Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection
Authors: Yu Guo
Shengfeng He
Yuxu Lu
Haonan An
Yihang Tao
Huilin Zhu
Jingxian Liu
Yuguang Fang
Venue: NeurIPS 2025 spotlight ()
Keywords: Image Generation
Object Detection
Computer Vision
Primary area: deep_learning

Abstract: Maritime object detection is essential for navigation safety, surveillance, and autonomous operations, yet constrained by two key challenges: the scarcity of annotated maritime data and poor generalization across various maritime attributes (e.g., object category, viewpoint, location, and imaging environment). To address these challenges, we propose Neptune-X, a data-centric generative-selection framework that enhances training effectiveness by leveraging synthetic data generation with task-aware sample selection. From the generation perspective, we develop X-to-Maritime, a multi-modality-conditioned generative model that synthesizes diverse and realistic maritime scenes. A key component is the Bidirectional Object-Water Attention module, which captures boundary interactions between objects and their aquatic surroundings to improve visual fidelity. To further improve downstream tasking performance, we propose Attribute-correlated Active Sampling, which dynamically selects synthetic samples based on their task relevance. To support robust benchmarking, we construct the Maritime Generation Dataset, the first dataset tailored for generative maritime learning, encompassing a wide range of semantic conditions. Extensive experiments demonstrate that our approach sets a new benchmark in maritime scene synthesis, significantly improving detection accuracy, particularly in challenging and previously underrepresented settings. The code is available at https://github.com/gy65896/Neptune-X.

Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection
Yu Guo 1.3 Shengfeng He?.,, Yuxu Lu', Haonan An', Yihang Tao',
Huilin Zhu", Jingxian Liu", Yuguang Fang' Hong Kong JC STEM Lab of Smart City and Department of Computer Science,
City University of Hong Kong "Singapore Management University
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology
'The Hong Kong Polytechnic University WWuhan University of Science and Technology ctw/gy/689////ep2///c// Netpoune-ptttuee
https://githu: com/ gy65896/
Abstract
Maritime object detection is essential for navigation safery, surveillance, and autonomous operations, yet constrained by two key challenges: the scarcity of annotated maritime data and poor generalization across various maritime attributes (e.g., object category, viewpoint, location, and imaging environment). To address these challenges, we propose Neptune-X, a data-centric generative-selection frame- work that enhances training effectiveness by leveraging synthetic data generation with task-aware sample selection. From the generation perspective, we develop telitiimodalllyyoooeioi
generative model that synthesizes diverse and realistic maritime scenes. A key component is the Bidirectional Object- Water Attention module, which captures boundary interactions between objects and their aquatic surroundings to improve visual fidelity. To further improve down- stream tasking performance, we propose Attribute-correlate Active Sampling, which dynamically selects synthetic samples based their task relevance. To support robust benchmarking, we construct the Maritime Generation Dataset, the first dataset tailored for generative maritime learning, encompassing wide range semantic conditions. Extensive experiments demonstrate that our approach sets new benchmark in maritime scene synthesis, significantly improving detection accuracy, particularly in challenging and previously underrepresented settings.
X-to-Maritime,
Introduction
Object detection is fundamental technology for maritime environmental perception, enabling the identification of object categories and the localization of bounding boxes in images captured by imaging systems deployed on various facilities, such as surface vessels, coastal infrastructure, and aerial platforms. It plays a key role in variety of maritime applications, including autonomous assisted navigation for surface ships [43 intelligent video surveillance for coastal facilities [40]. and autonomous inspection using Unmanned Aerial Vehicles (UAVs) 44
Despite rapid advances in deep learning- based object detection capability of these models remains heavily dependent on the scale and diversity of annotated training data. Specifically, maritime-specific object detection datasets face two core limitations. First, the acquisition and annotation process is costly and labor-intensive. Different from land scenarios, data collection from heterogeneous platforms such as ships, UAVs, and stationary coastal cameras requires significant operational resources. In addition, the manual annotation of bounding boxes limits dataset scalability. Second, existing datasets exhibit large disparities in training difficulty across multiple
23 the generalization
"Corresponding author: shengfenghe @ smu.edu.sg
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

AAn image harter sa9d.
horber
SAn Smagy afa siere Shove deei.. osytine Mise vey- wale sorthle
'An imees riayy soind-ahip dllp Stni.righte The rster surfiet refeniioss lgrt.''
Mi tsage of sibce 2012 siot. foaee sige. surposil idin.
56
AP
Gategory
Hortin sbj.
36
MAP
Viewpoint
Noe Looi. surjeccappan aepcccppaas Aod corry-.
23.2
Firel
perso parcen
Ranking of ATDFs Location
Ranking of ATDFs
m.A
MAP Imaging Environment
41.4
18.5
Right
Ronking of ATDFs
Ranking
ATDFs
(6) X-to --t-Maariimee Generation Results
(b) Correlations between TDFss and Detection Accuracy on Test Set
Figure 1: We introduce Neptune-X, a generation-sele -slletiionnnsectionn framework for robust maritime object detection. (a) It enables the first multi-modality solli--moddalityccoddtnnned data generation, supporting diverse and controllable maritime scene synthesis. (b) Our active selection strategy leverages the Attribute- correlated Training Difficulty Factor (ATDF), which correlates with detection performance and guides the selection of high-value synthetic samples to optimize downstream detector accuracy.
attributes, including imaging conditions, viewpoints, water environments, and object categories. These imbalances are driven by uneven sample distributions and systematic biases in data collection, resulting in poor generalization to rare or complex maritime scenarios.
To mitigate data limitations, traditional data augmentation techniques [1 48
have been widely adopted. These methods apply geometric transformations, color perturbations, and sample mixing increase training diversity. However, they operate only existing samples and cannot generate fundamentally new instances with novel semantics. As a result, they are insufficient for addressing performance degradation caused by data scarcity and semantic imbalance.
Compared to GANs IO 49 20), the diffusion models [30] achieve superior image quality and training stability for more flexible rext-conditioned image synthesis. Layout-to-Iman techniques [21 46, [1 further enhance controllability by conditioning on layout information, such as bounding boxes, and have been explored synthesize detection-specifie training data [34] However, applying these methods directly to the maritime domain remains problematic. Maritime scenes demand an explicit semantic understanding of the interaction between objects and their aquatic surroundings, as objects and environmental context (e.g., sea states, reflections, lighting) are closely intertwined in both appearance and meaning. Without modeling this relationship, generative models often produce semantically inconsistent and physically implausible artifacts, such as ships floating unnaturally in mid-air or disconnected from the water surface. Furthermore, existing approaches overlook the fact that synthetic samples vary in training utility due to differences in category, viewpoint, and condition. Failing account for such disparities leads suboptimal data selection and limited gains.
In this paper, we introduce Neptune-Xi x a unified data ceneration-selectiee to address the dual challenges of data scarcity and limited diversity in maritime object detection. Our approach fuses multi-condition maritime scene generation with task-aware sample selection to enhance both quantity and quality of training data. In the generation phase, develop controllable generative framework that supports diverse input modalities and produces semantically rich maritime scenes. A central innovation is the Bidirectional Object-Water Attention (BiOW-Attn) mechanism, which explicitly models interactions between objects and their aquatic surroundings to improve the realism and coherence of object placement. This enables the generation of visually plausible maritime scenes with fine-graie ine-grained spatial semantics (Fig. TT).
paradigm designed
For data selection, Neptune-X incorporates an Attribute-dependent Active Sampling (AAS) strategy to prioritize training samples that are most beneficial for detection performance. This strategy is guided by Atrribute-related Training Difficulty Factors (ATDF), which estimate the learning difficulty associated with different semantic attributes, such as viewpoint, object category, and environmental condition. As illustrated in Fig. Tp. ATDF captures the relative training complexity across attributes and informs the weighting of synthetic samples during selection. By aligning sample value with
'The model name "Neptune-X X" integrates the marine symbolism of Neptune (Roman sea god) with multi- modality conditional guidance (X).

---

task difficulty, AAS enables more focused and efficient use of generated data, ultimately guiding the detector to learn from challenging and underrepresented cases.
To support training and evaluation under diverse maritime conditions, we construct a new benchmark, the Maritime Generation Dataset, which covers broad range of scenarios with variations in object category, viewpoint, environment, and location. Extensive experiments demonstrate the effectiveness of Neptune-X in both synthetic scene quality and downstream detection performance, particularly in challenging and underrepresented cases.
In summary, main contributions are threefold:
We present the X-to-Maritime generation framework for maritime scenes, featuring Bidirectional Object-Water Attention module that enhances realism by modeling object-water interactions under multi-condition inputs.
We propose an Attribute-dependent Active Sampling strategy that estimates training difficulty across semantic dimensions and selects high-value samples through diffieulty-aware weighting.
We construct Maritime Generation Dataset, the first generative benchmark for maritime detection. Experiments show that our method improves both generation quality and detection performance in challenging scenarios.
2 Related Work
Diffusion-based Image Generation. Diffusion models [14] have demonstrated strong cross-modal generation capabilities and have been widely adopted for image synthesis tasks. Early works such
as DALL-E 2 (26] employed hierarchical diffusion guided by CLIP text encoders (25] to achieve
text-to-image generation, while Imagen (31 further improves generation quality by leveraging the language understanding power of large-scale language models. Stable Diffusion (SD) (30) made this technology more accessible by introducing latent space compression, enabling efficient high-resolution synthesis. However, text-only conditioning often lacks fine-grained spatial and attribute-level control. To address this, recent layout-to torouttto-image (L21) methods incorporate auxiliary conditions for more precise generation. For example, LayoutDiff (50) integrates bounding box constraints into the diffusion process, GLIGEN [18) introduces gated self-attention to fuse layout and textual conditions in pre-trained SD model, while RC-L2I [5] employs regional cross-attention to enhance instance-lewe controllability. However, these methods generally treat object regions independently and overlook interactions with complex scene contexts, limiting their effectiveness in domains like maritime environments.
Data Augmentation for Object Detection. Early data augmentation strategies such as Mixup 48). CutMix 477, and Mosaic 2 primarily rely on pixel-level rearrangements to increase visual diversity. While effective in remixing existing patterns, these methods are limited in their ability to produce novel samples that extend beyond the original training distribution. This constraint has driven recent interest in generative augmentation, where image synthesis models are used to create new samples with richer semantics and structural variability. For instance, DA-Fusion [36] employs a diffusion model enhance dataset diversity, while Fang al. [I] introduce a visual prior-guided controllable diffusion framework for object detection. AeroGen [4 further explores conouttcond diffusion to generate synthetic remote sensing imagery based on rotated bounding boxes. However, most existing generative augmentation methods focus solely on generation and overlook the importance of evaluating the training utility of synthesized samples, which makes it difficult to prioritize data that maximally benefits downstream learning.
Active Learning for Object Detection. Active learning for object detection aims to boost model performance with minimal labeling effort by selecting the most informative unlabeled samples. Early methods [32 45 ID adapt classification-batt -baeed strategies but often ignore the localization task.
To address this, later works introduce incertainty-basee approaches. For example, Choi et al. M modeled joint uncertainties using Mixture Density Networks, while PPAL [42 combines difficulty- calibrated uncertainty sampling with diversity- images exhibit semantic diversity, their impact on detector training varies. To improve efficiency, we draw on active learning principles to design a sample selection strategy tailored for generated data. Unlike conventional methods, which rely on human annotation, our approach leverages known labels from the generation process, avoiding annotation costs while enabling difficulty- selection.
selection. In our setting, although synthetic
3

