.1  | **0.93**  | **1.23**  | **1.66**  |\n| 1.0  | 0.89  | 1.20  | 1.59  |\n\nIn the following table, we provide the result for **MPS-VAS** framework with **sail boat** as target.\n\n| $\\lambda$  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|0.001| 0.83 | 1.22 | 1.53 |\n| 0.01  | 0.98  | 1.46  | 1.87  |\n| 0.1  | **1.07**  | **1.67**  | **2.10**  |\n| 1.0  | 1.01  | 1.52  | 1.90  |\n\nOur empirical findings across all the experimental settings are quite consistent, and justify the choice of $\\lambda=0.1$.\n\n> **Q2**: Whether it is possible to apply such method across different datasets is not discusses.\n\n**A2**: We can apply our method directly across different datasets without requiring any further modifications or hyperparameter tuning. In the following tables, 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 C = {25,50,75}.\n\nIn the following table, we report the results when we **train** the policy with **large vehicle on DOTA** as target and **evaluate** with **small car on xView** as target.\n\n| Method  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|E2EVAS | 4.22 | 6.73 | 8.07 |\n|OnlineTTA | 4.23 | 6.75 | 8.10 |\n| PSVAS  | 4.95  | 7.74  | 9.45 |\n| MPS-VAS  | **5.07**  | **7.92**  | **9.73**  |\n\nIn the next table, we report the results when we **train** the policy with **large vehicle on DOTA** as target and **evaluate** with **building on xView** as target.\n\n| Method  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|E2EVAS | 5.12 | 8.24 | 10.50 |\n|OnlineTTA | 5.14 | 8.27 | 10.53 |\n| PSVAS  | 6.10  | 9.45  | 12.31 |\n| MPS-VAS  | **6.18**  | **9.68**  | **12.83**  |\n\nIn the next table, we report the result when we **train** the policy with **large vehicle on DOTA** as target and **evaluate** with **sail boat on xView** as target.\n\n| Method  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|E2EVAS | 0.48 | 0.56 | 0.92 |\n|OnlineTTA | 0.49 | 0.57 | 0.95 |\n| PSVAS  | 0.89  | 1.05  | 1.54 |\n| MPS-VAS  | **1.02**  | **1.39**  | **1.91**  |\n\nOur experimental findings suggest that our proposed PSVAS and MPS-VAS framework significantly improves ANT compared to the most competitive baselines even in the case when the training and evaluation datasets and target objects differ."}, {"review_id": "xK2c4sgvsn", "reviewer": "Reviewer_2ixD", "summary": "This paper proposes a Partially Supervised Reinforcement Learning to search for the objects of interest in geospatial images. The paper uses a meta-learning approach to learn initialization parameters of the prediction module jointly with a search policy.  A variant of this approach, MPS-VAS, learns  to choose a subset of queries to make in each search iteration. The proposed method outperforms the state-of-the-art and other baselines on xView and DOTA benchmarks.", "questions": "1. Can we train on a dataset (xView) and generalize to the classes on another dataset (DOTA)?\n\n2. Can we use the same model for image classification task on the fMoW dataset?\n\n", "limitations": "This paper proposes a Partially Supervised Reinforcement Learning to search for the objects of interest in geospatial images. The paper uses a meta-learning approach to learn initialization parameters of the prediction module jointly with a search policy.  A variant of this approach, MPS-VAS, learns  to choose a subset of queries to make in each search iteration. The proposed method outperforms the state-of-the-art and other baselines on xView and DOTA benchmarks. The results show drastically better results than the state-of-the-art model OnlineTTA. However, the method is not tested on traditional vision benchmarks.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper proposes a Partially Supervised Reinforcement Learning to search for the objects of interest in geospatial images. The paper uses a meta-learning approach to learn initialization parameters of the prediction module jointly with a search policy.  A variant of this approach, MPS-VAS, learns  to choose a subset of queries to make in each search iteration. The proposed method outperforms the state-of-the-art and other baselines on xView and DOTA benchmarks. The results show drastically better results than the state-of-the-art model OnlineTTA.", "weaknesses": "While the proposed method is promising and novel, it does limit itself to only two benchmarks on satellite images. It would increase the contribution significantly if it was tested on some traditional vision benchmarks. At least, a discussion on it would be useful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the insightful comments and suggestions. We are encouraged to see that you find our proposed method is promising and novel. We respond to all the comments below.\n\n> **Q1**: Can we train on a dataset (xView) and generalize to the classes on another dataset (DOTA)?\n\n**A1**: Absolutely!  