S3OD: Towards Generalizable Salient Object Detection with Synthetic Data
Authors: Orest Kupyn
Hirokatsu Kataoka
Christian Rupprecht
Venue: ICLR 2026 Poster (2026)
Keywords: salient object detection
sod
segmentation
diffusion models
image generation
synthetic data
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: generalizing salient object detection with synthetic data and ambiguity-aware architecture

Abstract: Salient object detection exemplifies data-bounded tasks where expensive pixel-precise annotations force separate model training for related subtasks like DIS and HR-SOD. We present a method that dramatically improves generalization through large-scale synthetic data generation and ambiguity-aware architecture. We introduce S3OD, a dataset of over 139,000 high-resolution images created through our multi-modal diffusion pipeline that extracts labels from diffusion and DINO-v3 features. The iterative generation framework prioritizes challenging categories based on model performance. We propose a streamlined multi-mask decoder that handles the inherent ambiguity in salient object detection by predicting multiple valid interpretations. Models trained only on synthetic data achieve 20-50% error reduction in cross-dataset generalization, while fine-tuned versions reach state-of-the-art performance across DIS and HR-SOD benchmarks.

Published as a conference paper at ICLR 2026
S30D: TOWARDS GENERALIZABLE SALIENT OBJECT DETECTION WITH SYNTHETIC DATA
Orest Kupyn', Hirokatsu Kataoka",,, Christian Rupprecht 'University of Oxford, VGG?AIST
httpe://ssaoprojjet..iiitt
ABSTRACT
Salient object detection exemplifies data-bounded tasks where expensive pixel- precise annotations force separate model training for related subtasks like DIS and HR-SOD. We present method that dramatically improves generalization through large-scale synthetic data generation and ambiguity-awar architecture. We introduce S3OD, dataset of over 139,000 high-resolution images created through our multi-modal diffusion pipeline that extracts labels from diffusion and DINO-v3 features. The iterative generation framework prioritizes challenging categories based model performance. We propose streamlined multi-mask decoder that handles the inherent ambiguity in salient object detection by predicting multiple valid interpretations. Models trained only on synthetic data achieve 20- 50% error reduction in cross-dataset generalization, while fine-tuned versions reach state-of-the-an performance across DIS and HR-SOD benchmarks.
Synthelic Dataset
Model Predictions
Figure I: $30D Top: Our large-scale synthetic dataset, consisting of diverse complex scenes and high-quality samples. Bottom: Model Predictions. Our model trained on synthetic data generalizes well to real-world images, handling ambiguous scenes by predicting alternative hypotheses.
INTRODUCTION
Salient object detection (SOD) is a fundamental problem in computer vision with applications spanning AR/VR (Tian et al., 2022), robotics (Chan & Rick 2020). 3D reconstruction (Liu et al.) 2021a), and image editing (Goferman et al. 2011). Recently, two specialized subtasks have emerged: dichotomous image segmentation (DIS), which focuses highly accurate boundaries, and high- resolution SOD (HR-SOD) for 2K-8K resolution images, both of which present new generalization challenges. SOD exemplifies tasks that fundamentally limited by the availability of labeled data. Creating diverse, representative datasets is challenging, requiring extensive coverage of real-world scenarios and object types. The labeling process requires pixel-precise manual annotations, which can take up to 10 hours per sample (Qin et al. 2022). Moreover, annotations often exhibit inherent ambiguities and inconsistencies across datasets, as annotators interpret scene saliency differently, posing a fundamental challenge for deterministic approaches. These constraints yield relatively small datasets (Qin et al. 2022 Zeng et al.) 2019) that cannot capture the complexity of real-world systems. Even large-scale datasets like SA-IB (Ravi et al. (0244 struggle with the high-resolution pixel-perfect data (Ke et al. 2023). Current approaches train separate models for DIS and HR-SOD due to small datasets and domain gaps, leading to ssecifcc overfitting rather than generalizable principles. Recent architectural innovations (Yu et al.) (024:: Zheng et al.) 2024 Kim et al.) 2022) have achieved incremental improvements but have not addressed cross-domain generalization. The

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fundamental bottleneck remains data scarcity, not model complexity, whereas models typically enforce deterministic predictions, thereby ignoring ambiguity. Synthetic data offers an attractive solution, but existing approaches have critical limitations. Traditional pseudo-labeling setups are limited by the teacher's capabilities and often rely on the same vision encoders, thereby imposing performance ceilings. Methods that extend diffusion models predict masks directly (Wu et al.. 2023a) suffer from consistency issues arising from noisy diffusion features. In contrast, mask- conditioned generation (Qian al. (024) struggles with diversity as obtaining large mask libraries and generating complex scenes remain challenging.
In this work, we aim to unify DIS and HR-SOD by addressing two main limitations of prior work. We refer to the unified task high-fidelity salient segmentation. To this end, we introduce: 1) a multi-modal data generation pipeline that leverages the generative power of diffusion models, eliminating teacher bottlenecks, 2) ambiguity-aware architecture handling multiple interpretations, and 3) an iterative generation framework adapting to model weaknesses. Our main contributions are:
Multi-Modal Dataset Diffusion Pipeline: Our diffusion pipeline simultaneously generates images and masks by extracting FLUX DiT feature maps, concept attention maps, and DINO-v3 (Siméoni) ett al.) 2025) representations during the generation process. The generation pipeline leverages rich spatial understanding encoded during generation, along with robust semantic features from discriminative models, to decode high-quality masks. This ensures strong image-label alignment, enabling a flexible framework applicable to other dense prediction tasks.
Iterative Generation Framework: We introduce beedback-driver synthetic data generation that dynamically identifies model weaknesses, continuously adapting sampling distribution to prioritize challenging categories. Unlike traditional static methods, this iterative approach enables continuous improvement as datasets grow.
Using our pipeline, we generate 139,000+ high-resolution images with pixel-wise annotations, over 2x more than all existing SOD datasets combined. This enables up to a 50% reduction in error across benchmarks when evaluated for cross-dataset generalization. Models trained solely on synthetic data achieve strong cross-dataset generalization without real training data, whereas fine-tuned versions reach state-of-the-ee performance on the DIS and HR-SOD benchmarks.
Large-Scale Synthetic Dataset:
Ambiguity-Aware Architecture: We directly address SOD's inherent ambiguity through multi- mask decoder allowing multiple valid interpretations while enabling a simpler architecture compared to current state-of-the- methods. We employ the DINO-v3 backbone, which leverages enhanced visual representations to improve generalization.
RELATED WORK
Salient Object Detection:
SOD has evolved from handcrafted features (Borji et al. 2015) to complex multi-view transformer architectures et.. 2024). BASNet (Qin et 2019) introduced boundary- aware refinement with hybrid loss functions for precise object segmentation, while subsequent work
(Zhao et al., 2019 Wei et al. 2020b Wu al. 2019b Feng et al. 2019) explored efficient edge- refinement strategies. U--Net (Qin et al. 2020) developed a nested UNet architecture to capture multi-scale contextual information. CPD (Wu et al. 2019a) introduced cascaded decoders that directly refine features using generated saliency maps. PFANet (Zhang et al.) (2018) and PAGENet (Wang et al. (2019) leveraged pyramid attention networks to enhance segmentation quality. However, these approaches remain constrained by limitations of the training dataset and struggle in high- resolution inference scenarios. Recently, HR-SOD and DIS emerged as specialized subtasks focused on high-resolution, accurate segmentation. IS-Net QQnn al. 2022) established the DIS baseline using intermediate supervision with feature-level and mask-level guidance. Newer approaches incorporated transformer backbones (Liu et al. 2021b) to enhance feature extraction. InSPyReNet (Kim et al. 2022) adopted an image pyramid architecture for HR-SOD, whereas BiRefNet (Zheng al. (0044) introduced bilateral reference frameworks to capture intricate details. MVANet (Yu et al. 2024) recently proposed multi-view aggregation to detect finer details while improving efficiency, Nevertheless, these methods produce a single deterministic output and are constrained by limited training data. Our approach addresses both limitations while simplifying the architecture.
Synthetic Data Generation: Diffusion models have transformed data generation by enabling high- quality, diverse synthetic datasets. Recent work (Shipard et al. 2023 (aaryildız al. 2023 Tian
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et al. 2023 Azizi et al. 2023 Fan et al. 2024 improved classification model performance by generating synthetic data with latent diffusion models (Rombach et al. (022), though this approach limited to image classification. DiffuMask (Wu al.) 2023b), Attn2mask (Yoshihashi et al.) 2024), and DatasetDM (Wu et al. 2023a) utilize diffusion models to generate synthetie images with annotations for segmentation tasks. However, DatasetIDM's attention-based extraction yields noisy, incomplete masks with imprecise boundaries and struggles with complex multi-object scenes. OVDiff (Karazija al. (024) synthesises support image sets for arbitrary textual categories, while Instance Augmentation (Kupyn & Rupprecht, 2024) provides augmentation frameworks but only slightly expands original distributions. VGGHeads (Kupyn et al., 2024) demonstrated the impact of synthetic data on generalization for 3D head modeling, but remains bounded by external teacher models. For SOD specifically, SODGAN (Wu et al. 2022) employs GANs but struggles with complex scenes due to limited variability in training data. MaskFactory (Qian et al. 2024) conditions image generation on edited masks, but is limited to producing only slight variations of the training set. Unlike approaches that rely noisy attention extraction, mask conditioning,
external teacher models, method extracts supervision from multiple complementary sources within the generative process itself. By combining DINO-v3 visual features (Siméoni et al., (2025), diffusion transformer activations, and concept attention maps (Helbling et al. (2025), we obtain robust supervision with strong image-mask alignment while eliminating performance bottlenecks.