---

Text Condition
(1.1) Ob
Dearted Ozject Mede
jec Concitir (Bax Ganegen))
catutatoo for siat saring rulk.
bud
waaty
Laysuft Candition
Objec
Crbeedar
(b.3) Bidincctiond Cruss Atre
Repuat
Surfece
Beshage
Reshagpe
Onbodder
Besicc Black
Recia Bock
Fecrrres
Erovasse
Mcswing
Outpal
Sait-itt
Nurtlee Clierts
* (1) BIDW-Arm
Water Our foce Conaition (leak Desemption)
reforiing sereed
Takes
Crossutte
Ewarted Water Surface Mask
Miterr tikers
Data Generction Pipeline
Bidirectional Object Water Attention
Figure 2: Architecture of our M-ri-Mari generator. BiOW-Attn serves as the core component for integrating object and water surface conditions.
3
Neptune-X
This section introduces two key components of the proposed Neptune-X, including X-to-Maritime Generation and High-quality Data Generation.
3.1 X-to-Maritime Generation
Latent Diffusion Model. As state-of-the-art approach for text-to-image generation, SD 330 establishes itself as an effective framework for generative modeling. this paper, we adopt SD as the foundational architecture due to its demonstrated e effectiveness in conditional image generation. classic Latent Diffusion Model (LDM), comprising two parts:
To be specific, SD
Latent Space Projection: A Variational Auto-Encoder (VAE) learns bidirectional mappings between pixel-level images / e # x/flWxx RGB space and compressed latent codes E R where denotes the number of channels, while h H /m and w W/m denote the reduced spatial dimensions through a compression factor m. This dimensionality reduction enables computationally efficient diffusion processes while preserving essential visual features.
Conditional Diffusion Process: SD employs text-conditioned diffusion training through caption embeddings C, implementing Markov chain that gradually denoises latent representations across T timesteps. The denoising operator go, parameterized by 0, is optimized to estimate noise components through noise prediction objective, which can be given by
C (.,,,e [|le (z C)
where 2t represents the noisy latent at timestep t. This formulation enables stable SD training through gradient updates while maintaining semantic alignment between the caption conditions and generated images.
Multi-Condition Guidance, Despite extensive training in the SD model and its remarkable efficacy in text-to-image synthesis, effectively incorporating layout conditions jointly guide the maritime scenario generation process remains challenging. To address this limitation, we design a novel domain-specific model (named Neptune-X) for the generation of maritime images, shown in Fig. 2h. In particular, we propose a well-designed Bidirectional Object- Water Attention (BiOW-Attn) module integrate additional layout conditions from the water surface targets and the water body itself, thereby enhancing the generative capability and controllability in maritime scenarios. To enable multi-condition guidance, thus extend the denoising objective in Eq.
L = -=E2teo ||c - 1-golz,,t,C MMR
(C.:Mw) )12
(2)
object conditions
water surface condition

---

Generoted Training Set
Attribute depeeudeeddd Active Sampling
Training Set
Detector
Detection Results
ClassiFication
Easy
A
IoU
Pre-trained
Layout
Antooute-rdstee Tranig Difficulty Pactora
Reweight
Sort Select
Semantic
Hard
AAS
X-to-Maritiime
Selected Samples
Generation
Figure 3: Data generation flowchart of Neptune-X. AAS holistically integrates both detection accuracy and training difficulty through introducing the attribule-related training difficulty factors as weights to select high-value samples generated by X-to-Maritime.
where O denotes the number total objects, Cj and Mc represent the i-th object's feature embedding and binary spatial mask, respectively, and C. and Ma denote the water surface feature embedding and corresponding binary mask.
Layout Condition Embedders. Inspired by GLIGEN [88. our module employs an identical embedding strategy to transform both conditional types into token representations. For the i-th object with class label T. and spatial coordinates P we first encode the coordinates through Fourier embedding & to obtain positional features e 2 (P)). Simultancously, the textual label L is encoded by CLIP text encoder & into semantic tokens t' {(L)). The final feature embedding of the i-th object C is then derived through channel- wise concatenation [-; and MLP projection, i.c.
C. = MLP ([e::t)])
Similarly, we obtain the water surface embedding C., following the same procedure as object embedding, where we replace L' with a water surface description Lw and P. with the minimum enclosing rectangle P.,. of the water mask.
Bidirectional Soject-Water Attention. As shown in Fig. 2 we propose BiOW-Attn to enable the targeted generation of maritime scenarios, which comprises stages, i.e., conditional integration and bidirectional feature interaction. In the first stage, cross-attention modules independently process each object and water embeddings through identical operations applied the input feature and each embedding, generating conditionally augmented features as defined mathematically by
(
Q-KT X
Cross-Att(Q, KR. V&) = Softmax
VE,
le E (C) Cw
(4)
where À is scaling factor. Q denotes the query vector computed from input features, while (Kr, Vi corresponds to the key-value pairs projected from water and object embeddings.
For the object cross attention module in Fig. 1 the enhanced features ( guided by each object are summed to produce the output. The aggregated output then masked using the union of all object masks (M;) ) while non-object regions are filled with learnable null object embedding nullatj to stabilize spatial localization. Mathematically, the output F, can be generated by
F
Σ
fg
cM null @ MM, where M = UM f=1
(5)
In contrast, the water cross attention module in Fig. 2p.2 follows an identical architecture to obtain the output F F.. with M replaced by the water-specific mask M,, and null,k substituted by the water null embedding null
The second stage processes F. and F.. generated from the previous step through bidirectional cross attention Fig. 2p.3 by exchanging the input sources query vectors and key-value pairs. This module significantly improves the object- tbject-water water boundary interaction of generated images, thereby generating more physically plausible water surface targets. The final output produced by a Feed-Forward Network (FFN).
5

---

3.2
High-quality Data Generation
Fig- shows the data generation process of Neptune-X. Building upon the X-to-Maritime generation model constructed in Sec. 3.1 we employ random transformations (including randomly sampling image and water surface descriptions, and resizing or flipping annotated bounding boxes, etc.) on the existing training set labels to enhance the diversity of generated data. Subsequently, we apply a filter inspired by AeroGen M4 to eliminate low-quality data. This filter evaluates the generated data from two perspectives: 1) semantic consistency assessed by CLIP model, and 2) layout accuracy verified by a pre-trained ResNet classifier (ensuring alignment between generated objects in bounding boxes and their actual label categories). Finally, we pre-train detector model on the small-scale training dataset and employ for active sampling on the filtered data to automatically select the final samples with high value. Notably, this process incorporates a specially designed active sampling mechanism (named AAS) by introducing Attribute-correltted Training Difficulty Factors (ATDF) to optimize downstream detector accuracy.
Attribute-correlatee Training Difficulty Factors. The proposed ATDF can prompt the detector to select more challenging samples, thereby balancing training difficulty. Specifically, the ATDF is computed during the detector's pretraining phase. We measure each box's accuracy against ground truth, aggregate attribute speeuutttsssssccccceeeeeeeeecece difficulties, and perform intra- mnta--imensiooal normalization. In this process, the accuracy of each predicted bounding box is calculated using the method proposed in [4], which can be defined as
Acc(b, b) - 50' IoU 8) 1>
Here, 7 donates hypee--arameete, b and b are the predicted box and the corresponding ground truth, E [0, is the prediction confidence of b, the notation [oU(b, b) E [0, 1] represents the Intersection-over -vniuuooo (IoU) metric calculation between and b.
Based on the defined bounding box prediction accuracy, we compute the ATDF across all attributes in four dimension Each predicted box inherits the extra three specified attributes from its image's category label. Ultimately, each predicted box's 's accuracy is assigned to the specified attributes across all four dimensions. For the j-th evaluation iteration, let N be the number of predicted boxes possessing the s-th attribute. Its initial training difficulty dg can be expressed as
N
dg
Σ (1 - Acc,).
Then, we employ Exponential Moving Average (EMA) to update the ATDF for each attribute, enabling it to characterize the difficulty discrepancy across the entire training and validation sets. Specifically, the final ATDF of the s-th attribute in the j-th iteration is obtained by weighting the initial ATDF with the previous timestep's 's ATDF, expressed
dg + m m dg + (1 m
(8)
with m being the attribute wise momentum at the previous moment. The momentum term for the current iteration my is updated based object presence/absence, formalized as
m 11 m
If N > O, If N = O,
m
m
where mQ being the initial momentum. This adaptive momentum mechanism accelerates update rates for rare samples, thereby mitigating update speed disparities caused by sample imbalance within the same dimension [42). Finally, the ATDFs of all attributes within the same dimension transformed into a probability distribution via the softmax function, which can represent the training difficulty of each attribute in the entire pretraining phase. Note that higher probability values indicate higher training difficulty.
Attribute-eppenden Active Sampling. We design the AAS to actively select high-value samples from the data pool generated by X-to-Maritime to optimize downstream detection tasks. Specifically, taking the original train set as Xurann the filtered generative dataset pool is defined as X ggen The core objective of AAS is to select high-value samples X C Xgen using a detector D pre-trained on Ximin
Besides the category, add three additional dimensions, ie.. viewpoint, location, and imaging environment.

---

aas
in cireeeee cossing, wito the als parterad of and witter relacting upea from and slipe of ligta from, baita
(en leagee a rlar soene tares, fme, aiter dhriigg musk realiy is selettr with
axqpustigg
uetting sarfire refiction of Lights fon bridge and sly!
slight
sarling Tie westee
stte art defoouot legetig i'ne weter surfior codbits mouiny. armvssee guztt mpiia.
ciaectilus anda dutino dee degttin, warter eppans he der pisr vaaliing and aatural Lightios. qualitg
aight precilii rue.
AAn lmaage strenc leliz sienchalion from slort
og ttg-ttg syneres
affiissd surfhie crebii salee, sook, wich gevile riples
lsagg
durtng day
with iagur navilige, ruggottnn posr aucaating vhevelee car coltaic zup gitto stór geutte
parge
Conditions
GLIGEN
InstDift
RC-L2I
(g)
Figure 4: Comparison of image generation on MGD. The red, green, and yellow bounding boxes indicate low- www-qualiy/incorrect generation, missed generation, and unexpected generation, respectively.
Subsequently, both Xurin and X are utilized to fine-tune 'D further. Specifically, each predicted box is first used to calculate accuracy with its corresponding ground truth label according to Eq. 6 The ATDF values serve weights for the detection accuracy scores, producing a composite training difficulty measure per image. For a given image with viewpoint, location, and imaging environment ATDFs defined as driow, disc= and dewv. respectively, and with class-wise ATDFs {dh J. of N objects, the image's 's training difficulty d is computed as
d=
I
I aEA
1
Σ n=1
diis (1 Ac-)))
(10)
where = {view, loc, env) and is a tunable parameter. Finally, all samples are ranked by and the top-k highest-diffiulty instances are selected to form the X
Experiments
4.1 Experimental Settings
Table 1: Data source of MGD.
Implementation Details. The Neptune-X
framework is implemented in PyTorch 1.13
Source
Imaging Viewpoint
Num.
(Python 3.8) and executed on a PC with 2 Intel(R) Xeon(R) Silver 4410Y CPUs and 4 NVIDIA 5880 Ada GPUs. In the training, we employ the Adam W optimizer with an initial learning rate of 5x 10 for 100, 000 iterations (requiring 100 training hours), while we apply the standard data augmenta- tion techniques, including random horizon- tal flipping and scale resizing. The patch size and batch size are 512 x 512 and for model training. Notably, we reduce train-
MaSTr1325
ship view
800
USVInland
ship view
1000
MIT Sea Grant
ship view
100
SMD 24
shore and ship view
400
Seaships
33
shore view
1500
Seagull 29
aerial view shore view shore, ship, and aerial view
2996
Fvessel 12
1500
LaRS 52
1973
Others
shore, ship, and aerial view
1631
MGD
shore, ship, and aerial view
11900
ing costs and preserve the base model's generative capabilities by freezing the SD weights while only updating layout condition embedders and BiOW-Attn modules.
7