Please see the data we provide in response to a similar question by Reviewer 6bym. In a nutshell, we can apply the proposed approach with no modification to train on one dataset with a target object class and then evaluate on a different dataset, and a different target object class. In such cases, our approach also demonstrates superior performance---often by a large margin---compared to the most competitive baselines.\n\n> **Q2**: Can we use the same model for image classification task on the fMoW dataset?\n\n**A2**: Active search is qualitatively distinct from image classification task. The goal of the image classification task is solely to learn to predict well. In active search, in contrast, we aim to learn a search policy that balances exploration (improving our ability to predict where target objects are) and exploitation (actually finding such objects) within a limited budget. The key reason for this balance is that queries are informative about the location of target objects in two ways: 1) geospatial correlations in object locations, and 2) improvement of the quality of the learned predictive model.  In contrast, traditional classification is a sequence of one-shot **prediction** tasks, rather than **search** tasks.  Consequently, traditional vision benchmarks do not provide an appropriate evaluation framework for our problem.\n\n> **Q3**: It would increase the contribution significantly if it was tested on some traditional vision benchmarks. At least, a discussion on it would be useful.\n\n**A3**: As we mention in our response to the question above, visual active search (VAS) is a qualitatively different problem than traditional vision tasks, and therefore typical vision benchmarks are not appropriate means for evaluating VAS approaches, as we are interested primarily in the quality of visual search (trading off exploration and exploitation) rather than the quality of visual prediction.  We agree that this warrants further discussion and clarification, which we will add in the revision."}, {"review_id": "yYWIBbWMfG", "reviewer": "Reviewer_mjFZ", "summary": "This paper introduces a novel Reinforcement Learning (RL) framework for visual active search. In contrast to previous methods, the authors suggest integrating visual supervised signals to enhance performance. Additionally, a meta-learning based approach is proposed to allow the model to adapt to varying test cases. The authors also expand their methodology to support multiple queries. Experimental results demonstrate that the proposed method significantly outperforms both the simple baseline approaches and previous RL-based methods.", "questions": "Comments: Although I don't directly work on this area, I perceive the research problem to be valuable, and the proposed method appears to be sound. However, given my limited expertise, I find it challenging to assess the significance of this paper's technical contributions. Therefore, I've initially rated it as \"Borderline Accept\". My final rating will depend on feedback from other reviewers, as well as the author's rebuttal.", "limitations": "N/A", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The proposed method is both intuitive and technically sound.\n- The motivation for test-time adaptation and multi-query adaptation is logically sound and well-justified.\n- A variety of datasets and settings were utilized for the experiments, which substantiate the superior performance of the proposed method compared to other approaches.\n- The manuscript is well-written and presented, enhancing its clarity and comprehension.", "weaknesses": "- Regarding PSVAS, while the concept of simply adding supervised loss is reasonable, it seems more like a straightforward engineering extension to an existing method rather than a novel contribution.\n- The authors should conduct more ablation studies to provide readers with a deeper understanding of their method. For instance, an ablation study on hyper-parameters and different modules of the proposed method would be valuable.\n- In the proposed method, the supervised loss is merely a binary classification loss that signifies the presence of a target in the query image patch. I wonder if integrating a more fine-grained supervised loss could enhance the overall performance. For example, would adding a detection or segmentation loss be beneficial?\n- The paragraph from lines 128 to 140 appears to lack logical consistency. I recommend that the authors restructure this section for improved clarity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the comments and suggestions. We are encouraged to see your appreciation of the writing and presentation style, proposed methodology being intuitive and technically sound, and motivation of TTA and MQA being logically sound and well justified. We respond to all the comments below.\n\n\n> **Q1**: The authors should conduct more ablation studies to provide readers with a deeper understanding of their method. For instance, an ablation study on hyper-parameters and different modules of the proposed method would be valuable.