Unbiased Object Detection Beyond Frequency with Visually Prompted Image Synthesis
Authors: Xinhao Cai
Liulei Li
Gensheng Pei
Tao Chen
Jinshan Pan
Yazhou Yao
Wenguan Wang
Venue: ICLR 2026 Poster (2026)
Keywords: computer vision
image generation
object detection
dataset debiasing
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: This paper presents a generation-based debiasing framework for object detection. Prior debiasing methods are often limited by the representation diversity of samples, while naive generative augmentation often preserves the biases it aims to solve. Moreover, our analysis reveals that simply generating more data for rare classes is suboptimal due to two core issues: i) instance frequency is an incomplete proxy for the true data needs of a model, and ii) current layout-to-image synthesis lacks the fidelity and control to generate high-quality, complex scenes. To overcome this, we introduce the representation score (RS) to diagnose representational gaps beyond mere frequency, guiding the creation of new, unbiased layouts. To ensure high-quality synthesis, we replace ambiguous text prompts with a precise visual blueprint and employ a generative alignment strategy, which fosters communication between the detector and generator. Our method significantly
narrows the performance gap for underrepresented object groups, e.g., improving large/rare instances by 4.4/3.6 mAP over the baseline, and surpassing prior L2I synthesis models by 15.9 mAP for layout accuracy in generated images.

Published as a conference paper at ICLR 2026
UNBIASED OBJECT DETECTION BEYOND FREQUENCY WITH VISUALLY PROMPTED IMAGE SYNTHESIS
Xinhao Caiz.4; Liulei Li Gensheng Pei Tao Chen
Jinshan Pan', Yazhou Yao Wenguan Wang²
Nanjing University of Science and Technology 2 "Zhejiang University
3 Department of Electrical and Computer Engineering. Sungkyunkwan University
State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery
https:// /ttpp:u//gthub.... commNUSS Machine- Intel -igennc--aaorraorry
ABSTRACT
This paper presents generation-baser debiasing framework for object detection. Prior debiasing methods are often limited by the representation diversity of sam- ples, while naive generative augmentation often preserves the biases it aims solve. Moreover, our analysis reveals that simply generating more data for rare classes is suboptimal due two core issues: i) instance frequency an incom- plete proxy for the true data needs of a model, and ii) current layout-to-iman thesis lacks the fidelity and control to generate high-quality, complex scenes. To overcome this, we introduce the representation score (RS) to diagnose represen- tational gaps beyond mere frequency, guiding the creation of new, unbiased lay- replace ambiguous text prompts with
syn-
outs. To ensure high-quality synthesis, a precise visual blueprint and employ a generative alignment strategy, which fos- ters communication between the detector and generator. Our method significantly narrows the performance gap for underrepresented object groups, e.g., improving large/rare instances by 4.4/3.6 mAP over the baseline, and surpassing prior L2I synthesis models by 15.9 mAP for layout accuracy in generated images.
INTRODUCTION
The reliability of object detection models is fundamentally limited by biases in their training data, manifesting as skewed distributions across object categories (Ouyang et al. 2016),, sizes (Herranz al., 2016)). and spatial locations (Zheng al.) 2024). Conventional debiasing strategies. such as resampling (Cui et al.) (019) re-weighting (Tan et al., 2020), attempt to mitigate this by adjusting the influence training instances based on frequency. While effective to a degree, these methods
constrained by the visual vocabulary of the original dataset. They can re-balance the influence of rare samples but cannot generate novel appearances or contexts to fill representational gaps.
Generation data augmentation (Wu et al.] 2023 Trabucco et al.) 2024) has emerged as promising alternative to overcome this limitation. By synthesizing entirely new training samples, these methods hold the potential to create more balanced dataset. However, current solutions for object detection typically follow layout-to-image (L2I) synthesis pipeline (Chen et al. 2024a Wang et al. 2024), where the layouts used conditions for data generation directly sampled from the oriiinal training set. Thus the generation process inevitably preserves the very biased distributions they aim solve, leaving clear need for a truly bias-aware generation strategy.
But what would an effective generation-based debiasing framework entail? Our investigation in 2 reveals that: i) simply combining the frequency-centric debiasing view with generative approaches, i.e., generating more images for rare data groups, is not the final answer. It can outperform both traditional augmentation techniques (e.g.. copy-paste, random flip, crop) and bias-agnostic L2I syn- thesis, yet still falls short of the gains achieved by enriching the training set with more real samples; ii) the quality of samples generated by current L.2I synthesis methods remains below that of real data, as models trained synthetic samples consistently underperform those trained on real ones.
"Equal contribution. Corresponning author.