---

Table FID, CAS, and YOLO Score comparisons of different methods on image generation. The best and second-bes results are highlighted in bold and underlined.
YOLO Score f
Methods
Conditions
Venue & Year
FID
CAS
mAP/mAPso/mmAPrs
SD1.5 30
Text
CVPR2022
27.65
LafoutDiff
50
Box
CVPR2023
18.17
63.77
0.83/2.688/022
GLIGEN 18
Text + Box
CVPR2023
20.02
77.06
12.74/30.36/8.
InstDiff 38
Text + Box + Mask
CVPR2024
19.43
76.65
12.46/29. 73/929973//077
RC-L21
Text Box Mask
NeurIPS2024
25.63
74.84
8.75/22.99/5.45
Ours
Text + Box + Mask
Neur(PS2025
18.05
79.34
17.05/39.14/13.22
Imaging
foody
ship
Category
person
Table 3: mAP and mAPs0 comparison with/without generated data.
424
duskdtiww
Roating dbject
51.5
nightts
Siedd object
Model
mAP1 1
mAP50 f
YOLOv10 37
39.99
61.13
42,4
13.
V..3
+Gen Data
43.62 (+9.08%)
65.50 (+7.15%)
202
19
294
YOLOv11 [16]
41.29
62.51
25.
Vieupoint
+Gen Data
44.43 (+7.60%)
66.15 (+5.82%)
iice
harbbr
Ovoor
Mear: 51-17 Varranee 128.65
scrlal
WD Gen Data
YOLOv12 35
39.06
60.53
Location
Gen Data
Mean: Variance: 118.74
+Gen Data
42.91 (49.86%)
63.85 (+5.48%)
Figure 5: YOLOv1 I accuracy improvement visualization across various attributes.
Ship Generation Dataset. To advance research on image generation and object detection in maritime scenarios, we propose MGD, a comprehensive maritime image generation dataset. As shown in Ta MGD consists of 11,900 samples collected from multiple benchmark datasets and images we captured using various imaging devices, including coastal surveillance systems, UAVs, smartphones, and DSLR cameras. Each sample contains image with corresponding caption, water surface mask, and bounding box annotation, covering five object categories (ship, buoy, person, floating object, and fixed object), three viewpoints (shore-based, shipboard. and aerial). four locations (sea, river, harbor, and lake). and six imaging environments (sunny, cloudy, foggy, rainy, dawn/dusk, and night). Furthermore, MGD is split into training (7,140 samples), validation (2,380 samples), and sets (2,380 samples) in 3:1:1 ratio, with the validation and test sets combined for image generation evaluation. More details about MGD are provided in the supplementary materials.
Evaluation Metrics. For the image generation evaluation, we use the Frechet Inception Distance Score (FID) [3 for evaluating image generation quality, Classification Score (CAS) (8) and YOLO Score 19 for assessing generated object accuracy. For the data augmentation experiment, the mean
Average Precision (mAP) and mAPs0 so are utilized.
4.2 Image Generation Experiments
As shown in Table 2 our method demonstrates superior performance across all three evaluation metrics. While LayoutDiff achieves competitive FID scores, its significantly inferior YOLO Score demonstrates limited practical applicability. Notably, our method achieves significant improvements in both CAS (+2.28) and YOLO Score (+4.34/8.. 78/4.45), the two key metries evaluating controlled generation capability, substantially outperforming current SOTA approaches. Furthermore, Fig. 4 presents several generated instances of controllable diffusion methods for maritime image generation. These SOTA competitors frequently exhibit missing, erroneous, and unrealistic generation. In contrast, our method achieves superior controllable object generation through the BiOW-Attn module, which enhances object-water interactions to produce more harmonious and realistic maritime scenes.
4.3 Data Augmentation Experiments
Effectiveness on Traditional Detectors. this section, we validate the effectiveness of the proposed Neptune-X. Specifically, three advanced object detectors (i.e.. YOLOv10 (37). 11 [16), and 12

---

were selected for evaluation. Quantitative results on the test set are presented in Table 3 Notably, all detectors demonstrate significant performance improvements by adding generated data as training samples, achieving mAP gains of 7-10% and mAP50 improvements of 5-8%. Meanwhile, Fig. deemontttteess the detection accuracy improvement of YOLOv11 across all attributes in four dimensions before and after data augmentation. All metrics show improvement, with particularly significant gains observed for attribute categories that originally had lower detection accuracy (The mean increased by 13.77% and the variance decreased by 6.39% These results demonstrate the superiority of our proposed generation-selection paradigm for marine object detection, achieving significant accuracy improvement while mitigating cross cotssbet training difficulty disparities.
351
Effectiveness on Open-Vocalee Vocabulary De- tectors. The proposed data augmentation method demonstrates broad applicability and can be integrated with various types of detectors, including open-vocabulary de- tectors. To validate its generalization pability, additional experiments were con- ducted using Grounding DINO [22]. The experimental results, as shown Table indicate that the proposed method significantly enhances the detection performance, with notable im- provements observed in both mAP and mAP50 metrics. These findings suggest that the method not only compatible with common YOLO-scries detectors but also effectively improves the performance of open-vocabulary detectors.
Table 4: mAP and mAP30 comparison with/withow
generated data. denotes fine-tuned on our dataset.
Model
mAP
mA mAP f
Grounding DINO
8.42
12.60
Grounding DINO
65.03
86. 12
+Gen Data
68.04 (+4.63%)
89.86 (+4.34%) %)
Table Ablation study of different generation configurations.
BiCA
YOLO Score
ObjCA
WatCA
FID
CAS
Obj2WatCA
Wat2ObjCA
mAP//APsc///APrr
21.44
76.23
10.69/26.01/6
19.57
78.15
(..77/29600/100
18.35
78.00
12.52/27 58/10.06
0/552/27588/1006
18.37
78.68
15.60/36. 13/12.09
18.05
79.34
17.08/39.14/1..22
4.4 Ablation Study
Effectiveness of Generation Modules. this sub- section, systematically evaluate different cross- attention (CA) components for maritime scene gen- eration, including Object CA (ObjCA), Water CA (WatCA), and the bidirectional CA (BiCA) that con- sists of Water-to-Object CA (War2ObjCA) and Object- to- Water CA (Obj2WatCA). As quantitatively demon- strated in Table 5 our experiments reveal several key findings. The basic ObjCA alone yields unsatisfactory performance in both overall image quality (evaluated by FID) and target generation accuracy (assessed by the other two metrics) for maritime scenario generation. While introducing water conditions sig- nificantly enhances generation capability (notably reducing FID by 1.87), the simple usage fails to properly model object-water interactions, resulting unrealistic scene-target relationships that limit YOLO Score improvement despite decent CAS results. The bidirectional attention mechanism provides a comprehensive solution. Specifically, Wat2ObjCA effectively improves CAS and YOLO Score by enhancing object features based on water context, while Obj2WatCA further refines water characteristics using object conditions. The full integration of all modules achieves state-of-the-ee performance, highlighting the importance of our innovative dual-path modeling that simultaneously ensures high-quality scene generation and physically plausible object-water interactions.
Table 6: Ablation study of different pling strategies.
Methods
Number
mAP
mAPs0
N/O
39.99
61.13
5,000
41.48
63.19
Random
10,000
43.31
64.95
5,000
43.11
64.70
AAS
10,000
43.62
65.50
'It is worth noting a counterintuitive observation: detection performance under sunny conditions was lower, likely due intense sunlight causing water surface glare, which is uncommon in typical training data.

---

Effectiveness of AAS. To evaluate the effective- ness of the proposed Attrebutecoorrllated Active Sampling (AAS) strategy, we conducted compara- tive expeeriment experiments using YOLOv10 as the baseline detector. As shown in T the results demon- strate AAS's clear advantages over random sam- pling through two key observations. First, AAS achieves superior performance. The mAP and mAP30 obtained using 5,000 AAS-selected sam- ples match that 10,000 randomly sampled in- stances, while significantly higher than the ver- sion of 5,000 randomly sampled instances. Second, while random sampling shows substantial perfor- mance gains when increasing from 5,000 to 10,000 samples (AmAP=1.5 .83 and AmAP 50 .76), AAS exhibits minimal improvement (AmAP=0.51 and AmAP5o=0.800 in this range. This difference stems from AAS's targeted selection mechanism, which effectively identifies and prioritizes high- value samples in the sampling phase, leading to faster convergence and reduced need for additional samples. To thoroughly validate this and determine the appropriate number of training samples, this section conducted relevant experiments. As shown in Fig. O significant saturation effect in detection performance improvement was observed when the data volume increased from 10,000 to 20,000 samples. This phenomenon indicates that the AAS method achieves performance gains by pre-screening the most valuable generated samples. For lower-ranked samples, since the model has already learned relevant features from previous high-value samples, the information contained in these samples is no longer novel the model. Therefore, further increasing such samples does not lead to significant performance improvements. The active selection strategy of the AAS method enables rapid performance gains with a smaller data volume while significantly reducing computational costs and additional training overhead. This stands in sharp contrast to traditional methods that rely on large amounts data. To sum up, these results collectively confirm the efficiency and practicality of the AAS method in maritime scene object detection tasks.
45
68
44
67
43
65
42
64
41
MAP
63
40-
mAP
39
-11
10,000 20,000 30,000 40,000 50,000
Number of Generated Samples
Figure 6: Correlation between detection accu- racy and the number of generated samples used.
5 Conclusion, Limitation, and Future Work
In this paper, have presented data generation-selection paradigm (Neptune-X) reduce the cost of data collection and annotation while addressing cross-attribute training difficulty caused by limited sample diversity. Our method intends enhance maritime scene generation through attention to object-water interaction and improve training efficiency via an attribute-aware sample selection strategy that considers both predicted accuracy and difficulty priors. We have also constructed new dataset to support maritime image generation. Extensive experiments have demonstrated the effectiveness of our approach in both image synthesis and data augmentation, significantly boosting the detection performance.
While method demonstrates significantly better performance, it currently relies fixed set of predefined attribute categories (c.g.. viewpoint, lighting condition, object type) to estimate training difficulty. This discrete formulation may limit the granularity of difficulty modeling and adaptation. Future work should explore the framework extension to support continuous or hierarchical attribute spaces, allowing for more nuanced difficulty estimation.
Acknowledgments. This work is supported by the JC STEM Lab of Smart City funded by The Hong Kong Jockey Club Charities Trust (2023-0108), the Hong Kong SAR Government under the Global STEM Professorship and Research Talent Hub, the Guangdong Natural Science Funds for Distinguished Young Scholars (Grant 2023B 18 1515020097), the National Research Foundation Singapore under the AI Singapore Programme (AISG Award No: AISG4-TC-2025-00 -11-5-008-KKKK) and the Lee Kong Chian Fellowships
Impact Statement. This research advances machine learning and develops innovative solutions supporting technological progress in areas like intelligent navigation and smart infrastructure. It is not oriented toward commercial exploitation or other non-academic applications.
10

---

References
[1] S. Agarwal, H. Arora, S. Anand, and C. Arora. Contextual diversity for active learning. In European Conference Computer Vision, pages 137-153. Springer, 2020.
[2] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint wrXiv:2004..0900 2020.
[3] B. Bovcon, J. Muhovič, J. Perš, and M. Kristan. The mastrl 325 dataset for training deep usv obstacle detection models. In JEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3431-3438. IEEE, 2019.
[4] Z. Chen, C. Yang, Q. Li, F. Zhao, Z.-J. Zha, and F. Wu. Disentangle your dense object detector. In ACM International Conference on Multimedia, pages 4939-4948, 2021.
[5] J. Cheng, 7. 7hao, T. He, T. Xiao, 7. Zhang, and Y. Zhou. Rethinking the training and evaluation of rich-context soyouttt--image generation, Advances in Neural Information Processing Systems, 37:62083-62107, 2024.
[6] Y. Cheng, M. Jiang. J. Zhu, and Y. Liu. Are ready for unmanned surface vehicles in inland waterways? the usvinland multisensor dataset and benchmark. IEEE Robotics and Automation Letters, 6(2):3964 3970, 2021.
[7] Choi, Flezi, H.-J Lee, C. Farabet, and J. M. Alvarez. Active learning for deep object detection via probabilistic modeling, In IEEE/CVF International Conference Computer Vision, pages 10264-10273, 2021.
[8] Y. Dai, T. Xiang, B. Deng, Y. Du, H. J. Qin, and S. He. Stylegan-oo: Extending stylegan to arbitrary-ratio translation with stylebook. IEEE Transactions on Visualization and Computer Graphics, 2024.
[9] M. DeFilippo. M. Sacarny, and P. Robinette. Robowhaler: A robotic vessel for marine autonomy and dataset collection. In OCEANS, pages 1-7. IEEE, 2021.
[10] Y. Du, J. Zhan, X. Li, J. Dong, S. Chen, M.-H. Yang, and S. He. One-for-all: towards universal domain translation with a single stylegan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
[11] H. Fang, B. Han, S. Zhang, S. Zhou, C. Hu, and W.-M. Ye. Data augmentation for object detection via controllable diffusion models. In IEEE Workshop Applications of Commputr Vision, pages 1257-1266, 2024.
[12] Y. Guo, R. W. Liu, J. Qu, Lu, F. Zhu, and Y. Lv. Asynchronous trajectory matching-based multimodal maritime data fusion for vessel traffic surveillance in inland waterways. IEEE Transactions Intelligent Transportation Systems, 24(11):12779-1279) 2023.
[13) M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and Hochreiter. Gans trained by a two time-scale update rule converge to local nash equilibrium. Advances in Neural Information Processing Systems, 30, 2017.
[14] J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851 2020.
[15] Q. Jiang, F. Li, Z. Zeng, T. Ren, Liu, and I.. Zhang. T-rex2: Towards generic object detection via text-visual prompt synergy. In European Conference on Computer Vision, pages 38-57. Springer, 2024.
[16] R. Khanam and M. Hussain. Yolov11: An overview of the key architectural enhancements. arXiv preprint arXi:22410.17725, 2024.
[17] F. Li, R. Zhang, H. Zhang, Y. Zhang, B. Li, W. Li, Z. Ma, and C. Li. Llava-next-interleave Tack- ling multi-image, video, and 3d in large multimodal models, arXiv preprint arXiv:2407.07895 2024.