MODEL
Most recent SOD methods focus on improving performance by incorporating complex architectural components. such as multi-view feature fusion (Yu et al. 2024) or iterative refinement modules (Zheng et al. 2024). In contrast, we propose a lightweight architecture that addresses SOD ambiguity through a multi-mask decoder while significantly simplifying other components.
3.1 MODEL ARCHITECTURE
We build our model upon the Dense Prediction Transformer (DPT) (Ranitl et al. 2021 architecture,
which processes input images through transformer (Vaswani et al. 2017) stages followed by multi- scale feature reassembly. DPT transforms the input into patch-token sequences, processes them through transformer layers, and then reshapes them into multi-scale image-like representations. These features are progressively fused and upsampled through residual convolutional blocks (He et al. (0066) to produce final predictions. We adopt this efficient hierarchical design as our backbone. We initialize the DPT encoder with DINO-v3 weights to improve generalization, leveraging visual representations from large-scale superviedd training. The full architecture shown in Figure 2
We formulate the problem as function f -> M mapping from images I C Hxx33 to binary masks M {0, #) W of spatial resolution H x W. Many training annotations are ambiguous: multiple objects may be present, with unclear saliency interpretations. Singlc-output models often average all possible predictions, leading to low-confidence regions.
To address this, we design the final mask prediction head to output multiple masks (m1,.... m,) w) Predicted masks are soft m, E 1) to model pixel-wise confidence. For each training image 1 E I. only one ground truth annotation y E .M is available. Inspired by multiple-choice learning (Guzman- Rivera et al. 2012), during training, the main loss applies to the best prediction 8" arg min, (oU(m,, 1/) chosen via loU score between predicted and ground truth masks.
To prevent unused branches from degrading, we employ relaxed assignment (Rupprecht et al. 2017 where loss is computed across all branches with decaying weight: L = C.- + Xe Σ; C., where controls initial auxiliary branch weight, is decay rate, t is current epoch. Individual losses L, L(m,, y) are described next. For test-time selection, the model estimates IoU scores ($1,...,SN) ) for every prediction. This is supervised by actual IoU scores between prediction and ground truth during training, and this estimate is used to select the highest-scoring mask during testing.
3.2 OBJECTIVE FUNCTION
Following standard semantic segmentation practice, employ multi-component loss combining pixel-wise and region-wise supervision. The total loss L consists of two main components: Focal Loss (Lin et al., 2017) Liocal for class imbalance and IoU Loss LioU for region-level accuracy.
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DINO-13
Fusion
Muti Mask Head
Transfomeer
Reassemble:5: S4
Fusion
Conv2d
Conv2d
Tokenize
Transtomerr
Reassemble:5t Sg
Upsample
Flallen
Fusion
Convad
MIPP
Transformer
Resssemble:5gg
Interpolite
Fusion
Transtormer
Reassembie::Sar
Mask Candidates
Predicted loU
Figure 2: S3ODNet Architecture. Model extends DPT (Ranftl et al. 2021) to predict multiple mask candidates and vector of loUs with the ground truth, employing DINO-v3 the backhone. During training, the loss is propagated through the branch with the highest predicted IoU.
Focal Loss. To address foreground- foreground-background imbalance, we implement focal loss, widely used in dense prediction:
HxW
Lincal = Σ
(1 (1-m (1-m(())) log(ms(p))
p=1
where iterates over pixels indexing predicted mask (p) and ground truth v(p). and = 2 is the focusing parameter.
IoU Loss. To capture region-level accuracy, incorporate IoU loss, measuring overlap between predicted and ground truth masks:
EØe
Hxw mz(p)y(p)
Liot (ms)
Σpsr MxW (m,(p) u(p) m.(p)(())
The overall mask loss combines both components:
Linask (m):) ) = Stcak + Ciou(m;:)) v)
where Amask = 10 balances the losses.
IoU Score Loss. To enable optimal mask selection at inference, supervise predicted IoU scores 8; using mean squared error between predicted and actual IoU values:
Laccrre (8:) (s; ToU(m;;
Finally, the overall training objective comprises the mask loss of best prediction, score loss for all predictions, and decaying regularizer across predicted masks:
Laassi m,
Ascore (s:) + Arege Lmask (m:))
where Ascare 0.05, Areg = 0.1 weigh the losses, 7 = 0.2 is decay rate, /. is current epoch, and N is the number of prediction branches.
4 DATASET
Unlike other dense prediction tasks, scaling SOD datasets poses unique challenges that cannot be addressed by simply leveraging existing collections such as LAION (Schuhmann et al. (022). SOD requires samples with distinct foreground objects, and annotation demands significant expertise and attention detail, particularly for high-resolution images with precise boundary requirements. These constraints make traditional manual dataset curation both impractical and cost-inefffiieet. Our goal is generate large-scale synthetic data that accurately reflects real-world distributions.
4.1 MULTI-MODAL DATASET DIFFUSION
Large-scale diffusion transformers, such FLUX (Labs 2023), with 12B parameters encode rich semantic and spatial representations during the generation process. Rather than ignoring these latent representations and relying teacher models that predict masks directly from generated images, we extend the diffusion model to output masks by combining multiple complementary modalities. We extract latent feature maps that encode spatial layout understanding. concept-attentioon maps
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Update Category Weights:
10,
[c wi
Dataset Diffusion
S30D
Per Category Evaluation
Categories Set
Prompt Generator
Inputs
Generate Diverse Prompts: Exproity Varying Number of objacts, Ocolusions, Sizee, Background, ilumination
Image
Category Name:
DINO-V3
Number of Samples: M
Intermediate Footures
DPT Decoder
Mask
Figure 3: Iterative Generation Pipeline. The LLM (Achiam al. 2023 generates library of diverse prompts for a large set of object categories. These prompts guide diffusion model to generate synthetic images with corresponding masks. The resulting dataset is used to train the SOD model, which is evaluated sategory-wise. Performance feedback from a trained SOD model dynamically adjusts the category weights ti; prioritizing challenging cases in subsequent iterations.
that provide interpretable semantic localization, and DINO-v3 features from the decoded images that capture fine-grained visual semantics. This multi-modal supervision mitigates data scarcity and ensures alignment between generated images and their corresponding masks.
DiT Feature Maps. FLUX DiT employs hybrid architecture with 19 dual-stream transformer blocks (processing text and image tokens separately) and 38 single-stream blocks (operating on con- catenated sequences). We extract feature maps from four single-stream transformer blocks at layers (4, 16, 27, 36), encoding multi-scale spatial representations across generation stages. Each block outputs features IR Rx (x(+.++)))) 3072 where LT = 512. We extract only image tokens, I Bx L, x3072 and project them to 768 dimensions learned projections. These features encode the model's internal spatial understanding used during generation.
Concept Attention Maps. Common dataset generation methods (Wu et al. 2023a extract mean attention maps across all text tokens, producing semantically ambiguous supervision. Instead, use static set of concepts to obtain interpretable, consistent maps. Following the concept-attention framework (Helbling et al. 2025), for each generated image, we compute attention maps between image patches and static concept tokens. For concept token and image patch æ, we compute:
Aconcepe (x, y) = softmax(o..
where og and Oe are attention output vectors from the sample, we extract two concept attention maps using the primary object category (e.g., "dog") and the "background" token, yielding interpretable maps {Aobject; Abackground) that consistently encode object locations and background regions.
transformer layers. For each
DINO-v3 Visual Features. We extract semantic visual features from generated images using DINO- v3 (VYT-L), providing rich object-level representations that capture fine-grained visual semantics through self- self-supervised learning trained on large-scale real-world data.
The three modalities are fused through a dedicated module that projects each to a common 256- dimensional space via separate convolutional branches with batch normalization. FLUX features and concept maps are upsampled to match DINO-v3 resolution using bilinear interpolation. The projected features are concatenated channel-ve -wise and processed through a two-stage convolutional network (3 x 3 followed by x convolution), with the result residually combined with the original DINO-v3 features to produce unified moolt-l representations. We feed this combined representation into the DPT decoder, supervising it with DIS-5K, HR-SOD, UHRSOD, and DUTS datasets, ensuring the model learn how to decode multiple sources into ene-gnaieed segmentation mask.
4.2 ITERATIVE DATA SYNTHESIS
To incorporate feedback mechanism into the data generation, we introduce an iterative process that adjusts generation parameters based on the downstream model's performance for subsequent rounds. After training the model on synthetic data D(r), we evaluate its performance on held-out test set for x< x(T,), which is the average IoU score across
each category G. For each image I,, we compute
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Table SOD Datasets Statistics: S3OD dataset is orders of magnitudes larger than existing datasets and contains a wide variety of scenes and objects.