\n\n**A1**: We thank the reviewer for the comment. Indeed, we analyse the importance of the task-specific prediction module in Sections D.1 and D.2 of Supplementary Material by freezing the prediction module parameters during inference time. Here, we additionally analyse the efficacy of the task-specific prediction module by setting **$\\lambda$ = 0** while training the policy. We call the resulting policy **USVAS** (Un-Supervised VAS). We observe a significant drop in performance across all settings, demonstrating the importance of the Supervised prediction module in order to learn an effective search policy. Specifically, in the following tables we present the results when the policy is trained with small car on xView as a target, while the performance of the policy is evaluated for the following target classes: Small Car (SC), Helicopter, SailBoat (SB), Construction Site (CS), Building, and Helipad. We evaluate the policy with varying search budgets C ∈ {25, 50, 75} and the number of equal sized grid cells N = 49. \n\nIn the following table, we report the test result with **small car** as a target.  \n\n| Method  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|**USVAS** | 4.77 | 7.46 | 9.61 |\n| PSVAS  | 5.51  | 8.33  | 10.52 |\n| MPS-VAS  | **5.55**  | **8.40**  | **10.69**  |\n\nIn the following table, we report the test result with **Helicopter** as a target.  \n\n| Method  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|**USVAS** | 0.53 | 0.84 | 1.19 |\n| PSVAS  | 0.87  | 1.08  | 1.28 |\n| MPS-VAS  | **0.92**  | **1.13**  | **1.38**  |\n\nIn the following table, we report the test result with **Sail Boat** as a target.  \n\n| Method  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|**USVAS** | 0.64 | 1.08 | 1.27 |\n| PSVAS  | 0.93  | 1.23  | 1.66 |\n| MPS-VAS  | **1.07**  | **1.67**  | **2.10**  |\n\n\nIn the following table, we report the test result with **Construction Site** as a target.  \n\n| Method  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|**USVAS** | 1.44 | 2.27 | 2.99 |\n| PSVAS  | 1.62  | 2.49  | 3.14 |\n| MPS-VAS  | **1.74**  | **2.64**  | **3.47**  |\n\nIn the following table, we report the test result with **Building** as a target.  \n| Method  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|**USVAS** | 5.86 | 9.37 | 12.05 |\n| PSVAS  | 6.81  | 10.53  | 13.44 |\n| MPS-VAS  | **6.83**  | **10.59**  | **13.64**  |\n\nIn the following table, we report the test result with **Helipad** as a target.  \n\n| Method  | C = 25  | C = 50  | C = 75 |\n|---|:---:|:---:|:---:|\n|**USVAS** | 0.80 | 1.16 | 1.42 |\n| PSVAS  | 0.91  | 1.22  | 1.47 |\n| MPS-VAS  | **0.96**  | **1.30**  | **1.63**  |\n\n> **Q2**: I wonder if integrating a more fine-grained supervised loss could enhance the overall performance. For example, would adding a detection or segmentation loss be beneficial?\n\n**A2**: This is an intriguing question. Our main objective here is to **find as many target grids** as possible within a pre-specified budget. Consequently, we concentrate on devising an efficient search policy capable of identifying grids containing one or more targets. To achieve an efficient search policy, it becomes crucial to have knowledge about the likely locations of target grids. To address this, we employ a task-specific prediction module trained using BCE loss. However, if we were to shift our focus on a slightly different problem, i.e., Visual Active Target Object Detection, which aims to **precisely identify as many target objects** (along with their exact locations and shapes) as possible within the search budget, using a more fine-grained loss such as detection or segmentation loss would become essential. While this problem of Visual Active Target Object Detection is beyond the scope of our current work, it is a terrific problem for follow up research!\n\n> **Q3**: The paragraph from lines 128 to 140 appears to lack logical consistency. I recommend that the authors restructure this section for improved clarity.\n\n**A3**: Thank you for the suggestion. We will restructure the referenced section in a revised draft."}]

Author rebuttal:
We thank all the reviewers for their constructive and insightful feedback. We are delighted to see that our work is found novel and technically solid by all the reviewer. We’ve addressed all reviewer concerns to the best of our effort by adding detailed clarifications with supporting experimental results, and will incorporate the feedback in the final version.

Meta-review: The work received generally positive reviews. Concerns were raised about generalization, ablations, and baselines during the review period. The authors provided several responses that attempt to clarify these questions and generally address most of the concerns. The authors are encouraged to incorporate the information from the rebuttal into the final version for completeness.
Decision: Accept (poster)