---

Published as a conference paper at ICLR 2026
The problems can be two sets: Instance frequency is incomplete proxy determine the most needed data of a model (Chawla et al. 2002 He & Garcia 2009). According to the controlled ex- periments in $2 find that certain ngg-peerorming and data-rich groups (e.g., large objects) can be more 'data hungry' and gain greater benefit from additional data compared to low-performing groups with limited samples (e-g., small objects). Relying solely frequency can result in subop- timal interventions. @ Even with a perfect, bias-targeted layout and a powerfil generation model, current L2I approaches struggle to render new samples faithfully. Prior L2I methods primarily focus on fusing layout conditions into the generation process, with limited attention given to enhancing the fidelity of generated images to real-worlls data. Moreover, these methods directly translate 2D spatial arrangements into ID text sequences. This introduces ambiguity and lacks the fine-graines control for complex scenes with specific object relationships and occlusion (Johnson et al. 2018).
In this work, we propose targeted debiasing framework that automatically diagnoses the under- represented data groups and executes precise generation to diversify training data. To tackle O, we introduce a representation score (RS) that moves beyond simple frequency counts to quantify how well a concept is represented across both sample density and representation diversity. The RS then guides bias-aware recalibration module which constructs new, unbiased layouts to fill the identified representational gaps. Furthermore, the entire diagnosis- teeeisstheeeeeee create pipeline is embedded within dynamic debiasing engine that leverages detector errors continuously refine the RS, en- suring the system remains adaptive and focused on the challenging biases throughout training. To tackle @, we replace ambiguous text prompts with a visual blueprint, i.e., canvases composed of colored rectangles that specify the class, size, and position of each object. This provides the genera- tive model with direct and unambiguous instructions on object relationships, occlusion, and instance identity, ensuring the precise synthesis of debiased samples. Next, we exploit the duality between L2I synthesis and object detection, where the output of one task naturally serves as the input to the other. On this basis, we form generative alignment mechanism that enforces consistency within an Laags-LLyyut-mmag
loop. This facilitates communication between the generator and detector by penalizing the detector when it produces layouts that are insufficient for faithful image synthesis.
Unlike frequency-based methods, our RS-driven debiasing strategy tackles limited sample diversity by completing the truly underrepresented data groups with samples featuring novel appearances; moving beyond conventional generative augmentation, bluepibt and generative alignment fa- cilitates precise synthesis of high-quality data targeting specific representation gaps. Consequently, our method demonstrates strong debiasing effectiveness. It establishes a new SOTA and greatly narrows the performance gap for underrepresented groups, e.g., +3.6 mAP for rare classes, +3.2 mAP for instances at image borders, +4.4 and 1.9 mAP for large and small objects on MS COCO. Our approach also demonstrates high generation fidelity, with the accuracy of layouts in synthesized images surpassing prior SOTA by 15.9 mAP, when compared against existing L2I synthesis models.
2 THE FREQUENCY TRAP AND FIDELITY GAP: A MOTIVATING STUDY
In this section, we conduct controlled experiments across three dimensions: spatial location, cate- gory frequency, and object size, to assess the influence of different data augmentation and debiasing strategies. All studies utilize Faster R-CNN (Ren et al., 2015)) with a ResNet-50 (He et al., 2016) backbone. Hyperparameters are kept identical across models. We first train the detector on a random 1/4 subset of the MS COCO training set. We then measure the mAP by enriching the 1/4 subset by factors of 4/3, 2, and 4 with: i) resampling (Gupta et al. 2019) rare data groups via standard data augmentation techniques like copy-paste, random flip, and crop (termed Data Aug); ii) bias- agnostic 1.21 synthesis to generate new samples using layouts from training sets (termed Bias-A iii) resampling rare data groups via 1.21 synthesis (termed Freq-Aware Gen); and iv) real samples from the remaining 3/4 training set (termed Real Data). Results are reported by mAP center, middle, outer for spatial location; mAP frequectnt common, for object category; and mAPiaggs normal, small for object size. Detailed definitions for metrics are provided in Appendix. Results are summarized in Fig. [
Gen);
Observation 1: Generative Debiasing Outperforms Traditional Augmentation, Yet Falls Short of a Complete Remedy. The Freq-Aware Gen strategy, which uses L2I synthesis to cre- ate new instances for rare data groups, consistently outperforms the Data Aug baseline across all dimensions. But when compared to models trained by Real Data, its performance still lags behind.
2