Metric
DUTS
ECSSD
HKU-IS
DUT-OMRON
UHRSD
HRSOD
DIS-5K
S3OD (ours)
Images
15,570
1,000
4.447
5,168
5,920 948
2,010
5,000
139,981
Unique Objects
1152
310
551
744
381
758
1676
ed
Figure 4: S30D Dataset: The dataset consists of diverse object categories and complex scenes that closely reflect real-world environments, featuring various lighting conditions, spatial compositions, and object interactions. All samples are generated with multi-mode dataset diffusion.
various image transformations (flipingg etc.). x(lj) is high if the prediction is consistent across augmentations. We then compute a mean category score R; by averaging these scores across all
images in category C;. The category weights w/ (((+1)) for the next iteration updated proportionally to the inverse of these scores, ensuring categories with lower performance receive more samples in subsequent generations. Specifically, we map the category scores through a non-linear scaling function: w. (r-1) = Wmin + e
where = 8 and B = 0.5 control the strength of the performance-based skew, Wmin = 1 is minimum weight per class, and Wnew = 4 is the maximal [c]
cj
possible over-weighting. This scales up the weights of categories with scores below given threshold while maintaining minimum weight for well-performing categories. This adaptive sampling strategy ensures that the synthetic data generation process continuously evolves, producing examples that maximize model improvement. The pipeline is visualized in Figure
4.3 MULTI-STAGE QUALITY FILTERING
While synthetic data generation offers scalability, it inevitably produces imperfect samples that can degrade training quality. To ensure high-quality datasets, we implement a comprehensive multi-stage filtering pipeline that addresses standard failure modes in synthetic data generation.
Consistency Filtering. We evaluate prediction consistency using a separate large model trained without FLUX features. For each sample, we compute the loU between the original prediction and the horizontally flipped prediction, filtering samples with an IoU below the T = 0.8 consistency threshold. Low consistency scores often indicate overly ambiguous samples where even robust models struggle to maintain coherent predictions, suggesting fundamental issues with the generated image-mask pairs.
evaluate mask quality, identifying severe artifacts such as fragmentation, noise, or artifacts that commonly occur image segmentation. Only masks with cohesive white regions (s. main components) pass this stage, ensuring clean supervision signals for model training.
Mask Quality Assessment. We employ a Gemma-3 VL.M (Team al. 2025)
Semantic Validation. a second pass, the Gemma VLM evaluates semantic correctness by analyzing the original image and the mask overlay. This stage ensures both the presence of clear, salient objects and adequate mask coverage (> 70% of the main object), thereby filtering out samples in which the multi-modal supervision fails capture the intended semantic content.
This multi-stage approach removes 6.8% of the generated samples, significantly improving dataset quality while preserving the scale advantages of manual annotation.
4.4 IMPLEMENTATION DETAILS
We generate 139,90 139,981 high-resolution data samples Figure 4 three rounds (Rt = 3) which is 131 % more than 11 most common academic benchmarks combined. We sample category names from the ImageNet taxonomy, which covers wide range of objects and activities Table The first round generates 100 images per category, followed by a second and third rounds with an additional 25,000 images in each, prioritizing challenging categories. During processing, 6.8% of the samples

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filtered out. The images are generated with FLUX model with 25 inference steps. For each image, we randomly sample an aspect ratio from a fixed set of common image resolutions, further expanding the dataset variety. All student models are trained using the ViT-B backbone (Dosovitskiy et al. 2020). Model training on S30D dataset takes days on 8 H200 GPUs.
5
EXPERIMENTAL EVALUATION
We evaluate dataset and model generalization, as well as performance various benchmarks,
5.1
EVALUATION PROTOCOL
The performance of the salient object detection models evaluated on six datasets of two domains. For dichotomous image segmentation (DIS), we use DIS-5K (Qin et al. 2022), which contains 5,470 high-resolution images with extremely fine-grained labels for camouflaged, salient, and meticulous objects across varied backgrounds. For Salient Object Detection (HR-SOD), we evaluate on three high-resolution benchmarks: UHRSD (Xie et al.) 2022b) (5,920 images at 4K-8K resolution), HRSOD-TE (Zeng et al. 2019) (400 test images with shortest edge >
1200 pixels), and DAVIS-S (92 images from DAVIS (Pont-Tuset et al.) 2017) video segmentation dataset). We also include two low-resolution benchmarks: DUT-OMRON Yang et al.) 2013) (5,168 images with complex backgrounds) and DUTSS-TE (Wang et al. 2017) (5,019 test images from the largest available SOD) dataset). All datasets include pixel-wise ground-truth annotations for quantitative evaluation.
Metries. We evaluate each model with commonly used metrics: maximum F-measure (Fimav)
(Achanta al. 2009), Mean Average Error (MAE) (Perazzi et al. 2012), structure measure (Sa) (Fan et al. 2017) and enhanced alignment measure (E (Fan et al. 2018 The F-measure (Fş)
balances precision and recall, computed with 5² = 0.3 to emphasize precision. MAE computes the average absolute difference between the predicted and ground- truuh masks. The structure measure S. evaluates preservation of object-aware (S,) and region-aware (S,) structural similarities, computed as 5m a * S. + (1 at) * S, with a 0.5. The enhanced alignment measure E combines local and global similarity information, jointly capturing image-leve statistics and pixel-level matching.
Table 2: Cross Dataset Generalization: S3ODNet trained on synthetic data only demonstrates superior generalization across all datasets comparing to other methods trained subtasks datasets. SOD datasets stand for (HRSOD-TR (Zeng et al. 2019), UHRSD-TR (Xie et al. 2022b) and DUTS- TR (Wang et al. 2017). Best and second best results highlighted.
Method
Data
F., S. 7 EG t
DIS-I
MAE.
F..
S.
DIS-2
E%
MAEL
Fr.
DIS-3 S. E5: MAEL
F.. S. 7 ES
DIS-4
MAE]
Overall F., S. f E2 MAE]
InSpyroNet
DUTS
786
822
857
.64
828
845
.838
864
065
RiRerNet
.355
856
861
055
S3ODNet
SOD
850
585
902
.H66
880
.800
9144
013
888
.855
928
0/10
823
.881
.069
863
856
906
D19
S30DNet
S30D
865
2844
.917
.034
.916
3988
.933
.032
.901
895
938
.033
361
357
.933
.054
3811
3544
.95
.099
DAVIS-S
HRSOD-TE
UHRSOD-TE
DUTS-TTE
DUT-OMRON
Fi. f: S. 1 ES, MAE]
F..
5. ES, MAE.
P... 1 S.. 153 MAE.
F., 1 S.. E55 MAR.
F., 1 S. 1 E MARL
IaSpyreNet
DIS
.921
.937
966
.015
.891
.912
.923
.038
914
.922
.932
.133
845
330
.895
/446
.213
.001
.812
.071
MiReiNet
DIS
.961
.014
.887
.915
.926
.031
922
.937
866
006
.835
064
DIS
.119
S30DNet
DIS
1973
.10
315
952
830
946
927
.447
926
S30DNet
S30D
970
.967
.988
.005
954
.955
.972
.016
954
.944
.961
.023
937
938
.962
.020
.60
.387
.911
.040
5.2
GENERALIZATION
We argue that the most important aspect of modern salient object segmentation models should be generalizing to new image distributions. We evaluate cross-task generalization by training the model on the DIS-5K (Qin et al. 2022) dataset and evaluating on SOD benchmarks, and vice versa. A robust, generalizable method is expected perform well across all benchmark datasets, since they all focus on the same high-level problem. The results are presented in Table 2 S3OD trained on a combination of SOD datasets achieves superior generalization compared BiRefNet (Zheng et al.) 2024), or InSpyreNet (Kim et al. 2022).
Remarkably, even training solely on synthetic data enables the method to achieve state-of-the-ee generalization, reducing the MAE compared to the model trained on DIS-5K 5K by 50.0%, 46.7 %. 20.7%, 1.. and 34.4%. The models trained on DIS-5K only (3000 images) and evaluated on SOD benchmarks all achieve comparable results, demonstrating the importance of data scale and
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Method
Data
Fix 1 Saa 7 E3, 1 MAEL
DAVISSS
Fin
S. E3 MAEL
HRSODSTE
Fw. 7 S. 1 EM MAE.
UHRSOD-TE
Fi.
S. 1 E% MAE
DUTS-TE
Fr. 1 S. 1 ES: MAEL
DUT-OMRON
MVANet
DIS
.907
.929
.959
.016
.902
-919
.035
922
.926
.41
.052
.852
.877
.993
.42
.711
.792
.838
.072
MVANet
S30D
.975
.008
.950
.948
.942
.024
.893
.776
.873
RiRefNer
DIS
919
-936
1961
1014
.877
91.
996
.031
922
924
937
033
860
8966
.110
036
744
X199
835
DS4
BiRefNer
S30D
9663
-958
.978
.M19
955
.951
.665
.019
.955
9499
.962
.022
.928
9331
.951
.024
.45
.882
.899
JMS5
S30DNed
DIS
.973
010
923
947
901
S30DNei
S3OD
.970
.967
1988
1005
954
995
.922
.016
.954
9944
.911
.023
.97
1938
9962
.020
560
.887
.911
.40
Table 3: Impact of S3OD dataset on salient object detection performance across different methods. Training on S3OD improves generalization across all methods.
the impact of overfitting on subtask-speciffc performance. Still, S3OD trained on synthetic data demonstrates strong generalization across all benchmarks.