---

[18] Yi. H. Liu, Q. Wu, F. Mu, J. Yang, J. Gao, Li, and Lee. Gligen: Open-set grounded text- to-image generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. pages 22511-22521, 2023.
[19] Z. Li, J. Wu, I. Koh, Y. Tang, and L. Sun. Image synthesis from layout with locality-aware mask adaption. In JEEF/CVE International Conference on Computer Vision, pages 1381 13819-13828, 2021.
[20] Z. Li, Xu, N. Zhao, Y. Zhou, Y. Liu, D. Lin, and He. Parsing-conditionee anime translation: A new dataset and method. ACM Transactions on Graphics, 42(3):1-14, 2023.
[21] H. Liu, C. Xu, Y. Yang, L. Zeng, and S. He. Drag your noise: Interactive point- based editing via diffusion semantic propagation. In JEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6743 6752, 2024.
[22] S. Liu, Z. Zeng. T. Ren, F. Li, H. Zhang. J. Yang, C. Li, J. Yang, H. Su, J. Zhu, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. European Conference on Computer Vision, 2024.
[23] Z. Lu, C. Wang, C. Xu, X. Zheng, and Z. Cui. Progressive exploration-conforma learning for sparsely annotated object detection in aerial images, Advances Neural Information Processing Systems, 37:4059334408
2024.
[24] D. K. Prasad, D. Rajan, L. Rachmawati, E. Rajabally, and Quek. Video processing from electro-optical sensors for object detection and tracking in maritime environment: A survey, IEEE Transactions on Intelligent Transportation Systems, 18(8):1993-200 1011199999616-2.. 2017.
[25] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. 2021.
In International Conference on Machine Learning, pages
[26] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.0612. 22622, 1(2):3, 2022.
[27] N. Ravi, V. Gabeur, Y.-T. Hu, R. Hu, C. Ryali, T. Ma. H. Khedr, R. Rädle, C. Rolland, L. Gustafson, al. Sam 2: Segment anything in images and videos. arXiv preprint 000:0, 2024.
[28] S. Ravuri and O. Vinyals. Classification accuracy score for conditional generative models. Advances in Neural Information Processing Systems, 32, 2019.
[29] R. Ribeiro, G. Cruz, J. Matos, and A. Bernardino. A data set for airborne maritime surveillance environments. IEEE Transactions on Circuits and Systems for Video Technology, 29(9):2720- 2732, 2017.
[30] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In /EEE/CVE Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022.
[31] C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gon- tijo Lopes. B. Karagol Ayan, T. Salimans, al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35: 36479-36499, 2022.
[32] O. Sener and S. Savarese. Active learning for convolutional neural networks: A core-set approach. In International Conference on Learning Representations, 2018.
[33] 7.. Shao, W. Wu, 7. Wang, W. Du, and C. Li. Seaships: A large-scale precisely annotated dataset for ship detection. IEEE Transactions on Multimedia, 20 200402255332644 2018.
[34] D. Tang, X. Cao, X. Wu, Li, J. Yao, X. Bai, D. Jiang. Y. Li, and D. Meng. Aerogen: enhancing remote sensing object detection with diffusion-driven data generation. In IEFE/CVE Conference on Computer Vision and Pattern Recognition, 2025.
12

---

[35] Y. Tian, Q. Ye, and D. Doermann. Yolov12: Attention-centre real-time object detectors. arXiv preprint 1rX1v:2502.12524, 2025.
[36] B. Trabucco, K. Doherty, M. Gurinas, and Salakhutdinov. Effective data augmentation with diffusion models. International Conference on Learning Representations, 2024.
[37] A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, al. Yolov10: Real-time end-to-end object detection. Advances in Neural Information Processing Systems,
2024.
[38] X. Wang, T. Darrell, S. S. Rambhatla, R. Girdhar, and Misra. Instancediffusion: Instance- level control for image generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6232-6242, 2024.
[39] Y. Wang, T. Xu, Z. Fan, T. Xue, and J. Gu. Adaptiveisp: Learning an adaptive image signal processor for object detection. Advances Neural Information Processing Systems, 37:1 12598- 1 112623, 2024.
[40] F. Xu, C. Chen, Z. Shang, K.-K. Ma. Q. Wu, Z. Lin, J. Zhan, and Y. Shi. Deep ship detection and classification network. IEEE Transactions on Circuits and Systems for Video Technology, 2024.
[41] Y. Xu, W. Shao, Y. Du, Zhou, J. Xie, P. Luo, and S. He. Invert your prompt: Editing-aware diffusion inversion. International Journal of Computer Vision, 2025.
[42] C. Yang, L. Huang, and E. J. Crowley. Plug and play active learning for object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17784 17793, 2024.
[43] X. Yang, H. She, M. Lou, Ye, Guan, Li, Xiang, Shen, and B. Zhang. A joint ship detection and waterway segmentation method for environment-aware of usvs in canal waterways. IEEE Transactions on Automation Science and Engineering, 2024.
[44] Z. Yang, L. Wen, J. Deng, J. Tao, Liu, and D. Liu. Fcos-based anchor-free ship detection method for consumer electronic uav systems. IEEE Transactions Consumer Electronics, 2024.
[45] D. Yoo and I. S. Kweon. Learning loss for active learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 93-102, 2019.
[46] Y. Yu, B. Liu, C. Zheng, X. Xu, H. Zhang, and S. He. Beyond textual constraints: Learning novel diffusion conditions with fewer examples. In JEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7109-7118, 2024.
[47] S. Yun, D. Han, J. Oh, S. Chun, J. Choe, and Y. Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. In IEEE/CVF International Conference Computer Vision, 2019.
[48] H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz. mixup: Beyond empirical risk mini- mization. In International Conference on Learning Representations, 2018.
[49] C. Zheng, Liu, H. Zhang, X. Xu, and S. Hc. Where is my spot? few-shot image generation via latent subspace optimization. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3272-3281, 2023.
[50] G. Zheng, X. Zhou, X. Li, Z. Qi, Y. Shan, and Li. Layoutdiffusion: Controllable diffusion model for layout-to-image generation. In /EEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22490-22499 2023.
[51] Z. Zhong, L. Zheng, G. Kang, Li, and Y. Yang. Random erasing data augmentation. In AAAI Conference Artificial Intelligence, 2020.
[52] L. Žust, J. Perš, and M. Kristan. Lars: A diverse panoptic maritime obstacle detection dataset and benchmark. In JEEE/CVF International Conference Computer Vision, pages 20304-2034 2023.
13

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Yes. Our main contributions also detailed in Sec. D Also see Sec. and Appendix for more theoretical and experimental evidence.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification: Yes, please see Sec. 5 for limitations.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
14

---

Justification:
This paper does not include theoretical contribution.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: We have provided the implementation details the paper for result repro- ducibility.
Guidelines:
The answer NA means that the paper does include experiments.
If the paper includes experiments, No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how reproduce that algorithm. (b) If the contribution is primarily
model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome deseribe the particular way they provide for reproducibility, In the case of closed-source models, it may be that access to the model is limited in some way (e.g., registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
15

---

Answer: [Yes]
Justification: We use the publicly accessible dataset in Table Once the blind review period is finished, we' Il open-source all codes, instructions, and model checkpoints.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.. cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to to reproduce the results. See the NeurlPS code and data submission guidelines (https
ssicnpolicy) for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Please see Sec. and Appendix A
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [No]
Justification: Our experiment is computationally intensive thus we only report the average number on the testing set.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emror bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
16

---

It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative enror rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Question:
Answer: [Yes]
Justification: Please see Sec.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary failed experiments that didn't make into the paper).
9. Code ethics
Question: Does the research conducted the paper conform, in every respect, with the NeurlPS Code of Ethics http:p/pp///.cccc..c.o/oo//////a
Answer: [Yes]
Justification:
We followed the NeurlPS Code of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., if there is special consid- eration due to laws or regulations their jurisdiction..
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [No]
Justification: This work is not related to any private personal data, and there are no explicit negative social impacts.
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
17

---

The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of MI..
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [No]
Justification:
We do not foresee any high risk for misuse of this work.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Lieenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: All code, data, and models open-sourced by the authors.
Guidelines:
The answer NA means that the paper does existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
18

---

If this information is not available online, the authors are encouraged to reach out to the asset's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification:
The new dataset will be released if the paper is accepted.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/mmoel as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question:
Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
16. Declaration of LLM usage
19

---

Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [No]
Justification: The paper does not involve LLMs in the design of ideas methods.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs any important, original, non-standard components.
Please refer to our LLM policy fattps://neur//: /ttppp/p...///oofeeerre////////0 for what should or should not be described.
20

---

A Appendix Supplemental Material
A.1 Overview
This supplement provides more data and method details well as experimental results, including:
We provide detailed information about the Maritime Generated Dataset (MGD).
We offer more details about the proposed Neptune-X.
We conduct more experiments verify the effectiveness and superiority of the proposed method.
Table 7: Sample numbers and percentages of various dimensions and attributes.
100
Sioss 555%
Dimensions
Aurriutes
Number Proportion
ship
29313
72.44%
ship 72%
Category
Environ Environment
Imaging
cloudy 23%
buoy
5326
13.16%
Category
person
4843
11.97%
floating obj.
618
1.53%
fixed obj.
366
0.90%
Maritime
Generation
shore
6042
50.77%
ship 20%
Dataset
View
ship
2459
20.66%
acrial
3399
28.56%
Viewpoin
Location
sea
carial 29%
49%
5829
48.98%
river
5531
46.48%
Location
harbor
282
2.37%
lake
258
2.17%
shore
51%
467
rives
sunny
6491
54.55%
cloudy
2794
23.48%
Imaging
foggy
1225
10.29%
Figure 7: The percentages of various dimensions and attributes in our MGD dataset.
Environment
rainy
515
4.33%
dawn/dusk
583
4.90%
night
292
2.45%
A.2 Maritime Generation Dataset
constructed the Maritime Generation Dataset (MGD), the first generation dataset for maritime scenarios. In particular, the MGD contains 11,900 samples covering diverse semantic scenes. Fig. and Table(]illuusraae the numbers and percentages samples of MGD in terms of viewpoint, location, imaging environment, and object category. Furthermore, each image sample contains visual images with corresponding labels, including water surface mask, object bounding boxes, and multi-level descriptions of the entire image, water surface, and objects. More specifically, the flowchart of data labelling shown in Fig. g which contains two steps: data collection and data annotation.
Data Collection. The data collection process involves the utilization of imaging devices deployed across multiple platforms. During this phase, we strategically selected and leveraged multiple open- source maritime benchmarks while employing diverse imaging equipment, including surveillance cameras, DSLR cameras, and smartphone cameras, to gather large-scale raw data. The collected datasets exhibit significant variations in geographical capture locations, temporal acquisition param- eters, meteorological conditions, water surface characteristics, and surface target typologics. This systematic diversity guarantees the comprchensive data richness, thereby enabling generative models to synthesize highly diversified maritime scenarios.
Data Annotation. To optimize annotation efficiency, we employed state-of- tfte-offthh--ar image detectors, segmenters, and vision- language models for assisted annotation, with human annotators performing calibration and verification of the generated labels. In the first stage, the T-Rex2 [1]] and SAM2
27 models are utilized to generate target bounding boxes and water surface masks, respectively. This semi-automated process requires manual input: exemplar images for T-Rex initialization and point prompts for SAM2 segmentation. All model outputs undergo secondary human verification to ensure annotation accuracy. The second stage leverages the state-of-the thettrr LAV--Nex [17
21