---

Published as a conference paper at ICLR 2026
mAp watt object location
Real Data + Data Aug
Certer
middle
oiter
MAP Win.. categgoryyy frequency
Real Data + Data Aug
frequent
common
rare
Real Data + Data Aug
large
namaal
small
0.35
Freq-lware
Blas-ágnostic
0.40
Blas-Agrostic Gen
Freqslware Gen
*
Blas-gnoztic Gen
Frequiwrre Gen
MAP w.r.t object
9.4
0.30
0.25
10.3
0.30
0.25
0.2
0.15
174
173
1/2
174
173
1/2
Data Scale
Data Scale
Data Scale
Figure I: Comparison of four data enrichment strategies with respect to object location, category frequency, and object size as the dataset scale increases from 1/4 by factors of 4/3, 2, and 4.
These results support our claim that Data Aug is "constrained by the visual vocabulary of the original dataset", leading limited diversity and improvement. The superiority of Freq-Aware Gen confirms that generation- based augmentation is a promising alternative. At the same time, its failure to match Real Data proves that current solutions are not the final answer.
ANALYSIS:
Observation 2: Frequency is an Incomplete and Potentially Misleading Proxy for Data Need. We observed that certain data-rich groups. such as large objects, benefited disproportionately more from additional samples in Bias-Agnostic Gen (+9.8 mAP) than Freq-Aware Gen (+8.1 mAP). This indicates that relying merely on frequency can lead to a suboptimal intervention.
This provides direct evidence for our claim that "Instance frequency is an incomplete proxy to determine the most needed data of model", and "Relying solely on frequency can result in suboptimal interventions". The Freq-Aware Gen strategy, by design, focuses its efforts on low- frequency groups (e-g., rare classes, small objects). While this yields modest gains in those specific areas, it overlooks larger opportunity for model improvement.
ANALYSIS:
Observation 3: Fidelity Gap Limits Generative Data Augmentation. While both Bias-Agnostic Gen and Real Data enrich the training set by adding new data that follows the identical biased distribution of the original 1/4 subset (i.e., not attributable to the layout choices or data distribution), the mAP gain from Real Data is consistently higher than that of Bias-Agnostic Gen.
ANALYSIS: Since the data distribution perfectly controlled, the performance gap can be directly attributed to the fidelity gap between synthesized images and real-world data. This finding supports our claim that "current 1,21 approaches struggle to render new samples faithfully". In this work, we will solve this problem from both the layout conditioning and generator training strategies.
Remark. Our empirical analyses confirm that while generation-base data augmentation is promising, current approaches fall short in two aspects. First, the suboptimal performance of the frequency-driven Freq-Aware Gen strategy demonstrates that instance frequency is an incomplete proxy for the representation needs of models. A more sophisticated diagnostic tool is required to identify the true data gaps. Second, the performance gap between Bias- Agnostic Gen and Real Data, which both share bias of the training set, reveals fundamental limitation in current synthesis control and fidelity. This suggests that even if we know what to generate, current layout-to-ine to-mage methods lack the precision generate it effectively.
PIOMPLDD DYNAMIC DEBIASING FOR OBJECT DETECTION
This section presents our generation- generation-based debiasing framework, which includes dynamic debias- ing engine ( to construct unbiased layouts guided by both frequency and sample diversity, and visual blueprint-prompter synthesis pipeline ($3.) 3.2 powered by generative alignment.
3.1 DYNAMIC DEBIASING VIA SCORING-DRIVER LAYOUT GENERATION
There are two core challenges in our generation- based debiasing strategy. First, we need to quanti- tatively measure the dataset biases inherent, which is the foundation for targeted debiasing. Second,
3