To further validate the impact of S3OD dataset, retrained BiReftNet (Zheng et al. 2024) and MVANet (Yu et al., 2024) on our synthetic data. Results are reported in Table 3 and are consistent with other evaluations. Training on S3OD improves the generalization of all models, and S3ODNet still outperforms other methods trained in the same setup.
5.3 STATE-OF-THE-ART COMPARISON
Table 4: Quantitative comparison DIS5K and SOD benchmarks. Best results highlighted in bold. The S3ODNet* are the metrics computed with the best match over three predicted masks.
Method
DIS-1
DIS-2
DIS-3
DIS-4
Overall
Fi. T S..
B
MAE]
Fm.
S.,
AM
MAE.
F..
S..
7
MAEL
F..
S.. 1 5 1
MAE]
Fi. S. 1 B3 MAEL
SAM-HQ
.977
-907
.943
.019
889
8833
.928
.129
.851
851
X997
045
.763
7791
.84.
088
.55
8661
5913
(445
InSpyreNet
873
894
9005
.196
911
.940
0134
900
.905
-900
BiReINe
860
885
.911
037
.894
9000
.930
036
925
.919
955
028
.901
-000
.939
.039
.896
90]
-944
035
MVANet
862
880
.906
0039
.909
9112
.942
032
.944
.918
.954
.030
.907
.955
.966
.039
.900
-04
.937
.035
330DNer
902
.923
.921
.953
822
.930
.920
.660
.025
.909
9002
.954
.034
.914
.911
.029
S3ODNe
924
.188
936
016
941
931
975
018
9977
924
Method
DAVIS-S
HRSOD-TE
UHRSD-TE TE
DUTS-TE
DUT-OMRON
Fin f. 8. 7 Egg MAE]
Fin
S. 85 MAEL
Fi. S., 7 E52 MAEL
Fr, S. 1 1 1 MMEL]
Fi. 8. 1 E53 MAEL
InSpyroNet
977
.973
.987
.007
9956
1956
.962
.188
.957
.953
.665
.020
.932
.936
.956
004
823
.872
.906
.446
BiRerNer
.975
966
-969
S3ODNet
.979
9944
.993
.084
.963
996
.978
.03
964
.952
.969
.018
954
.949
.972
.015
.879
.898
.924
.032
S3ODNer
982
9777
.993
.004
.779
973
1991
.005
.977
966
995
.088
963
.999
987
.008
9007
919
.933
023
Prior work does not evaluate cross-task generalization and trains task/benchmark-specinn While we argue that the evaluation above is the way forward for salient object segmentation, we also evaluate in the historically used setting. We finetune the model trained our S3OD dataset on both the DIS-5K (Qin et al. 2002)) and a combination of SOD datasets (HR-SOD (Zeng et al., 2019), UHRSOD (Xie et al. (022b). DUTS-TR (Wang et al.; 2017)). We report the results in Table 4 S30D significantly outperforms all the other methods on DIS-5K benchmarks achieving a new state-of-the-ar and reducing the error rate by 14.0%, 7.3% 30.6% and 17.1%. %.
models.
However, salient object detection benchmarks have become highly saturated. S30D achieves superior results on HRSOD-TE (Zeng et al. 2019). DUTS-TE (Wang et al. 2017), and DUT-OMRON (Yngg trained on the first two datasets. The evaluation the
et al. 2013), even though all models DUT-OMRON benchmark serves as the strongest generalization test as none of the models were trained or fine-tuned on it, and the benchmark consists of 5,168 samples. S3OD achieves 24.8 %, 13.6% 26.9% and 15.8% % reduction in error rate compared to BiRefNet. Notably, on UHRSD (Xie ct al. 2022b), the largest HR-SOD training dataset, and DAVIS-S, which contains only 92 images, all large models with transformer backbones achieve comparable results. This is another indicator of benchmark saturation and supports our choice of cross-task generalization evaluation. The variant S30D computes the metrics using the best match among the three masks and the ground-truth mask. This oracle evaluation uses ground-truth information and cannot be compared with other methods. However, it demonstrates the inherent ambiguity in the data annotations and/or the task. This confirms that ambiguity-aware modelling will be highly useful in practical applications.
5.4 SYNTHETIC DATA EVALUATION
We also evaluate our data generation mechanism compared to other data synthesis methods. We measure the impact of synthetic data performance and generalization, evaluating S3OD and other
8

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Published as a conference paper at ICLR 2026
Table 5: Synthetic Data Generation Evaluation: S3ODNet model trained on a combination of
DIS-SK and 3 synthetic datasets. Training with S30D dataset significantly improves generalization.
Timining
Fi. f S.
DIS (1-4)
E5g
MAEL
Fw.
S.,
HRSOD-TE E3g
MAEL
Fw.
S.,
UHRSD-TE E3
MAEL
F... 7
S..
DUIS-TE
MMAL
F...
DUT-OMBON E3, MAE]
S...
DIS
.910
-897
943
132
.923
-913
.932
033
946
.927
-947
133
.902
-901
.925
.136
.008
.830
.858
.061
DIS MaskFactory
912
.004
.950
.830
910
.916
-956
031
937
922
-947
003
886
.898
.924
0038
.774
.912
.842
.071
DIS DateseDDM
858
889
9359
036
899
896
991
041
932
914
934
037
187
877
9000
048
770
7755
.818
A150
DIS $30D
.905
9663
9U58
.951
.025
.899
.048
synthetic data generation methods (Wu et al.
2023a; Qian et al. 2024). MaskFactory (Qian et al.
2024) augments the DIS-5K HIS-5K (Qin et al. (2022) dataset with both rigid and non-rigid transforms and generates a new set of images conditioned on augmented masks. To ensure fair comparison, we train model DIS-5K and mix of DIS-5K and three synthetic datasets. Since the other two synthetic datasets contain only 10,000 train images, we also subsample a subset from S3OD of the same size from the 2nd iteration of data generation. We evaluate the model both on DIS and SOD benchmarks. The results are presented in Table
Results. Interestingly, S3OD achieves comparable performance to MaskFactory (Qian al. 2024) on the DIS-5K test set, even though it was not fine-tuned for categories and types of object in this benchmark, despite MaskFactory utilising the DIS-5K train set to generate augmented masks. On the other four SOD benchmarks, S3OD demonstrates significantly stronger generalization and performance compared to both the original training dataset and other synthetic data generation methods, thereby demonstrating the diversity and versatility of our data generation method.
5.5
GENERALIZATION TO CAMOUFLAGED OBJECT DETECTION
CODIOK
CAMO
NC4K 5.
Method
Data
Fm. f
S. f
E 1 MAE]
F.. f
S. 1 E
MAE]
F..
E 1 MAE]
S3ODNet SOD
.850
.862
.911
.034
.858
.848
.893
.896
.889
.929
.034
S3ODNet
DIS
.832
.853
.035
.845
.846
.892
.058
.885
.882
.922
.035
S3ODNet
MaskFactory
.099
.888
.035
.849
.838
889
.060
.872
.864
.038
S3ODNet
S3OD
.844
.880
.920
.033
.859
.864
.906
.056
.897
.901
.936
.032
FSPNet
COD
.769
.851
.895
.026
.830
.856
.899
.050
.843
.879
.915
.035
BiRefNet
COD
888
.913
.960
.014
.004
.904
.954
.030
.909
.914
.953
.023
S3ODNet
S30D +COD
.911
.933
.970
.012
.908
.903
.449
.031
.923
.920
.961
.020
Table 6: Evaluation on COD benchmarks. We evaluate generalization to Camouflaged Object Detection. When trained on S3OD dataset S3ODNet reach the strongest generalization to the new task in zero-shot transfer setting, comparing to other real and synthetic dataset. Fine-tuned on COD data S3ODNet achieves state-of-the-art results COD-10K and NC4K benchmarks.
Figure 5: Zero-shot Evaluation on Camouflaged Object Detection. Left Right: Image, Predicted Mask, Ground Truth. Our model trained on S3OD generalizes to detecting camouflaged objects despite being trained exclusively on synthetic SOD data.
To evaluate the generalization of our dataset and model beyond salient object detection, we assess transfer to Camouflaged Object Detection (Fan et al., (020): challenging task in which objects are specifically designed to blend with their backgrounds. We evaluate on three COD benchmarks: CODIOK ([aa et al. 2020), CAMO (Le et al. [2019). and NC4K (Lv et al. 2021). Table T that S3ODNet trained solely on S3OD (without any real data) achieves strong zero-shot performance, outperforming models trained on SOD, DIS, or MaskFactory datasets across all metrics. Next, following the BiRefNet setup, and finetune S3OD on CAMO and COD-10K train sets. The finetuned model reaches state-of-the-ane results COD10K (F... 0.911 1 vs BiRefNet's 0.888) and NC4K

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Published as a conference paper at ICLR 2026
(F = 0.923 vs 0.909). Similar to SOD evaluation, observe that the smallest benchmark the other models are also trained on (CAMO with only 250 test images), shows saturation due to overfitting, This validates that our synthetic data teaches generalizable segmentation principles beyond salient object detection. Interestingly, S3ODNet, trained only on our synthetic data, outperforms some methods trained COD datasets (Huang et al.] 2023).