---

Data Collection
Data Annotation
Location
Clossi Exumpler
Example Image
Lnite
R-Rex
Shore
Manual Inspection
TTeak: Mask genanation
Water Region Mask
Point
Weather
Outputs:
Ship Viw
Vissil
oing Pead
iitaag descrrppiioee
Task: Rbav. and weher eurface descriphie
Surface
Instruction
wate woiliingg nuturnl lighting
USV
eftettin
Nome
Nam
Rotta
Exemploee
vwcteer surf foce descriptior>
"ris wanur car qpallicc suang suu whw dight sane
Aeriol View
Object
Abbox clettrii fione
wiite peusonge wide milli
518
1.9%%
DAY
flne...
Food B0y
Total Num. 40466
305
0.933
wiite
saanh pos otaggs."
Figure 8: Flowchart of data labelling.
Algorithm BiOW-Attn
Input: Input features fin- object's embeddings { objects' masks (M i- water sur-
face embedding Cw, water surface mask Mu, learnable null object/water surface embeddings [null null learnable object/water surface gated scaler { }..
sin reshape(F....
2: Take C and Jin inputs and calculate the object-guided feature f/ via Eq.
3: Take {fo]==1 [M))= and null as inputs and get final object-guided feature F, via Eq. (5)
4: Take Cu and fin inputs and calculate the water surface-guidee feature fwr via Eq. 44.
5: Take Mar. and null as inputs and get final water surface-guided feature F w via Eq. (5)
6: Perform bidirectional attention to obtain F and F
7: fout fm + tanh(3.) F tanh(3.)) F
8: Fout = CachappeMMP(.A....
9: return Foat-
lasion-language model for multi-scale understanding. Through tailored text prompts, the model analyzes three distinct levels of visual features:
Image Description includes shooting scenarios, camera perspectives, timestamps, weather condi- tions, and lighting.
Water Surface Description contains surface calmness, color properties, and wave patterns.
Object Description documents color attributes and detailed category features (e.g., specific ship classifications
A.3 More Details of Neptune-X
Bidirectional Object-Waad Water Attention. The detailed process of the proposed BiOW-Atth module shown in Algorithm Specifically, we first reshape the input features Fi, to facilitate cross-altention
computation (line 1). We then perform spatially masked cross-attention operations via Eq. (4) and (5) to obtain abject-conditioned feature F, and water-conditioned feature F,, respectively (lines 2-5). A bidirectional cross- attennttt module subsequently models interaction relationships between the water surface object and the aquatic surrounding. followed by gated residual fusion (lines 6-7). During initial training, we set 3, = 3 to ensure fine-tuning stability. Finally, a Ferw-Foowwar Network (FFN) processes the fused features, with output obtained through final reshaping (lines 8-9).
'Note that these fine- grained object features are extracted for more controllable image generation. Thus, this type of label is excluded from the data generation pipeline promote randomness in the generated objects.
22

---

Algorithm/ 2 Data Sampling
Input: X-to-Maritime generator G, text condition C, object conditions {C MM; M i= water surface
condition (Cw., M. number of samples N, selected sample collection Xsel ResNet classifier <. CLIP text/visual encoder {E, ). pre-trained detetcor D.
1:
for = I,.., N do
2:
1gen= G {C), Mo Mw.))
3:
Calculate layout accuracy Ace; between ((Tgen) and category labels cls corresponding to
{C)
4:
Calculate semantic aceuracy Acc, between (Fgen) and {(cls).
5:
if Ace; > ? and Acc, > T. then
6:
Get the predicted bounding boxes via D(Tgea)-
7:
Calculate the training difficulty dn of n-th samples via Eq. 10
8: end
9: end for
10: Sort by d and get the sorted sample set.
11: Select the top-k samples and put them into X
12: return Xeel
High-quality Data Generation. During the data generation phase, we enrich the generated samples by combining layout conditions and text caption conditions, ultimately producing 100,000 generated images. Subsequently, we perform data filtering through two key evaluation metrics: layout similarity and semantic similarity.
For layout similarity assessment, we train ResNet-based classifier on the MGD dataset to evaluate the generated samples. Meanwhile, employ the CLIP model to compute the cosine similarity between each image and its corresponding textual object descriptions as the semantic similarity metric. For example, if the image contains a ship and person, then the description is 'an image of ship and a person'.
In the active sampling stage, we first use the pre-trained detector to identify objects in the images and calculate accuracy by comparing them with the bounding boxes specified in the layout conditions. We then introduce the attribute-related training difficulty factors (ATDFs) weighting coefficients. Finally, we rank the results and filter underperforming samples into a training pool for iterative model optimization. The detailed flowchart is illustrated in Algorithm 2
A.4 Experiments Results
Evaluation Metrics. This section introduces the mAP metric used in object detection and three other indicators (FID, CAS, and YOLO Score) used in image generation.
mAP and mAP su: Mean Average Precision (mAP) serves as the core evaluation metric for object detection tasks. The calculation process involves four key steps. First, True Positives (TP) and False Positives (FP) are determined based on the Intersection- ovenn-over-aaee -ven-nnnionnn (IoU) threshold. Then, detection results are sorted by confidence scores to plot the Precision-Recal (P-R) curve. The area under the P-R curve computed obtain Average Precision (AP) for each class. Finally, the mean of AP values across all classes yields the mAP. The PASCAL VOC benchmark employs fixed loU threshold of 0.5 (denoted as mAP;0), while the COCO dataset adopts averaged results across IoU thresholds ranging from 0.5 to 0.95 (denoted mAP). Compared to mAP mAP imposes more stringent localization accuracy requirements. These two metrics comprehensively reflect model's detection stability across categories and its localization precision.
Fréchet Inception Distance (FID): FID 13) is a basic metric for generative model evaluation, measuring the statistical distribution discrepancy between generated images and ground truth in the latent space. In particular, this process can be divided into two steps, i.e, feature extraction and distribution distance calculation. FID first extracts the latent feature vectors fgans fgt of generated and real images by Inception-v3, and calculates the mean {Mgen: Agt) and covariance matrix (Egen } Finally, the FID distance can be calculated by
FID
||Mgon
Mggal 12 Tr (Egen + Egg 2(E Eg)) E2)
(11)
23

---

imagt... firt thoes. doudy, water Suiffle ttlin.
Image deuty Water surfele.. silm.
Image stere. P9Rg Wittr surfats-g2ygnn
magg...toe. soree- saney water Surffee camm.
mmay rugg shars night warer Surffie. Sille.
muge dee.e..ee share.. dase, Wattr Surfiee.. cflin.
"mage. set. doudy wete surface stin... gragish dise.
"mage whore. suiny way surface Jales Blae grese
baby
16).. short.. fo99, water
Insgge.. star...arr..tee shar. way surfore
tnage Autr boog sarfece
Inage srert. canny water selbre thut
mnday... thor. doudy. watr surfaze
suage Wkor. freey water
muage. 1et. Dusicy surfase.
mww.y...th...hor waiy Why purfare
perzen
Figure 9: Image generation shore viewpoints.
where || denotes the squared Euclidean norm, Tr(-) the matrix trace operator.
Classification Score (CAS): CAS 28 serves critical metric for evaluating the generation quality within bounding box-based object constraints. To compute CAS, we first train a ResNet- 101 classifier on our MGD for 100 epochs. The trained model is then used evaluate classification accuracy by comparing the categories predicted by 10s on generated images against the ground-truth categories specified in the input object conditions.
YOLO Score: YOLO Score [19 is used to evaluate the location and category accuracy of the generated objects. In particular, we utilize a YOLOv10 model trained on our MGD for 100 epochs. This trained detector evaluates generated samples by computing three standard object detection metrics, i.e., mAP (averaged over loU thresholds from 0.5 to 0.95), mAP (using 0.5 IoU threshold), and mAP15 (using 0.75 IoU threshold), which collectively form the YOLO Score.
More Generation Results. To fully demonstrate the powerful image generation capabilities of our proposed X-to-Maritime framework, we present more visualization results. As shown Figs. IO display generated maritime scenes from shore, ship, and aerial perspectives, respectively. Notably, our model demonstrates accurate comprehension of input multi-modality conditions. The generation process is jointly guided by both textual caption conditions (for scene description) and layout conditions (for controlling object and water surface position and content). Most significantly, the framework faithfully reproduces hydrodynamic interactions between water surface objects and their aquatic surroundings while maintaining strict physical plausibility. This capability directly stems from novel bidirectional object- cross-attention mechanism, which effectively models the mutual influences between maritime entities and their environment.
In addition, Fig. presents two cases with different random seeds and the removal of text conditions only. The results demonstrate that under identical imput settings, both the generated objects and the overall background exhibit rich diversity. This further validates the diversity of the generated results produced by the proposed model, an advantage primarily attributed to the rich semantic features encompassed in the constructed MGD maritime generation dataset.
we compare generation quality of models under different configurations. It can be clearly observed that using only the ObjCA module, while providing effective object control, fails to account for aquatic environments, leading to unsatisfactory generation results. Representative examples include unrealistic water-object
More Results of Different Generation Configurations. As shown in Fig. 12

---

maae. Liip. duR WEY surfee iain.
Imaae rugp .hg.. siney water Surfals, der gress
Imsge...iin sauey water surfare cat Hare grttn
Image .ng sonmy water surfass 6a Se0 BinL
Image..uur Ahtp.. BeAny water surfare seitlt grann
tmage..riur....... Aoor-aripp sokny water surfare seurey green.
magge..uue delp. douay waye surfRie...
mmagy Uig... usa Water surfilee..
Hlating
indgg..itkt... Atr3).
Inagee s.0 Jeáal... sanmy. water murfass blue-
ina.e...oa.. 2.-...ci....... Water surfere
ina.g..ss...eik. water surfoss
Image 28ia..
Se5 Abial.. sainy water surfase take Elas
nitt r.t.... Rtral. dare water surfax..
TmAge... Atr3L Watr surfoss, Buue-gyae
water surface.
surface
shp
pergan
person
Figure 10: Image generation cases on ship and aerial viewpoints.
cakse fiom
watbe opeaicl to cas
sepprrie pou dklliing wik Ligh getwiss oiginmnc
(b) Visualization of Text-Free Conditionl Generation Results
Figure 11: Image generation cases using (a) different random seeds and (b) only removing text conditions. The main reason for the scene similarity in (a) is that the text specifies background and hydrological conditions, while the unspecified objects exhibit diversity.
interactions the second case and ships floating mid-air in the third generated sample. contrast, introducing water surface conditions significantly alleviates these abnormal generation cases. The Obj2WatCA module enhances generation quality by improving water realism through object-to- water influence. However, the object control precision becomes reduced. This trade-off is visible in the first case, where objects with inaccurate positions appear in the generated results. Meanwhile, using only War2ObjCA improves visual target generation quality but still produces build failures. Ultimately, by integrating the advantages of all modules, our method demonstrates superior performance in simulating realistic object-wates interactions while maintaining high-fidelity generation.
Comparison of Different Generators in Data Augmentation. This section aims to validate the effectiveness of the proposed -trrttie framework in maritime object detection by comparing the performance improvements achieved through different generated data. The generated data were then combined with training data to fine-tune object detection models. Specifically, all generative models utilized identical conditional inputs generate expanded data. These data, along with original data, were used for fine-tuning the detectors to ensure fairness and comparability of experimental results. In the experiments, YOLOvI0 37 was employed as the object detection model, with mAP and mAP50 serving evaluation metrics.
25