---

Published as a conference paper at ICLR 2026
Debiasing Engine
overall statistics
data
Object Detector (Do)
prodiction
Bias Diagnosis Engine Represeniation Score (RS)
Layout
RS-Driven Layoul
(Biased)
Recalitmaion
Generative Aligmment
Dynamic Debiasing
updare RS
recalibrared
Merged Data (De-Bisee))
layout
A
Layout Renderer
Syn. Data
recalihrated
layour & synthesired Image
(Go) 11-22
Visual Bluepriai
9000
Prooprinnt-rompplel 1.21 Synthesis
Inference
Generazive Alignment
- Dynamic Dehiasing
Figure 2: The overall pipeline of our framework, which 1) analyzes real data statistics to compute
the representation score (Eq. ( considering across frequency and diversity (Eq. 2) performs RS-driven layout recalibration (55 to sample target layouts for under-represented groups: 3) converts recalibrated layouts into visual blueprints (Bq 999.9100 1O), which provide pixel-level conditions for L2I generation (Bq 12 4) the process is constrained by duality-aware generative alignment (Eq 13
for feature consistency and error-based dynamic debiasing (Eg for adaptive RS updates.
the generated layouts for L2I synthesis should be both diverse and physically plausible, as naive randomization often produces unrealistic scenes that are unsuitable for model training.
Representation Score. We define representation score (RS) the quantitative proxy for how well a specific data group is represented in the dataset. Groups with low RS are under-represented and prioritized for debiasing. For object detection, the data group = (c, u) is set of bounding boxes with attributes including object class c, box size 8, and horizontal position и of box center. Considering that and continuous variables, discretize the image coordinates into M x M grid for position u, and categorize object areas into discrete K logarithmic bins for size s.
The sample frequency computes the empirical probability of instances in G occurring in an im- age: Dreq = N Ng)) where N (G) is the instance number of G and Nan is the number of all instances in the dataset. The analysis in $2 reveals that relying solely on instance frequency is insufficient, as even frequent groups can be underrepresented. Thus, RS moves beyond merely counting instances to integrate representation diversity, which comprises both visual and context di- versity. The visual diversity (9) is defined as the average feature distance between instances in G which captures intra-group visual variation, and context diversity Pax(9) reveals the co-occurrence between class and other classes::
Dix = |g|2
la, 0/11? ΣΣ TEG jEG
Dax(g)
Σ [Kd iETe(g)
(1)
[I(GG
IC|
where o extracted by the detector backbone after ROI pooling, Io(g) is the set of images containing class in group G, KG; is the set of classes in image and C is the set of all classes in the dataset. Finally, three components are combined into a representation score:
RS(G) = Direg (Dvis B. Dex
RS provides a robust measure of representation quality. Groups with low RS can then be targeted for generative debiasing, ensuring focused and effective correction of dataset imbalances.
Layout Recalibration. To preserve plausible object relations, we begin with a layout seeded from real im-
Imput Layout
Recalibrated layout
Recalibrated layout 1 age, and then perturb it under the guidance of RS, to fill the identified representational gaps. Instead of re- calibrating position and size indepen- dently, we treat them as coupled at- tributes of a data group G and resam- ple them jointly. For given object the seed layout belonging to group (c, 8, w), shown in Fig.
Size & Position
Categury
Recaliontt [54. 3-4)
Recalibrane
Original
Syn Pair
Sample
Data Marge
Original Image
Synthesizod Imagee Kayn
Figure 3: Illustration for layout recalibration.
4

---

Published as a conference paper at ICLR 2026
is shifted to a new target group G' = (c, ud') by sampling new size s' and position w'. The sampling probability is inversely proportional to the RS of the target group:
< u' c) (RS(c, 8', u²) +.)
where RS(c, s', u²) is the pre-computed RS for the group defnned by class size bin s', and position bin u'. The hyperparameter 7 controls the strength of the debiasing. On the other hand, to preserve the natural vertical layering (e.g.. sky above ground, cars roads), the vertical center o' of bounding box is only slightly perturbed from its original position v with a small Gaussian jitter:
o' = where e~ T~ = (o,))))
(4)
o2 is intentionally kept small to ensure that the vertical placement of objects remains faithful to their original context. This integrated layout recalibration approach is more powerful than treating each attribute in isolation, as it respects the complex dependencies between object properties.
To enrich the dataset with underrepresented object categories, the target class e' is chosen according to context-aware, RS- guided policy that balances contextual plausibility and representation gaps:
Telc' KC) x (1= E 1|c $ K.)
(RS(C) +e)
Context-Aware Term
RS-Guided Term
where K. is the set of classes already present in the scene. The context-aware term encourages adding instances of classes already present (x >> 1). RS(c') is the mean representation score for class d, averaged over all its size and position bins. Once target class c' is selected, we choose its specific size (s') and position (u') using the same inverse-RS sampling policy from Eq 3 ensuring the newly
added object fills the most needed representational gap for that class.
Error-Based Dynamic Debiasing. The representation score (RS) provides a strong foundation for bwar- layout recalibration, which further contributes to debiased object detection learning. However, since RS remains static throughout training, it cannot reflect the evolving bias of datasets enriched with newly generated data samples. To address this, RS should be dynamically updated to account for shifts in group-level representation qualities. Specifically, given the training procedure:
Ipeed
D& (Zsyn):
Esyn Go (Irecalii:
where brecalih is the layout after bias-aware recalibration, G& and D& represent generator and detec- tor, respectively. The training objective of the object detector is minimize the layout consistency loss (i.e., Llayou) between the predicted and the ground-truth recalibrated layouts. Crucially, the RS for each data group G. is refined using an exponential moving average with μ 0.99 that incorpo- rates the detection error Layou (i) for instance i within that group:
RS, = μ RS(G.) + (1 (4) (i).
14)
This establishes a dynamic debiasing mechanism, where Go is continuously steered to produce informative data mitigate emerging biases, ensuring a targeted and adaptive learning process.
3.2 HIGH-FIDELITY L21 SYNTHESIS WITH VISUAL BLUEPRINTS
Given a geometric layout 1 = {(bn, Gx) ) E TR Nx5 composed of N objects with corresponding bounding boxes b, [ 1n 2, Un
E IR4 and class labels cn E layout-to-image (L21)) synthesis (Zhao al., 2019 Zheng et al. 2023) aims to generate visually coherent images that respect the specified structure. A common solution in existing work (Chen et 2024a) Waag et al.) 2024] is serialize the layout 1 into token sequence s(I), which is then appended with text prompt y to form a unified conditional input ý = concat (y. s()). The training objective is to minimize the difference between true and predicted noise following Rombach al. (002))
L
E - ea (x 6, fe(())
(
where fo is the text encoder. Despite being straightforward, it suffers from a textual bottleneck caused by serializing 2D spatial arrangements into ID text sequence. This leads to ambiguity and imprecise spatial relationships. To overcome this, we introduce visual blueprint, a geometry- faithful alternative using pixel-space conditioning signals for unambiguous geometric guidance.
5