Figure 5 visualizes predictions challenging camoulaaged scenes, showing that models trained only on S3OD successfully detect occluded complex objects with ambiguous boundaries, confirming that even such challenging scenarios represented in S3OD synthetic dataset.
5.6 ABLATION STUDY
Table 7: Iterative Data Generation Ablation: Progressively generating hard samples improves
model performance and generalization across all datasets.
DIS (1-4)
HRSOD-TE
DUTS-
DUT-OMRON
Training Data
E3; ~ MAE]
Fm
S.
EX
MAE]|
Fm.
S. f E
MAE]
E3r
MME]
$30D Single Round
889
1883
916
[0]
951
953
969
.18
933
.935
.959
.020
885
.881
.907
.042
$30D (2 rounds)
880
.884
.188
_040
953
954
.971
.017
.95
.939
.911
.020
839
885
.908
.040
S30D (3 rounds)
.881
.884
.925
.039
.944
.95
.922
.016
.977
.988
.020
860
.887
.911
.040
Table 8: Data Diffusion Model Ablation: Combining all three modalities achieves optimal perfor- mance across benchmarks.
DINO va DVE Maps Concept Maps
Fv.
S. 7 Pig
DIS (1-4)
MAR]
F.. S. 1 F5 MAR
HRSOO-TE
Fi. S., 1 R3r
CHRSSETTE
MAF
Fr.
S..
DUTS: TI!
MARL
Fx. 1 8. S.... F72 MAR]
DUT-OMBON
710
.345
7885
191
733
786
779
097
865
8988
890
056
773
.815
3.00
0.00
681
.735
.772
1995
913
(09
949
000
9999
958
974
014
965
953
.911
017
990
945
968
017
870
-87
.911
914
996
944
080
961
957
972
014
965
952
971
016
944
943
966
1117
871
589
.915
.917
.913
.951
.028
.962
.911
.76
.022
.66
953
971
.016
948
964
969
J016
873
.911
.918
.034
Table 9: Architecture Ablation: Multi-mask decoder improves performance on DIS-5K.
Table 10: Prompt Generator: LLM prompts improve diversity and quality.
Backbone NM
Fm f
S. f
MAEI
Prompt
CLIP
1St
Swin-B 1
.884
.883
.044
DINO-v3
1
.909
.911
.033
Class Name GPT
.399
67.8
DINO-v3
2
.892
.966
.034
.434
.98.1
DINO-v3
3
.914
.913
.031
We evaluate our multi-modal data diffusion approach and architectural components. Table 8 shows individual feature types are insufficient: diffusion features alone cannot decode high-resolution masks, while DINO-v3, despite strong performance, can suffer from train-test distribution gaps when applied to generated images. The combination of all three modalities achieves optimal performance across benchmarks, with diffusion features providing crucial complementary information for challenging, ambiguous cases. The DINO-v3 backbone significantly outperforms Swin-B (Table demonstrating the value of foundation models. Three mask predictions also yield the best performance, proving multi-mask effectiveness. Iterative generation Table consistently improves performance with 3.6% F-measure gain DIS datasets and 5.3% on DUT-OMRON, confirming the effectiveness of prioritizing challenging categories. A-M--geeerttdd prompts improve synthetic image quality with 44.7% Inception Score Table TO increase over simple class names.
6 CONCLUSION
We demonstrate that combining features from generative and discriminative models: diffusion trans- former feature maps, concept attention maps, and DINO-v3 features enables effective synthetic data generation for salient object detection. Our iterative generation framework dynamically prioritizes challenging categories, while the ambiguity pretations. This pipeline significantly improves cross dross-dttaset generalization and provides scalable framework for addressing data scarcity in dense prediction tasks, suggesting that synthetic datasets can complement manual annotations in computer vision applications.
architecture naturally handles multiple valid inter-
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This research supported by ERC StG 101222037-Volute AIST policy-based budget project "R&D on Generative Al Foundation Models for the Physical Domain", and Japan Science and Technology Agency (JST) part of Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE), Grant Number JPMIAP2518. Computational resources were provided by ABCI 3.0 from AIST and AIST Solutions. We are grateful Andrew Zisserman for valuable discussions and feedback that helped improve this work.
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Chenxi Xie, Changqun Xia, Mingean Ma. Zhirui Zhao, Xiaowu Chen, and Jia Li. Pyramid grafting network for one-stage high resolution saliency detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11717-11726, 2022b.
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Ryota Yoshihashi, Yuya Otsuka, Tomohiro Tanaka, Hirokatsu Kataoka, al. Exploring limits of diffusion-synthetii training with weakly supervised semantic segmentation. In Proceedings of the Asian Conference on Computer Vision, pp. 2300-2318, 2024.
Qian Yu, Xiaoqi Zhao, Youwei Pang, Lihe Zhang, and Huchuan Lu. Multi-view aggregation network for dichotomous image segmentation. In Proceedings of the IEEE/CVF Conference Computer Vision and Puttern Recognition, pp. 3921-3930 2024.
Yi Zeng, Pingping Zhang, Jianming Zhang, Zhe Lin, and Huchuan Lu. Towards high-resolution salient object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pp.
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A
FEATURE COMPLEMENTARITY
Figure 6 visualizes the three feature sources on dataset samples. Concept attention maps provide strong but coarse -eeground-backgroundd separation through explicit semantic grounding. DINO-v3 features capture fine- fin-graiined visual semantics where similar regions exhibit similar embeddings, enabling strong object-level understanding. FL.UX DiT features encode spatial scene parsing in- formation from the generative process, including boundary localization and structural composition. The visualization also demonstrates the limitations of individual feature sources. Concept maps provide strong foreground cues in simple scene but fail to precisely localize foreground objects in more complex scenarios (rows 3 and 4) - demonstrating the limitation of unsupervised segmentation methods that rely only on attention maps (Helbling et al. 2025). Rows 3 and 5 also show DiT feature maps capabilities: in contrast to DINO features, the objects in reflection snow pile patches have higher similarity as the diffusion model efficiently reuses the information during generation. This precisely demonstrates the importance of combining multiple feature sources: in highly complex, ambiguous scenes, generative and discriminative features complement each other, allowing for the decoding of high-quality mask. Note that FLUX and DINO-v3 features are high-dimensional and visualized via PCA for interpretability.
Image
FLUX Maps
DINO-V3 Maps
Attention Maps
Mask
Figure 6: Multi-Modal Feature Visualization: Each modality captures complementary information: concept attention maps provide semantic localization, DINO-v3 encodes fine-grained visual semantics, and FLUX DiT features capture spatial scene structure. High-dimensional features (FLUX, DINO-v3) are visualized via PCA projection to RGB.
B PROMPTING
To further enhance the quality and diversity of our synthetic data, we employed an LLM (Achiam et al.. 2023) to generate detailed, specific prompts rather than using simple class names. The prompting strategy was designed to systematically vary key aspects scene composition, including object size, positioning, occlusion levels, lighting conditions, and environmental complexity. For example, when generating "lion" category images, prompts varied from scenes with multiple lions to single lions in challenging environmental conditions. These detailed textual descriptions guided the diffusion model to create more challenging, diverse training samples that better reflect real-world scenarios and edge cases. The set of example prompts for the "lion" category includes:
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1. A medium-sized lion lying sunlit rock, partially obscured by tall grass, with dense forest background; intricate shadows play on the lion's fur and the rock surface.
2. A small lion cub, occupying the left third of the frame, peeking through a thicket of dry branches savannah setting with blurred golden grass and distant treeline.
3. Two lions resting under the shade of an acacia tree, one lion partially hidden by the tree's trunk; dappled sunlight filters through the leaves, creating complex patterns the ground.
4. A majestic lion standing on a hilltop, backlit by the setting sun, casting a dramatic silhouette against vibrant, cloud-streaked sky with the savannah stretching out in the background.
5. A close-up of lion's face, centered in the frame, with its mane blending into similarly colored rocky background; fine textures the fur and rock are sharply defined.
6. A trio of lions walking through misty grassland, with their figures partly obscured by the fog: subtle variations in coloration and mane distinguish each lion.
7. A lioness crouching low in a field of tall yellow grass, partially obscured and camouflaged by the foliage, with clear blue sky above and distant hills in the background.
The full system prompt is presented in Figure
a photo a cup
a photo llon
Figure 7: Prompt Enhancement: Top: Class name as a prompt. Bottom: LLM Prompt Generator. By focusing on key properties of salient object detection dataset the agent creates detailed and diverse prompts to maximize the diversity and realism.
C DATASET QUALITY
Table 11: Dataset Quality Comparison: S3OD generated with large DiT model fine-tuned for photorealism achieves substantially higher quality and better real-data alignment compared existing synthetic approaches, demonstrating the importance of realistic generation models.
Method
Diffusion Model
Inception Score
FID +
S3OD
FLUX-Kree (Black Forest Labs
2025)
35.19
1.74
S3OD MaskFactory
FLUX-dey (Labs) 2023)
31.94
1.90
Stable Diffusion (Rombach al.
2022
17.41
2.81
DatasetDM
Stable Diffusson (Rombach et al.