---

image of sas scout takde
dheiing
imdgee stpawrss soene aeft. wirm light as aun ins, sholing govlle dlow warrr surffse
and braqqui sppenrauod sugett refledion of segs
"As Inage-ff rive scou takeo from a there stoglt skaring the slaytine The woither oppains be cider wite amopl swulligtt, e edtting a beight will-ite
aesessendnt. The whtter surphie cxbibits salis and replettivs qualtty by with siight diple effer visille on curfars."
-Ass of nea seoue frees sherkng
the skypiiee The waitvar with trigle siollgee. The water surthet (a salin with aligle Mut dirte live"
Rlject
during daytiise appaes
disple sinnigiet
Conditions
+OKjCA
+WaMCC
ObgWWaiA
WarZObjCA
Figure 12: Comparison of different confi configurations in image generation.
Table 9: Comparison of different sampling methods on YOLOv10 detection accuracy.
Table 8: Comparison different data generation meth- ods YOLOv10 detection accuracy.
Methods
mAP
mAP 50
Methods
Venue & Year
mAP 1
mAP50
w/o
39.99
61.13
w/o
39.99
61.13
Entropy
42.62
64.40
LayoutDiff (50)
CVPR2023
40.03
61.01
Variance
42.42
64.17
GLIGEN [8]
CVPR2023
41.54
62.85
Margin
42.87
64.55
InstDiff 38
CVPR2024
41.32
62.57
Greedy K-Center
42.24
63.90
RC-L2I 5
NeurIPS2024
41.48
63.26
K-Means Corset
42.27
63.79
Ours
NeurIPS2025
43.62
65.50
AAS
43.62
65.50
The experimental results, shown in Table indicate that existing generative methods perform poorly in maritime scenarios. Due to their failure to adequately account for the unique characteristics of maritime environments, such complex interactions between water bodies and objects, the synthetic data generated by these methods exhibit significant shortcomings in realism and detail fidelity. In contrast. the proposed X-to-Maritime framework incorporates Bidirectional Object- Water Attention (BiOW-Attn) module to model the water-object interaction. This module effectively captures the influence water on object appearance and the feedback effects of objects on water, thereby generating high-quality results. Experimental results demonstrate that the generated data produced by the X-to-Met Mo-Maritime framework significantly enhance detection accuracy.
Comparison of Different Sampling Methods in Data Augmentation. To comprehensively demon- strate the advantages of the proposed AAS method, extensive comparative experiments were con- ducted with five different active learning approaches. These methods include uncertainty-basee sampling strategies (Entropy, Variance, and Margin) and diversity-based sampling strategies (Greedy K-Center and K-Means Corset). For the diversity-base
methods, a 7-dimensional feature vector was constructed, incorporating the number of detection boxes, average detection box area, stan- dard deviation of detection box areas, average confidence score, standard deviation of confidence scores, mean x-coordinate of detection boxes, mean y-coordinate of detection boxes, and the number of object categories. The experimental results, presented in Tables P show that while traditional active learning methods can improve object detection performance to some extent, the proposed AAS method achieves significantly greater enhancement by comprehensively considering attributes specific to maritime scenarios, such as water conditions, weather states, and viewpoint information. This validates the effectiveness of AAS in selecting the most valuable synthetic samples through the integration of multi-dimenniionl attribute factors, further demonstrating its superiority visual perception for maritime intelligent transportation systems.
26

---

Table 10: Ablation study of different generation configurations on detection performance.
BICA
ObjCA
WatCA
mAP1
mA.P50
Obj2WalCA
Wa12ObjCA
40.94
61.18
41.09
61.15
42.54
63.85
42.75
63.93
43.62
65.50
Table 11: Ablation study ATDFs on detection performance.
Viewpoint
Location
Imaging Environment
Object Category
mAP
mAPso t
40.26
62.09
41.27
62.26
/
41.40
63.10
43.62
65.50
Ablation Study Data Augmentation with Different Generation Configurations. This section conducts ablation study each component of the BiOW-Attn module quantitatively analyze their individual contributions to the improvement of object detection performance, as shown in Table TO) The experimental results demonstrate that solely introducing water conditions can enhance object detection performance to some extent, but such improvement remains limited. In contrast, the bidirectional attention module, by simulating the interactions between water bodies and objects. more accurately reflects the physical characteristics of maritime scenarios, thereby achieving greater performance gains in object detection.
Aui lounge of scone taltss from a shere ouring tre anigllnd lic. wentier axpears be everoadi wil
AAn inage of river ssend raltoo prass share view charl the degelen was eypears be ovtroeal whth sort, Stff-aed ligitiin The water surfiie exhicits salm, lipht Bist-
'Ais lnving of soeed soloow from share angil duurling the dhiiiig The with that sat, the lighting is trigjet, andgarinng good
(roi louage of diet stond
An lsuage of scend
'As lmvage of labe soond tolose from as and view dhurling the dayliing The wenther strs stote
takton from selp durlng bir dhyfiled. five westrr cveretal with dlewdly sky and tie lighting is sort and diffuuses "hes water swrfhee
takten from tkip duriend
the silee The weather is
wentier
detr bright sunlight
cort diffuses lighting. The water sorffie salm witk
thit watdd a fore adlm
with srep sunlight, The
with alijet ripple""
surfiees is ulune siiget ripple offece.
alght grttnish heet"
-isibulity. The winer a orfass sukitits colon. fid
is with gondd ipples.
<akiy writd infataite
carip
color with gentle
blus saliiaas
bait wibr
Go
denn glag sanal.
ipple..
tolor with mustinal wint
with with
dltioo..
<akkig volies aool end muteo
culls lise flaking
vetiulay.
scunipoe worlh
<orip
callasat dliggt sail.
<oakipo white enulse wath poicctes
nod lull.
<cendp.> werlte and
<osippo
call tlaggt call.
po A wiiiy aual red pyserbeet sawdigg
ship
ship
ship
ship
ship
ship
Figure 13: Image generation cases via fine-grained object control.
Ablation Study of ATDFs. In maritime object detection tasks, the diversity and quality of samples crucial for the model's generalization capability. To more effectively leverage generated data, Active Sampling (AAS) strategy based on Attribute-correlated Training Difficulty Factors (ATDFs). This strategy quantifies the importance of each sample across different attribute dimensions and prioritizes the selection of samples that contribute most to improv- ing object detection performance. section validates the effectiveness of each dimensional ATDF in object detection tasks. As shown in Table TT. the contributions of different seeribute-dimession factors to object detection perfor- factor is associated with specific characteristics of
we propose an Attrbute-
further investigate the role of ATDFs sample selection, this
mance presented. Each
27

---

maritime scenarios. The experimental results indicate that as more sttribute-dimense factors are incorporated. the sample selection strategy becomes more precise in identifying the most valuable samples for enhancing detection performance. Specifically, when only single-dimensional factor used, the improvement object detection performance relatively limited. However, as the number of dimensional factors increases, the accuracy of sample selection significantly improves, and object detection performance demonstrates gradual upward trend. This suggests that combining multi-dimensionn attribute factors can more comprehensively capture the complexity and diversity of samples, thereby providing more valuable training data for detection models.
Discussion on More Controllable X-to-Maritime. In our data generation task, to enhance the diversity of generated targets, we employed only the object category as the object embedding feature. To explore more fine-grained control over the generated targets, including detailed category specifications (e-g., ship types like sailboats, inflatable boats, cargo ships, fishing ships) and visual attributes like color, we concatenate both the object category and its detailed description to form more controllable object embedding for model training. As demonstrated in Fig. [3] the trained model exhibits clear awareness of multi-level textual descriptions (at the image level, water surface level, and object level), while accurately simulating realistic maritime scenes according to layout conditions. The generated samples convincingly show the model's capability to respond to hierarchical textual controls while maintaining photorealistic quality.
28