2022
14.97
3.16
We conducted quality assessment of our synthetic dataset across multiple dimensions. Manual verification of 1,000 randomly sampled masks revealed high annotation quality: only 14 samples (1.4%) exhibited minor issues such as slightly incomplete mask boundaries, while merely sample
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UMAP: Images
UMAP: Masks
WOT Muatfadarry
Haudactory
0M073
USSPPT
Image Feature Distribution (UMAP)
Mask Feature Distribution (UMAP)
Images Precision Recall
Masks Precision Recall
1.0
1.0
0.6
0.6-
Precesion
0.4
0.4-
$100 9091
$100 (Fii0 699)
DataseDDM (1.0.81))
DavasetDM (F-0.844)
0.0
0.0
1.0
0.0
6.4
0.6
Recall
Recall
(c) Precision-Recall: Images
(d) Precision-Recalt Masks
Figure 8: Domain Gap Analysis. (a-b) UMAP projections of DINO-v3 image and masks features. S3OD covers large portion of the real data distribution matching combined SOD real datasets. (c-d) Precision-Recal curves (Kynkäänniemi et al. 2019) vs a combination of DIS and SOD dataset: S3OD achieves higher recall and precision for both images and masks, compared other synthetic datasets that only cover part of the real data distribution, demonstrating lower synthetic to real gap.
(0.1%) was missing a clear foreground object entirely. This demonstrates the effectiveness of our multi-stage filtering pipeline and multi-modal dataset diffusion approach.
To quantitatively evaluate synthetic-to-real domain gap compute quality and coverage of the samples produced by generative model following (Kynkäänniemi et al. (009)) versus a combination of SOD and DIS datasets. We observe that both synthetic images and masks closely follow real distribution in contrast to other methods that only model a part of it. Further, UMAP (MeInnes et al. 2018) projections of DINO-v3 image and masks features demonstrate that S3OD samples cover larger region the real data manifold compared to MaskFactory (Qian et al. (024). Reduced domain gap directly explains the superior generalization of the models trained S3OD.
Another significant challenge in synthetic data generation is the domain gap between synthetic and real images. We observed that standard FLUX model fine-tuned for aesthetics produce unnatu- rally oversaturated images that differ substantially from real- world photography. To address this, we employ the FLUX- Krea checkpoint (Black Forest Labs] 2025), which underwent large-scale reinforcement learning alignment specifically for photorealism, producing significantly more natural-
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Minivan
Tent
lbex
Mailbag
Joquar
Mashed Potato
Cycllit
Throne
Figure 9: S3OD Dataset Samples: Our method generates diverse high-quality samples across wide variety of object categories.
looking images. Additionally, during pretraining we apply comprehensive image augmentations to further reduce the synthetic-to-real domain gap.
We evaluate synthetic data quality using standard generative model metries compared to existing approaches. As shown in Table []] our method achieves superior image quality and diversity compared datasets based older diffusion models. S3OD achieves Inception Score of 35.19 compared to MaskFactory's 's 17.41 and DatssetDM's 14.97, indicating better diversity and quality. Our FID score of 1.74 significantly outperforms MaskFactory (2.81) and DatasetDM (3.16), demonstrating closer similarity to real data distribution.
D QUALITATIVE EVALUATION
We visualize the random samples from different categories of S3OD in Figure 9 It demonstrates the diversity and realism achieved by our synthetic data generation pipeline, spanning various object types, lighting conditions, and scene compositions. The samples exhibit challenging scenarios with complex backgrounds, partial occlusions, and varying object: key attributes for training robust salient object detection models. As shown Figure] LLM-based prompt generation significantly enhances the visual quality and diversity.
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E
MODEL DETAILS
S3ODNet achieves strong balance between performance and efficiency as shown in Table T2 comparable to other state-of-the-art models that utilize large transformer backbones. Notably, the model is both more efficient and has more parameters compared to models that are based on the Swin architecture (Liu al. 102 The DINO-v3 (Siméoni al. 2025) backbone with ViT-B (Dosovitskiy et al. 2020) offers favorable trade-off between computational efficiency and state-of- the-art performance.
Table 12: Model Efficiency. S3ODNet achieves comparable performance other state-of-the-ard
salient object detection methods.
Model
Total Parameters
FLOPs (T)
FPS
InSPyreNet Kim ct al. 2022
90,721,444 ,443
1.495
2.88
BiRefNet (Zheng et al. 2024
220,176,496
1.143
3.65
MVANet YYu ct al S3ODNet
2024)
94,139,02
0.857
4.62
116,905,286
0.807
3.80
F STATE-OF-THE -HR COMPARISON
Input Image
InSPyReNet
BiRefNet
MVANet
S30D
Ground Truth
Figure 10: Qualitative Comparison: We compare S3ODNet vs state-of-the-ar methods on DIS-5K (Qin al. 2022) dataset. By modeling multiple hypothesis S3ODNet is able to predict detailed masks with high confidence. Alternative prediction can be seen in the bottom right corner.
We further expand the analysis of S3OD performance vs other state-of-the-art methods. Table 13 evaluates the performance compared models finetuned from the foundational segmentation model (Ravi et al. 2024). We observe that all models that are based on SAM perform well on simpler subset DIS (DIS-TE) but the performance drops significantly as the sample complexity increases. S3ODNet outperforms all approaches (Ke et al. 2023 Liu et al. 2024) matching the performance of DIS-SAM (Liu et al., 2025), which a more complex two-stage pipeline consisting of two separate models performing segmentation in high resolution, resulting significantly larger complexity and number of parameters compared to our simple network design. This evaluation demonstrates that the limited manually labeled data is still insufficient to finetune models pretrained on various data from slightly different domain.
the state-of-the-ae foundational
Next we provide the results of more state-of-the-ar methods as well as S3ODNet variant trained only on DIS-5K or SOD datasets in Table t4 further evaluate the impact of pretraining on synthetic data. We include (Wei et al., 2020a Zeng et al. 2019: Tang et al. 2021 Xie et al. 2022a) model to SOD evaluation. Interestingly. S30D trained only on synthetic data outperforms most of the older methods that were trained on SOD datasets when evaluating on SOD benchmarks! This showcases both the quality of the synthetic data and model effectiveness. S3ODNet trained only on SOD
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confirms the insights from Section the performance salient object detection benchmarks is saturated. All transformer-basee based methods that were trained on SOD data show comparable performance when evaluating on the same datasets. The only benchmark that is from different data distribution is DUT-OMRON, demonstrating that S3ODNet trained on SOD outperforms other methods and pretraining on S30D further improves performance. This also highlights the importance of cross-dataset generalization evaluation instead of only measuring overfitting to small academic benchmarks.
The evaluation of S3ODNet trained on DIS-5K follows the same trend. We further evaluate (Qin et a 2019; 2020; Xie et al., 2022a Qin et al. 2022 Pei et al.] [023] (hho et al.) 2023). Similarly to other evaluations, S30DNet trained on DIS outperforms other methods trained on same dataset and pretraining on S3OD further improves the performance.
Table 13: Comparison of SAM-based methods and S3ODNet: Our model outperforms most larger models finetuned from Segment Anything matching the performance of complex two-stage pipeline
Liu et al. 2025).
Mothod
DIS-TEI Fi. 1 8., 7 F64 MAE
DIS- TE2
DIS-TES
DIS-TE4
Ovemll
Fi.
S.. 1 E 1
MAEL
F.. 1 S., 1 Fir MAEL
Fw.
S.. 1 F22 MAE]
F.. 1 S., 1 F& MAE]
SAM
838
8843
.015
.047
.813
.792
.863
.181
.773
.761
848
.194
.677
.697
.762
.162
.773
.773
845
(996
HQ-SAM
.955
-950
029
.776
.863
088
PISAM
890
-894
9447
.027
903
.907
953
.027
899
-901
-953
.030
.369
.871
-939
046
.90
.893
-948
003
DIS-SAM
.929
9929
-960
.019
.944
921
-955
.025
918
918
.448
030
889
888
993
04.1
.917
.911
9449
.029
S30DNet
923
.909
.954
.034
Table 14: Quantitative Comparison: We extend the comparison to more baselines and also evaluate S3ODNet trained only on real data. S3ODNet trained on the same datasets as prior work demonstrates better performance. Pretraining on S30D further improve the performance, showing the value of the dataset even on saturated benchmarks.
Method
Data
P.,
7
8.
DAVIS
F3
MAF]
F..
8- 1
HRSOD-TE:
MAR
Fi..
S. 1
UHRSD-IE
MAF]]
F...
S.. 1 F.2
DUTS:TE
MAF.
Fr. 1 J.... FF2
DUEAMMONN
MAFT
LDF
SGD
.911
.922
.947
.319
.904
.904
.919
.032
1355
.915
.591
.047
.892
.398
.910
.034
.820
.838
.873
.051
HRSOD
50D
399
.876
9955
-136
905
.856
-134
-030
8335
.324
.335
050
.743
.762
.331
D65
DIIQ
50D
938
.900
.947
011
922
.920
.947
J022
900
911
.905
009
.84
.900
.919
031
.820
.33
873
045
PGNet
SUD
.310
945
935
.859
.371
135
1258
Nat
500
957
965
996
D46
80D
96.2
008
SRODNer
SU3
991
0005
966
948
9967
951
9666
919
1333
S30DNet
530D SOD
9744
.933
.054
966
.961
.978
.013
996
952
.969
01.
.944
.949
.972
.015
.879
889
.924
JA32
Method
Data
Fr.
S.
D1S-1
E3r
MAEL
5.
DIS-2
Et MAEL
TIE--3
.E5 MADI
Fr.
S.
DXS-4
ET MAEL
F.
5.
Overill
E5 MAE]
RASNer
.663
.756
.105
-781
.765
.844
.796
.814
.992
UNN
DIS
.701
762
.783
.668
7766
822
0R3
.813
073
800
.114
.837
.085
.711
.759
8225
.882
PGNNt
DIS
.754
.00
.845
.057
.607
.833
.80
.063
.843
.644
.911
1056
.551
.541
.8999
.065
309
1530
8855
106.3
IS-Net
DIS
740
787
.330
.174
759
1933
8588
070
.510
8836
583
0664
827
3.30
.870
.072
780
811
858
070
FP. DIS
DIS
786
821
860
.050
.27
866
.893
059
366
891
922
0419
8:6
.522
.906
.061
831
.847
895
D477
UDUN
DIS
.74
817
364
J059
X29
J543
886
055
8655
H65
1917
050
846
2449
.901
1159
X31
854
.92
1257
SAM-HQ
D1S
.897
.977
.93
.019
889
883
928
029
851
.07
045
763
709
.843
083
850
850
983
045
InSpyreNet
DIS
.845
815
.544
394
965
036
905
.95
.966
.042
391
900
.917
039
BiReiNet
DIS
035
MVANee
035
SRODNet
DBS
.896
922
9005
943
133
028
396
333
942
3877
943
1932
S30DNet
DIS + S30D
.892
902
922
0031
923
921
983
826
933
920
960
825
.99
902
.944
.034
.14
.111
.95
.029
G MULTI-MASK DECODER ANALYSIS
Our multi-mask decoder builds upon the multiple hypothesis prediction (MHP) framework of (Rup- precht et al., 2017), which shows that predicting M hypotheses creates Voronoi tessellation of the output space, with each hypothesis converging to the conditional mean of its region. However, salient object detection differs fundamentally from inherently ambiguous tasks like future predic- tion: most samples have single clear ground truth and only a small subset are truly ambiguous (multiple objects or complex scene). This creates critical training instability. Without explicit regularization, branches that are initially far from the data receive no gradients from the best-match selection i' = arg min; loU(m,, y) and degenerate, as most samples assign to a single dominant branch. This is why we introduce auxiliary loss with exponential decay L = L;- + >rege 72 "E, Li, which prevents branch collapse by forcing all branches to maintain proximity to ground truth early in training. then gradually allows diverse outputs as the decay reduces supervision. This setup enables branches to handle both the dominant unambiguous cases and the sparse ambiguous samples. The ablation study below validates this design. The baseline configuration achieves optimal balance
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between branch diversity and segmentation performance. Without auxiliary loss, observe branch collapse as two branches stop receiving gradients and output empty masks. Static regularization without decay produces overfits to output all similar masks ignoring the ambiguity, while stronger regularization or slower decay both slightly reduce entropy without clear performance benefits.
Table 15: Multi-Mask Decoder Loss Ablation: We report segmentation performance on UHRSD- TE and DUT-OMRON benchmarks, along with diversity metrics computed across all test samples.
Diversity Metries
UHRSD-TE
DUT-OMMON
Areg
Entropyt
Avg loU
Fm t
Sa f
E
MAE
F..
S. t
E t
MAEI
0.1
0.2
.878
.833
.964
.948
.967
.019
.844
.890
.919
.033
0.2
0.2
.823
.869
.963
.948
.967
.020
.873
.891
.917
.033
0.1
0.1
.824
.877
.962
948
.967
.020
.873
.890
.916
.034
0.1
0.0
.906
.945
.962
.949
.968
.019
.874
.890
.919
.034
0.0
0.0
0.0
0.0
.964
.947
.966
.020
.876
.890
.920
.034
We evaluate the impact of auxiliary branch regularization through the Xreg and decay rate 7 parameters in our multi-mase decoder loss formulation. The baseline configuration uses Areg 0.1 with exponential decay ? 0.2.
Due to the computational cost of retraining the model, we cannot perform exhaustive grid search over all possible parameter combinations. Instead, we strategically select four key ablation variants that test fundamental design choices: (1) stronger regularization (Areg 0.2) to assess if auxiliary branches benefit from full mask supervision, (2) slower decay (7 0.1) to maintain full mask longer during training, (3) static regularization (y = 0.0) without any decay evaluate the necessity of the temporal annealing mechanism, and (4) no auxiliary loss (Areg = 0.0) training only the best-matee best-matching branch to test if some branches stop receiving gradient during the training.
These variants assess the trade-off between enforcing branch diversity and preventing degradation of unused predictions. The last configuration (Areg = 0.0) is particularly important as it tests whether supervising all branches with the ground-trut mask in early epochs provides any benefit and stabilizes the training.
H LIMITATIONS AND BROADER IMPACT
S3OD data is fully generated, so we deliberately don't provide a test split for the dataset, as we believe the methods be pretrained synthetic data, but should be evaluated smaller-scale, precise human annotations. The multi-stage filtering strategy detects and removes most of the fail cases but the model occasionally might produce some artifacts both while generating image or mask, such as mask not fully covering an object or scene missing a clear salient object. We acknowledge the high computational cost of generating large-scale data using diffusion transformers, yet the process is still orders of magnitude faster than manual labeling and can be effectively parallelized. Additionally. similarly to (Zheng al. 2024) we observe that training for more than 100 epochs almost does not impact the metrics but slightly improves finer details quality so we were able to obtain similar metrics with using only 4 A6000 GPUs for 2.5 days which makes the training pipeline more accessible. We expect that the insights into the combination of generative and discriminative features, well as the iterative data generation, can be reused other tasks and domains, especially where obtaining the ground truth data is the main bottleneck for scaling.
THE USE OF LARGE LANGUAGE MODELS (LLMs)
The LLM is a core part of the dataset generation method Figure 3 ensuring we build a large library of diverse captions for various object categories. We also used LLMs polish the writing, verify grammar or improve the sentence structure.
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System Prompt for Salient Object Detection Data Generation
Generate exactly (num-prompts] diverse, photorealistic prompts for (main images for salient object detection. Create natural scenes with varying complexity levels. Requirements:
Photorealistic scenes only artistic cartoon styles
Main object clearly visible and identifiable
Sharp focus throughout the scene
Natural lighting and environments
Vary these aspects across prompts:
Object variations: Different sizes, positions, quantities (1-3 objects), conditions (new/worn), and orientations
Scene complexity: Mix simple and complex backgrounds from clean settings cluttered envir environments with multiple objects and busy textures
Lighting conditions: Natural daylight, golden hour, overcast skies, indoor lighting, shadows and highlights
Indcor/outdoor settings, natural habitats, functional contexts (object being used), storage areas, scasonal variations
Environments:
Visual challenges: Include some scenes with partial occlusion, similar colors, reflective surfaces, overlapping objects, or camouflage effects when natural
Perspectives: Close-ups, medium shots, wide views, and varied camera angles (above, below, side views)
Context diversity: Objects in use, at rest, in groups, in natural habitats, different weather conditions, times of day
Balance: Create mix of challenging segmentation scenarios. Return exactly [num prompts} prompts as Python list: ' 'A scene description.....
Important: Maximize diversity- avoid repetitive scenarios settings.
Figure 11: The complete system prompt used to instruct the LLM (Achiam et al. 2023 for generating diverse text descriptions. These descriptions create photorealistic scenes with varying complexity, object configurations, and environmental conditions to simulate challenging real-world scenarios for salient object detection. The prompt emphasizes natural lighting, sharp focus, and balanced diversity across simple and complex backgrounds.