Reviews:
[{"review_id": "AxuCZdQnIr", "reviewer": "Reviewer_EyhV", "summary": "This paper introduces Neptune-X, a framework for improving maritime object detection by generating synthetic training data with a generative model and selecting the most useful synthetic samples via an active sampling strategy. The authors highlight that maritime datasets suffer from scarce, costly annotation and poor diversity across key attributes (object category, viewpoint, environment, etc.).\n\nNeptune-X addresses this in two main ways:\n\n1. X-to-Maritime Generation: A diffusion-based generator (built on Stable Diffusion) extended with a Bidirectional Object-Water Attention (BiOW-Attn) module to model interactions between objects and the water surface. This improves the realism and semantic coherence of synthetic maritime scenes under multi-modal conditions.\n\n2. Attribute-dependent Active Sampling (AAS): An approach that scores generated samples by how difficult they are for an object detector, using Attribute-correlated Training Difficulty Factors (ATDFs) across dimensions like category, viewpoint, location, and environment, to prioritize hard or underrepresented cases.\n\nThey also create the Maritime Generation Dataset (MGD), the first large-scale dataset designed for training generative maritime models, with over 11,000 images and rich annotations.\n\nExtensive experiments show improvements in synthetic image quality (measured via FID, CAS, and YOLO Score) and object detection accuracy (via mAP) when augmenting detectors with selected synthetic data. An ablation study demonstrates the benefit of BiOW-Attn and AAS over baselines.", "questions": "1. BiOW-Attn: Could the authors provides qualitative result of the data generated without water condition? I am not fully motivated the purpose of water condition. Can I understand the water condition is just like the semantic mask condition telling the model here is the water? Can we just use traditional semantic mask to do this instead of designing a new attention layer?\n\n2. Sampling Strategy Comparison: How does the Attribute-dependent Active Sampling compare to standard active learning heuristics (e.g., entropy sampling, core-set)? Could the authors include such baselines?\n\n3. Detector Dependence: The pre-trained detector used for ATDF scoring is critical. How sensitive is the overall framework to the quality of this detector? Would a worse initial detector degrade AAS selection?\n\nBroader Impact Discussion: The paper briefly notes no negative societal impacts, but generated imagery could plausibly be misused for deception (e.g., fake maritime surveillance images). Would the authors consider adding even a short discussion acknowledging this risk?", "limitations": "The authors do include a limitations discussion acknowledging reliance on discrete attribute categories and proposing more continuous or hierarchical modeling in future work. They don't discuss possible misuse (e.g., generating fake surveillance imagery). I recommend adding that. Overall they are upfront about technical limitations but could improve societal impact reflection.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "## Strengths\n\n1. Clear Motivation & Importance: The maritime domain has real and specific data scarcity issues that make synthetic generation important and meaningful. I like the way how the authors adjust existing techniques in ML community to support the domain-specific applications.\n\n2. Technical Contributions: The BiOW-Attn module is a well-motivated addition, specifically modeling object–water boundary interactions for more realistic generation. The AAS sampling framework integrates active learning principles into generative augmentation, addressing the problem that not all synthetic data is equally useful.\n\n3. Strong Empirical Evaluation: Multiple metrics (FID, CAS, YOLO Score) on generation. Detection performance improvements (7–10% mAP gains) on multiple YOLO baselines. Ablation studies isolating the effect of attention components and sampling strategies.\n\n4. Dataset Contribution: The new Maritime Generation Dataset (MGD) is well-documented, diverse in viewpoints, locations, and conditions, with an explicit plan for release.\n\n## Weaknesses\n\n\n1. Evaluation Scope: The object detection experiments only use YOLO variants; it’s unclear if improvements generalize to other detection architectures. For example, can grounding dino be used as a object detector to evaluate the dataset?\n\n2. Limited Baseline Comparisons in Sampling:\nWhile random sampling is compared to AAS, other active learning sampling strategies (e.g., uncertainty-based, diversity-based) are not evaluated. Also, ATDF is introduced with some mathematical sophistication, but the benefits over simpler scoring are only superficially explored.\n\n3. Assumptions and Generalization:\nThe ATDF method relies on predefined discrete attributes (category, viewpoint, etc.) which the authors admit limits granularity. But there’s no experiment showing how much that matters.\nThe approach is tightly tailored to maritime scenes; it's unclear if BiOW-Attn or AAS generalize to other domains with different context interactions---However, I don't think this point affect the value of this paper as we already claimed we are focusing on the maritime domain.", "was_revised": true, "final_justification": "In the rebuttal the authors provide new experiments result addressing my concern on the Yolo detector and the generalizability of the method.\n\nI also continuously appreciate the strengths of the paper: \n\n1. Clear Motivation & Importance: The maritime domain has real and specific data scarcity issues that make synthetic generation important and meaningful. I like the way how the authors adjust existing techniques in ML community to support the domain-specific applications.\n\n2. Technical Contributions: The BiOW-Attn module is a well-motivated addition, specifically modeling object–water boundary interactions for more realistic generation. The AAS sampling framework integrates active learning principles into generative augmentation, addressing the problem that not all synthetic data is equally useful.\n\n3. Strong Empirical Evaluation: Multiple metrics (FID, CAS, YOLO Score) on generation. Detection performance improvements (7–10% mAP gains) on multiple YOLO baselines. Ablation studies isolating the effect of attention components and sampling strategies.\n\n4. Dataset Contribution: The new Maritime Generation Dataset (MGD) is well-documented, diverse in viewpoints, locations, and conditions, with an explicit plan for release.\n\nTherefore I remains my score and recommend acceptance.", "rebuttal": "We sincerely appreciate Reviewer EyhV's valuable feedback. Our responses to the weaknesses, questions, and limitations are listed below.\n\n**R-W1 Limited to YOLO variants** Our proposed method is indeed applicable to various types of detectors, including open-vocabulary detectors such as Grounding DINO. To demonstrate this generalization, we conducted additional experiments using Grounding DINO as the object detector. As shown in the table below, our proposed data generation method significantly enhances Grounding DINO's detection performance, achieving substantial improvements in mAP and mAP@0.5. This demonstrates that the effectiveness of our generation approach extends beyond common detectors represented by the YOLO variants to open-vocabulary detectors.\n\n| Methods                   | mAP            | mAP@0.5        |\n|---------------------------|----------------|----------------|\n| Grounding DINO (official) | 8.42           | 12.60          |\n| Grounding DINO (fintuned) | 65.03          | 86.12          |\n| + Gen Data                | 68.04 (+4.63%) | 89.86 (+4.34%) |\n\n**R-W2 & Q2 Sampling strategy comparison** Thank you for your insightful comment. To fully demonstrate the advantages of our proposed method, we conducted comprehensive comparisons with 5 different active learning approaches, including uncertainty-based sampling methods (Entropy, Variance, and Margin) and diversity-based sampling methods (Greedy K-Center and K-Means Corset). For diversity-based sampling, we constructed 7-dimensional features using detection box count, average box area, box area standard deviation, average confidence, confidence standard deviation, average box x-coordinate, average box y-coordinate, and category count.\n\n| Sampling Methods | mAP   | mAP@0.5 |\n|------------------|-------|---------|\n| w/o              | 39.99 | 61.13   |\n| Entropy          | 42.62 | 64.40   |\n| Variance         | 42.42 | 64.17   |\n| Margin           | 42.87 | 64.55   |\n| Greedy K-Center  | 42.24 | 63.90   |\n| K-Means Corset   | 42.27 | 63.79   |\n| AAS              | 43.62 | 65.50   |\n\nThe results show that while these traditional methods also improve performance, our proposed AAS method achieves superior results by more comprehensively considering maritime-specific attributes such as water conditions, weather, and viewpoint information, leading to better detection performance.\n\n**R-W3 Assumptions and generalization** We thank the reviewer for the thoughtful comment. While our method is specifically designed for maritime scene generation and detection, certain components may offer insights or inspiration for other domains that face similar challenges. For instance, the generation module could be adapted to tasks requiring fine-grained control over multi-condition interactions, and the AAS sampling strategy may be relevant to object detection problems involving multi-dimensional attribute distributions. We will expand the discussion in the final version to clarify the potential for such cross-domain relevance and the associated limitations.\n\n**R-Q1 BiOW-Attn and water condition** The purpose of BiOW-Attn is not merely to introduce water conditions, but to better capture the interaction relationships between water bodies and water surface objects, thereby improving generation quality. The qualitative visualization results (Figure 10) and quantitative metrics (Table 2) comparing scenarios without water conditions and with simple water condition addition are already provided in our paper. From the experimental analysis, approaches that ignore or simply introduce water conditions tend to produce issues such as objects floating in the air above water and unrealistic water-object interaction boundaries. Our designed BiOW-Attn effectively addresses these problems by modeling the bidirectional attention between objects and water surfaces, leading to improved generation quality.\n\n**R-Q3 ATDF's detector dependence** The purpose of ATDF is to observe the detection performance differences of specified detector across different attribute dimensions, thereby enabling the targeted selection of generated samples that better improve corresponding detector performance. Therefore, a worse initial detector does not degrade the effectiveness of AAS selection. This has been experimentally validated in Table 3 of the main text, where three detectors with different performance levels were used. When fine-tuned with the samples selected based on their respective ATDFs, all detectors showed significant performance improvements, demonstrating the robustness of our AAS strategy across different detector qualities.\n\n**R-Q4 & R-L Broader impact discussion** While our work is intended primarily for maritime safety, monitoring, and research applications, we acknowledge that synthetic image generation capabilities could potentially be misused for creating deceptive maritime surveillance content. We will add a broader impact discussion in the final version that addresses these concerns and emphasizes the importance of responsible deployment of our framework."}, {"review_id": "JZ7yyPG6Zc", "reviewer": "Reviewer_S8Ax", "summary": "This paper studies the problem of using synthetically generated images to improve object detection in a new domain. It proposes a framework named Neptune-X to actively select and balance synthetically generated images for domain specific fine-tuning. To facilitate this, this paper introduces a conditional image generator called X-to-Maritime, building on top of existing Stable Diffusion [Ref 25] framework. Specifically, X-to-Maritime leverage several application-specific design including multi-conditional guidance, layout condition embedder, and bidirectional object-water attribution for improved control and quality. For end-to-end evaluation, the paper applies the Neptune-X to curate a large-scale synthetic dataset, which in turn boosts the YOLO object detection in the Maritime domain.", "questions": "Please address the comments especially [W1.1], [W1.2], [W1.3] and [W2] mentioned in the previous question.", "limitations": "N/A", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n- [S1] This paper addresses a very practical domain adaptation problem. Specifically, it tackles both data sacristy and poor generation in maritime object detection, building on top of a foundational text-to-image diffusion model.\n\n- [S2] While most of the work in the literature focused on generative model design, this paper presents an end-to-end solution to the problem, featured by the attribute-dependent active sampling technique.\n\n**Weaknesses**\n\n- [W1] The current evaluation does not clearly indicate  the specific impact of the proposed X-to-Maritime image synthesis engine on the final object detection performance. While Table 3 demonstrates performance gains when augmenting training data with generated samples, it remains ambiguous how much of this improvement is attributable to the enhanced synthesis capabilities of X-to-Maritime itself, as opposed to the overall data augmentation strategy. \n  - [W1.1] To provide a more comprehensive understanding of the X-to-Maritime engine's contribution, it would be beneficial to include a comparative analysis of YOLO detector performance when retrained on synthetic data generated by existing state-of-the-art text-to-image synthesis methods, such as SD1.5 [Ref 25], RC-L2I [Ref 5], InstDiff [Ref 33], GLIGEN [Ref 16] , and LayoutDiff [Ref 42]. This would clearly highlight the direct impact of X-to-Maritime's generated data quality on object detection improvement. \n  - [W1.2] Furthermore, extending the ablation studies (currently focused on generation quality in Table 4 ) to demonstrate the contribution of different components of the X-to-Maritime generation model (e.g., BiOW-Attn, multi-condition guidance) directly to the object detection improvement metrics (rather than YOLO score from a pre-trained detector) would offer deeper insights into the effectiveness of each generative module for downstream task performance.\n  - [W1.3] To more comprehensively illustrate the impact of the proposed methods, it would be beneficial to present the results as a 2D curve, with mean Average Precision (mAP) on the Y-axis and the number of synthetic samples on the X-axis. This visualization would allow for a clearer understanding of the performance trends as the quantity of generated data increases. Specifically, it would be insightful to determine if adding, for example, 10x the current amount of synthetic data continues to yield improvements in object detection performance. Furthermore, such a curve would help elucidate the sustained importance of both the AAS strategy and the X-to-Maritime model in scenarios with significantly larger synthetic datasets.\n\n- [W2] The paper states that the YOLO score was computed using an object detector pre-trained on synthetic data (L533-L537). This approach appears to be an unfair measurement when evaluating the quality of the synthetic data, as it would make more sense to use other object detectors trained on a larger, independent dataset to provide auxiliary signals.\n\n- [W3] Figure 2 depicts the proposed Embedder taking separate Layout and Water Surface images, suggesting distinct processing streams. Clarification is needed regarding whether the embedder employs a separate auto-encoder for encoding Layout and Water Surface information in the actual implementation. If this is the case, it raises concerns about the generalizability of the approach to other related domains. Such a design might imply a significant reliance on domain-specific expertise or extensive pre-training tailored to maritime scenes, potentially limiting its direct applicability or requiring substantial adaptation for different environments or object types.", "was_revised": true, "final_justification": "Thanks for providing detailed response! Raised my rating from 4 to 5, assuming [W2] & [W3] will be addressed in the final rating.", "rebuttal": "We sincerely appreciate Reviewer S8Ax's valuable feedback. Our responses to the weaknesses and questions are listed below.\n\n**R-W1.1 Extensive comparison of generative models for detection improvement** Thank you for your suggestion. To demonstrate the benefits of our X-to-Maritime image generator in object detection, we conducted comparisons with state-of-the-art methods. The performance improvements of these methods on YOLOv10 detector are shown in the table below. It is clear that our method achieves the best performance in improving maritime detector performance, primarily attributed to the design of the BiOW-Attn module, which significantly enhances the modeling and control capabilities between objects and water bodies, thereby generating more realistic images. We will add further discussion in the final version.\n\n| Methods       | mAP   | mAP@0.5 |\n|---------------|-------|---------|\n| w/o           | 39.99 | 61.13   |\n| LayoutDiff    | 40.03 | 61.01   | \n| GLIGEN        | 41.54 | 62.85   | \n| InstDiff      | 41.32 | 62.57   | \n| RC-L2I        | 41.48 | 63.26   | \n| Neptune-X     | 43.62 | 65.50   | \n\n**R-W1.2 Extensive comparison of different configurations for detection improvement** Thank you for your suggestion. The benefits of each component to object detection improvement are shown in the table below. It is evident that using only simple object conditions can hardly effectively improve performance, mainly because it tends to produce poor generation results (e.g., ships separated from water surface). Simply adding water conditions can further enhance detection performance, but the improvement is still limited. However, introducing the bidirectional attention module for object-water modeling can significantly enhance the realism of synthetic data, thereby better improving downstream detector performance. We will add this experiment in the final version.