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Reviews:
[{"review_id": "55Cr6dSoew", "reviewer": "Reviewer_7iqD", "summary": "This paper introduces S3OD, a new synthetic data generation pipeline and ambiguity-aware model for salient object detection (SOD). The approach leverages multi-modal diffusion models to generate over 139,000 high-quality, high-resolution images and corresponding masks that capture diverse, ambiguous segmentation scenarios. S3OD combines iterative, feedback-driven data sampling and a streamlined multi-mask architecture built on a DINO-v3 backbone, aiming to improve generalization across both dichotomous segmentation (DIS) and high-resolution SOD (HR-SOD) tasks. Empirical evaluation demonstrates strong cross-dataset transfer and new state-of-the-art results on multiple SOD/DIS benchmarks. The dataset and code will be released.", "questions": "Please see Weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**1) Scale and Diversity of Synthetic Data (Figure 1, Table 1, Figure 4, Figure 6):** S3OD delivers an order-of-magnitude increase in dataset scale for SOD, with 139k+ images spanning 1676 unique objects and a wide spectrum of scene types, lighting, and occlusions, as seen qualitatively in Figures 1, 4, and 6 and quantitatively in Table 1. Manually verified mask quality and data curation strategies, including filtering with VLMs, result in a synthetic dataset that rivals or exceeds real sets in annotation fidelity.\n\n**2) Innovative Data Generation Pipeline (Figure 3):** The multi-modal pipeline fuses diffusion latent features, concept attention maps, and DINO-v3 features to create dense, pixel-precise masks without teacher bottlenecks or reliance on pre-existing mask libraries. The iterative feedback loop dynamically adjusts category sampling based on model performance (see Figure 3 and Section 4.2), which is a notable step forward in dataset synthesis methodology.\n\n**3) Ambiguity-Aware Model Architecture (Figure 2):** The proposed multi-mask head on top of a DINO-v3/DPT backbone enables the network to represent inherent annotation ambiguity—a factor often overlooked in SOD. Figure 2 provides a clear and helpful visualization of the model’s architecture, showing multiple candidate mask outputs and predicted IoU for selection at inference.", "weaknesses": "**Potential for Domain Overfitting or Synthetic-“Leakage” Not Fully Addressed:**\n\n1. Although the cross-dataset generalization is well documented, concerns about overfitting to synthetic artifacts (such as those possibly present in highly artificial or LLM-generated prompts) are only partly mitigated by filtering and photo-realism tuning (see Figure 7 and Section B). There is no explicit domain gap or bias quantification (such as t-SNE/UMAP distributions, or model calibration metrics) to back up claims about synthetic-to-real transfer.\n\n2. Table 9 claims strong Inception/FID scores, but these are image-level metrics—less relevant than mask consistency or class-level error for segmentation evaluation.\n\n**Limited Theoretical Justification for Multi-Modal Fusion and Mask Decoding (Section 3.1, Equations):**\n\n1. While practical motivation is given, the paper glosses over the theoretical rationale for fusing FLUX DiT, concept attention, and DINO-v3 features in the supervision pipeline. The process is described at a systems level (“projected to a common 256-dimensional space via separate convolutional branches...”) but lacks rigorous analysis of why this fusion is optimal, how information is disentangled, or how fusion improves over single-modality supervision. Further, the formulation of loss weighting and decay (e.g., $\\lambda_{\\text{mask}}=10$, $\\gamma=0.2$) is heuristically chosen; no ablations or theoretical discussion support these choices.\n\n2. The mask selection (best out of N) and loss propagation scheme is borrowed from multiple-choice learning but could benefit from a more detailed analysis or ablation to determine how many branches (N) are optimal and how performance scales with it.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lfQxWfWUWG", "reviewer": "Reviewer_BL7i", "summary": "S3OD targets the long-standing generalization limits in salient object detection by unifying DIS and HR-SOD through synthetic data and an ambiguity-aware model. The authors build a 139k+ high-resolution dataset via a multi-modal diffusion pipeline that extracts masks from diffusion and DINO-v3 features, then iteratively steers generation toward hard cases based on model feedback.  They propose a streamlined multi-mask decoder that explicitly models multiple valid saliency interpretations, addressing label ambiguity while keeping the architecture simple.  Trained only on synthetic data, their method improves cross-dataset generalization with 20–50% error reductions; with brief fine-tuning on real data, it reaches or surpasses state of the art across DIS and HR-SOD benchmarks.", "questions": "- Could a teacher model be used to annotate the FLUX-generated images, then train S3OD on these teacher-labeled data, and compare its performance with the S3OD proposed in this paper?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Multi-Modal Dataset Diffusion Pipeline that fuses diffusion feature maps, concept attention maps, and DINO-v3 representations to jointly generate images and masks, ensuring strong image–label alignment and enabling a 139k+ high-resolution synthetic set that boosts generalization.\n\n- Ambiguity-aware architecture with a streamlined multi-mask decoder that explicitly models multiple valid interpretations.\n\n- Iterative generation framework that is feedback-driven to prioritize challenging categories, delivering consistent gains and cross-dataset generalization, culminating in SOTA across DIS and HR-SOD and large error-rate reductions versus prior methods.", "weaknesses": "- The author should clarify whether mask extraction in the Multi-Modal Dataset Diffusion pipeline requires any training or calibration. If not, provide rigorous evidence of mask fidelity.\n\n- The proposed data-generation paradigm appears tailored to binary/saliency segmentation; please evaluate transfer to camouflaged object detection (COD) or non-salient classes and report the zero-shot and fine-tuned results.\n\n- The author should strengthen the annotation rationale with interpretable visualizations. Provide side-by-side maps of DINO-v3 features, DiT feature activations, and the resulting masks across easy and challenging scenes, plus quantitative correlations, to demonstrate complementary cues.\n\n\n- On page 4, line 197, the last two terms in the loss function formula should be grouped together.\n\n- It is recommended not to place Tables 7 and 8 at the very bottom of the page.\n\n- Citations should be enclosed in parentheses.\n\n- On page 2, line 059, “remains” → “remain”.\n\n- On page 2, line 067, “features” → “feature”.\n\n- On page.6, line.270: “order of magnitude” → “an order of magnitude”/“orders of magnitude” contain” → “contains”.\n\n- On page.8, line.401: “siginificantly” → “significantly”.\n\n- On page.8, line.407: “S3OD achieve” → “S3OD achieves”.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AvmMWBIGvX", "reviewer": "Reviewer_VcaT", "summary": "In this paper, the authors proposed S3OD method for salient object detection. In addition, the authors introduced S3OD dataset. In particular, they have created a massive synthetic dataset of over 139000 high resolution images with machine generated labels. The S3OD dataset helps improve the performance of the S3OD method.", "questions": "Please see the above comments.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "+ The authors introduced S3OD dataset with 139,000+ samples.\n+ Models trained only on the S3OD data show good performance.", "weaknesses": "- The authors use S3OD as the name for both method and dataset. This causes confusion in the paper reading.\n- The novelty of the proposed S3OD method is incremental. All components of S3OD exist in literature. \n- The new S3OD dataset is AI-generated. However, all existing SOD datasets used ground truth from humans. The ground truth of S3OD dataset should come from humans. \n- The experiments are unfair. All baselines were not trained on the S3OD dataset. More than 139,000+ samples of S3OD should benefit baselines. \n- The authors should carefully proofread the paper. They have not described Figure 1, Figure 4 and Table 1 in the text.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZjgWKTC5RE", "reviewer": "Reviewer_n7Sk", "summary": "This paper introduces S3OD, a large-scale synthetic dataset (139k images) and a corresponding ambiguity-aware salient object detection (SOD) model. The work proposes a multi-modal diffusion pipeline that fuses FLUX DiT features, concept attention maps, and DINO-v3 embeddings to generate high-quality image–mask pairs. An iterative generation framework prioritizes difficult categories based on model feedback, and a multi-mask decoder handles label ambiguity by predicting multiple plausible saliency maps. Experiments on DIS-5K, HRSOD, UHRSD, and DUT-OMRON show strong cross-dataset generalization, with models trained purely on synthetic data approaching or surpassing state-of-the-art methods.", "questions": "1.The treatment of ambiguity remains heuristic. The paper models uncertainty through multiple mask branches and a decaying regularizer but lacks a probabilistic or information-theoretic formulation.\n2.Loss functions (Focal + IoU) and weighting constants (e.g., λ_mask = 10, γ = 0.2) are empirically set without analytical justification or stability analysis.\n3.The relaxed branch assignment mitigates degeneration empirically but offers no convergence proof or theoretical characterization of branch usage.\n4.Domain-gap evaluation between synthetic and real data relies solely on FID and Inception Scores—useful but insufficient for capturing distributional or representational discrepancies.\n5.The experimental section is extensive and well organized: the authors evaluate cross-dataset generalization, multi-modal ablations, multi-round data synthesis, and backbone comparisons. This breadth supports the main empirical claim that synthetic data can significantly improve SOD generalization. Nevertheless, several confounding factors remain unresolved. The performance gains might partially stem from larger data volumes and stronger pretrained encoders (DINO-v3) rather than from the proposed mechanisms themselves. Moreover, reproducibility is limited by high computational cost (three data-generation rounds, filtering, and multi-GPU training) and incomplete sensitivity analyses of key hyperparameters.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.Novel synthetic-data pipeline that unifies diffusion- and representation-based cues (DiT + DINO-v3 + concept maps), addressing weaknesses of prior attention-extraction methods.\n\n2.Large-scale dataset contribution with detailed filtering and iterative feedback, potentially valuable to the SOD community.\n\n3.Ambiguity-aware decoding design that explicitly models multiple valid masks, a practically important perspective for SOD.", "weaknesses": "1.While the dataset generation process is well described, the core algorithmic novelty of the ambiguity-aware model is incremental—largely a DPT backbone with multi-branch outputs and IoU-guided selection. This architectural contribution may not reach ICLR’s expected level of theoretical innovation.\n2.The evaluation relies mainly on FID and Inception Score; no user study or domain-gap quantification is provided to show the practical relevance of the generated images for real-world deployment.\n3.The comparison with other methods may not control for data volume and pretraining effects (e.g., DINO-v3 backbone vs. Swin-B), so performance gains might arise from model capacity rather than the proposed framework.\n4.Critical hyperparameters (diffusion steps, fusion architecture, loss weights) are partially specified but not fully justified. The claim of “2 days on 8×H200” suggests heavy computation that may not be accessible to the community.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)