\n\n| ObjCA | WatCA | Obj2WatCA | Wat2ObjCA | mAP     | mAP@0.5 |\n|-------|-------|-----------|-----------|---------|---------|\n| w/o   | w/o   | w/o       | w/o       | 39.99   | 61.13   |\n| √     |       |           |           | 40.94   | 61.18   |\n| √     | √     |           |           | 41.09   | 61.15   |\n| √     | √     | √         |           | 42.54   | 63.85   |\n| √     | √     |           | √         | 42.75   | 63.93   |\n| √     | √     | √         | √         | 43.62   | 65.50   |\n\n**R-W1.3 Correlation between detection accuracy and number of synthetic samples** Due to the rebuttal requirment constraints, we present the results in tabular format. We conducted data augmentation experiments on YOLOv10 using 5k, 10k, 20k, 30k, and 50k synthetic samples, respectively. The experimental results are shown in the table below.\n\n| Generated Samples | mAP   | mAP@0.5  |\n|-------------------|-------|----------|\n| Baseline (0k)     | 39.99 |  61.13   |\n| 5k                | 43.11 |  64.70   |\n| 10k               | 43.62 |  65.50   |\n| 20k               | 43.63 |  65.70   |\n| 30k               | 43.82 |  65.92   |\n| 50k               | 44.06 |  66.05   |\n\nIt is worth noting that there is a clear saturation effect when the data volume increases from 10k to 20k samples. This phenomenon primarily stems from our proposed AAS method, which pre-selects the most valuable generated samples. The samples ranked lower in the selection process contribute minimally to detection performance improvement since the model has already performed well on similar cases. This active selection strategy enables our method to achieve faster performance improvements with lower computational costs, reducing additional training overhead while maintaining effectiveness. We will include the suggested 2D curve visualization in the final version to more intuitively demonstrate this.\n\n**R-W2 YOLO score** We thank the reviewer for this concern, which may stem from a misunderstanding. In fact, the detector used for YOLO score computation is pre-trained on large-scale datasets and then fine-tuned on our constructed small-scale maritime dataset (which does not contain any synthetic data). The fine-tuning is necessary because our dataset includes objects that are rare in general scenarios but particularly important in maritime contexts, such as buoys. The detector has never used any data generated by generative models during the training, so there is no unfairness issue. We hope this clarification addresses your concern.\n\n**R-W3 Water condition embedding** This concern may stem from a misunderstanding caused by Figure 2. Actually, we use a single embedder to encode both object and water surface conditions simultaneously, rather than employing separate auto-encoders. We will modify Figure 2 in the final version to avoid this unclarity."}, {"review_id": "anSNA0U64C", "reviewer": "Reviewer_tKB5", "summary": "- The authors propose Neptune-X1, a unified framework for maritime object detection that combines diverse scene generation with task-aware data selection.\n\n- Authors introduce a Bidirectional Object-Water Attention (BiOW-Attn) module that enhances realism by modeling object-water interactions under multi-condition inputs. \n\n- To select data samples that are most beneficial for detection performance, authors propose an Attribute-dependent Active Sampling (AAS) strategy that estimates training difficulty across semantic dimensions and selects high-value samples through difficulty-aware weighting.\n\n- Authors also construct a Maritime Generation Dataset (MGD) for maritime detection, by pooling together 11,900 images from various publicly available and newly captured datasets – with corresponding captions, water surface masks, and bounding box annotations. The dataset covers five object categories, three viewpoints, four locations, and six imaging environments.\n\n- Table 2 demonstrate that Neptune-X is better generator compared to baselines, and Table 3 and Fig 5 demonstrate that training on generated samples leads to improved performance across the board.", "questions": "- I wonder if the authors have ablations on ATDF factors to evaluate their individual contribution to the AAS strategy?\n\n- Can the authors provide examples of generation diversity — for instance, using the same layout with different seeds or text prompts, and vice versa? This would help assess how well the model supports diverse outputs.\n\n- Is the model capable of editing existing real images (e.g., inserting new objects into real maritime scenes), and if so, could this be used to further improve detection performance?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- This work proposes a novel technique specifically tailored to improving methods for maritime detection, which represents a valuable contribution to the field.\n\n- The thorough experiments and results clearly demonstrate the effectiveness of both the generator (Neptune-X) and the post-processing components (AAS strategy and ATDF factors).\n\n- The authors have collected and annotated a dedicated dataset, the Maritime Generation Dataset (MGD), which could be beneficial for the broader research community.\n\n- The paper is well-written, includes clear illustrative figures, and provides sufficient details about the experiments and the collected dataset.\n\nWeaknesses:\n- The modeling contribution, particularly the Bidirectional Object-Water Attention (BiOW-Attn) module, while effective, lacks strong novelty.\n\n- The AAS strategy shows clear benefits in the low-data regime (~5K samples), but its marginal gains at 10K suggest diminishing returns. A more detailed explanation for this saturation would be helpful.", "was_revised": true, "final_justification": "I thank the authors for their explanations and detailed experiments on saturating performance and ATDF contributions. I will raise my score in light of this.\n\nIdeally, it would have been nice to verify the generated visuals - but I understand that the conference prevents visuals in rebuttal phase, which is a broader issue.", "rebuttal": "We sincerely appreciate Reviewer tKB5's valuable feedback. Our responses to the weaknesses and questions are listed below.\n\n**R-W1 Effective but not strongly novel** We sincerely thank the reviewer for recognizing the effectiveness of our approach. While we acknowledge that the BiOW-Attn module builds upon existing attention mechanisms, our contributions extend beyond this single component. Our work makes three major contributions: (1) the BiOW-Attn mechanism, which is tailored to capture bidirectional object-water interactions, a critical yet underexplored factor in maritime scenes; (2) the novel AAS strategy that integrates active learning with attribute-aware sampling for synthetic data selection; and (3) the construction of the first benchmark dataset (MGD) specifically designed for maritime scene generation. \n\nMore importantly, this represents the first attempt to systematically apply generative models to the maritime domain—an important but often overlooked application scenario. We hope this pioneering effort in addressing the unique challenges of maritime object detection through synthetic data generation will not be overlooked, rather than focusing solely on the novelty of individual module designs.\n\n**R-W2 Marginal gains of AAS** Thank you for your suggestion, which is similar to the **Reviewer S8Ax's W1.3** question. We conducted more extensive experiments to illustrate this saturation effect. As shown in the table below, there is a clear saturation effect when the data volume increases from 10k to 20k samples. This phenomenon primarily comes from our proposed AAS method, which pre-selects the most valuable generated samples. The samples ranked lower in the selection process contribute minimally to detection performance improvement since the model has already performed well on similar cases. This active selection strategy enables our method to achieve faster performance improvements with lower computational costs, reducing training overhead while maintaining effectiveness. To better illustrate this point, we will add this discussion in the final version and include 2D curve visualization to better demonstrate the relationship between sample quantity increase and detection performance improvement.\n\n| Generated Samples | mAP   | mAP@0.5 |\n|-------------------|-------|---------|\n| Baseline (0k)     | 39.99 |  61.13  |\n| 5k                | 43.11 |  64.70  |\n| 10k               | 43.62 |  65.50  |\n| 20k               | 43.63 |  65.70  |\n| 30k               | 43.82 |  65.92  |\n| 50k               | 44.06 |  66.05  |\n\n**R-Q1 Individual ATDF contribution** The table below demonstrates the individual contribution of each attribute dimension (ATDF) used in our AAS strategy for sample selection. As more attribute dimensions are incorporated, higher-value samples are prioritized, leading to progressive performance improvements. This validates the effectiveness of our AAS method in combining multi-dimensional attributes to select the most valuable synthetic samples for detection enhancement.\n\n| Viewpoint | Location | Imaging Environment | Object Category | mAP   | mAP@0.5 |\n|-----------|----------|---------------------|-----------------|-------|---------|\n| √         |          |                     |                 | 40.26 | 62.09   |\n| √         | √        |                     |                 | 41.27 | 62.26   |\n| √         | √        | √                   |                 | 41.40 | 63.10   |\n| √         | √        | √                   | √               | 43.62 | 65.50   |\n\n**R-Q2 More visualization of generation diversity** Due to the rebuttal policy restrictions, we are unable to provide additional anonymous visualizations during this phase. However, we will include comprehensive visual examples in the final version to illustrate the generation diversity of our model under the same layout with different seeds and text prompts.\n\nWhile these specific cases are not shown in the current version, we kindly refer the reviewer to Figures 8–11 in the appendix. These figures demonstrate the model’s ability to generate visually diverse maritime scenes without noticeable repetition in ship appearance, water texture, or overall scene composition, highlighting the model’s capacity for variation.\n\n**R-Q3 Image editing ability** Our model supports scene-level editing by modifying the input layout, which enables the generation of diverse maritime scenes. While direct editing of real images (e.g., inserting objects into real maritime scenes) is not the focus of our work, our generator, like other diffusion models, can be used in conjunction with existing image editing techniques to perform such tasks.\n\nDue to rebuttal policy restrictions, we cannot include additional visualizations at this stage, but we will provide relevant examples and discussion in the final version."}, {"review_id": "HFDP6V3waC", "reviewer": "Reviewer_1w7F", "summary": "This paper proposes Neptune-X, a data-centric framework that improves maritime object detection by combining synthetic data generation with task-aware sample selection. It introduces X-to-Maritime, a multi-modality generative model with a Bidirectional Object-Water Attention module for realistic scene synthesis, and Attribute-correlated Active Sampling to enhance training efficiency. The authors also present the Maritime Generation Dataset to support benchmarking. Experiments show significant improvements in both image synthesis and detection performance.", "questions": "NA", "limitations": "One potential limitation that is not discussed in the paper is the dual-use nature of the proposed techniques. Although the work is positioned for maritime safety and monitoring, the enhanced object detection and scene synthesis capabilities could also be adapted for military surveillance or defense-related applications. It would be helpful if the authors could briefly acknowledge this potential and clarify the intended scope of deployment.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1.\tLeveraging image generation for data augmentation is a valuable and promising approach to expand the training set for maritime object detection.\n\n2.\tThe paper is well-written and easy to follow.\n\n3.\tThe experiments demonstrate high-quality image synthesis and substantial improvements in detection performance.\n\nWeaknesses\n\n1.\tThe idea of using image generation for data augmentation is not novel for maritime detection [C] and other domain-specific detection tasks. Prior works such as DriveDreamer [A] and Panacea [B] in autonomous driving have explored similar approaches and evaluated their effectiveness for improving training utility.\n\n2.\tThe technical contributions are highly domain-specific, and some components—such as the AAS—appear hand-crafted. While these designs are effective within the maritime context, they lack generality and may offer limited methodological insights for the broader machine learning community. As a result, the work may be better suited to a domain-focused venue rather than a general-purpose conference like NeurIPS.\n\n[A] Wang, Xiaofeng, et al. \"DriveDreamer: Towards Real-World-Drive World Models for Autonomous Driving.\" ECCV 2024\n\n[B] Wen, Yuqing, et al. \"Panacea: Panoramic and controllable video generation for autonomous driving.\" CVPR. 2024.\n\n[C] Tang, Datao, et al. \"AeroGen: Enhancing remote sensing object detection with diffusion-driven data generation.\" CVPR. 2025.", "was_revised": true, "final_justification": "The rebuttal offers reasonable points about the suitability of this manuscript for NeurIPS. Since I am not an expert in this specific domain, I am no longer inclined to reject the paper and instead support the evaluations of the other reviewers.", "rebuttal": "We sincerely appreciate Reviewer 1w7F's valuable feedback. Our responses to the weaknesses, questions, and limitations are listed below.\n\n**R-W1 Data augmentation explored before for maritime detection** We respectfully disagree with this comment. To our knowledge, maritime scene generation for data augmentation has not been explored in any prior work. While image generation has been applied in other domains such as autonomous driving (the suggested [A, B]) and aerial imagery (the suggested [C]), these approaches are fundamentally incompatible with the maritime setting.\n\nThe cited methods assume structured environments with stable, predictable spatial layouts and well-defined object categories. In contrast, maritime scenes are inherently unstructured, with highly variable sea states, ambiguous object boundaries, and complex object-water interactions that lack reliable contextual anchors. These unique challenges render existing approaches ineffective when applied to this domain.\n\nMoreover, our empirical results in Table 2 and Figure 4 clearly demonstrate that general or cross-domain augmentation strategies fail to produce meaningful performance gains in maritime detection. This reinforces the need for a domain-specific generation framework, which our work is the first to provide. Our approach directly addresses the critical aspects of maritime imagery that existing methods overlook, establishing a new and necessary direction for data-driven learning in this field.\n\n**R-W2 Domain-specificity** We respectfully disagree with this comment. The domain-specific nature of our work should be seen as a strength, not a limitation. Addressing complex real-world problems, such as maritime object detection, requires deep domain expertise, problem-specific modeling, and careful attention to context-specific challenges. These qualities reflect the type of thoughtful, impactful work that NeurIPS has a long history of supporting.\n\nIt is a misconception to view NeurIPS as a venue limited to general-purpose algorithms. In reality, a significant portion of NeurIPS papers are domain-specific, leveraging machine learning to advance understanding in specialized areas. For example, NeurIPS regularly publishes work on medical images (e.g., tumor segmentation, radiology interpretation), astronomical imaging (e.g., telescope-based object detection), biological microscopy, remote sensing, and robotic perception. These works often include components tailored to their domains, because the structure and semantics of the data vary substantially across application areas.\n\nOur work is in this tradition. Maritime images differ drastically from more commonly studied domains. They are visually ambiguous, lack structured background cues, and involve dynamic interactions between objects and water under varied environmental conditions. Developing effective solutions in this space necessarily requires specialized design choices, and our method was built to address those specific challenges directly.\n\nIn this sense, domain-specificity should not be equated with limited relevance. Rather, it reflects a deliberate effort to make machine learning effective in contexts where naive application of general tools would fail. We believe this aligns with the inclusive and applied spirit of NeurIPS.\n\n**R-L Dual-use nature** We appreciate the reviewer's concern about the dual-use potential of our technology. Our proposed model is specifically designed for intelligent navigation and smart maritime applications, similar to autonomous driving and smart city scenarios, and is not intended for military purposes. \n\nNonetheless, we recognize the importance of addressing responsible deployment. In the final version of the paper, we will include a statement clarifying the intended scope of use and reinforcing our commitment to ethical and non-military applications to help mitigate risks of unintended misuse."}]

Author rebuttal:

Meta-review: The paper received four expert reviews. The authors provided a rebuttal that attempted to address the concerns raised in the reviews. The reviewers read the rebuttal and engaged with the authors.  The reviewers unanimously like the paper and recommended accept. The area chair agreed with the recommendation and decided to accept the paper. Congratulations! Please see the reviews for feedback on the paper to revise the final version of your paper and include any items promised in your rebuttal.
Decision: Accept (spotlight)