---

Published as a conference paper at ICLR 2026
Blueprint Construction. Given layout I, we construct visual blueprint Lond E "xxx3 where bounding boxes are mapped into colored rectangles indicating different instances using a rendering operator R (i.e., Fig. 4):
cond Rl: P).
Here, P= } ii color palette used differentiate object categories. To maximize the visual distinction of object classes, the colors in P are assigned evenly spaced hues on the unit circle in HSV space, which are subsequently converted to RGB values via:
P: = RGB (( 1) So, Vo),
(10)
where RGB(H,, is the standard HSV-to- RGB mapping, and &p is a fixed hue step. Satu- ration S and value V are set to I for maximum vibrancy. However, rendering only colored rectangles can result in information loss, par- ticularly in complex scenes containing overlap- ping or multiple instances of the same class. To address this, the rendering operator R follows three principles: i) to distinguish instances of the same class, the HSV value is decremented by small step for each subsequent instance; ii) objects are rendered in descending order of bounding-box size to prevent smaller objects from being fully occluded by larger ones; and iii) background objects are rendered with slight transparency, so as to provide the model with visual cues about overlapping relationships. It is worth noting that the binary maps used in ControlNet typically represent classes with adjacent inte- gers. This leads to low numerical variance for different classes and introduces potential ambiguity for the encoder (e.g., the Person class corresponds to (0, O, 0) and Sheep to (0, O, 19)). In contrast, we assign instance masks to equidistant hues on the HSV unit circle. This ensures distinct pixel values in RGB space, and provides high-variance signal that is far easier for the encoder to distinguish. visualization of this comparison provided in Fig of the Appendix.
Input I Layout L
Visual Blueprint cond
Blueprint Construction
(Eq. 9-10)
Geometry-Faiihfa
Control Injection (Bq. 12)
Sample
Denoiser
Syathesized Image Xayn
Control Module
Triinable
Figure 4: Illustration for blueprint construction.
Blueprint-Prompted Prompted Layout Conditioning, To integrate our blueprint Loond into the generation process, we require an architecture that can inject its rich spatial information into pre-trained U- Net without sacrificing its powerful generative priors. The adapter-based strategy proposed by /Zhang jet al.) (2023) is ideally suited for this setup. The blueprint is first projected into musti-resolution feature maps, go conod) via lightweight, trainable encoder ga. This provides an unambiguous, multi-scale structural prior that complements the global semantic guidance from the standard text prompt y. The model then learns to generate the image by minimizing our visual L2I objective:
1
L E E Exx,, t, fo( u)
(11)
These structural features w are then fused into the frozen U-Net F(: using a trainable copy F(:: and two inooiiiiil adapter blocks Z₁ and Zz:
y. - F(x; θ) + Z, (F + 2 (u); O.))
(12)
As such, we treat the pre-trained diffusion model powerful generative backbone and specialize for our debiasing task, guided by the unambiguous geometric information from our visual blueprint.
Duality-Aware Generative Alignment. Current generative frameworks treat the L2I generator and object detector as isolated components, leading to a misalignment where the synthesized image, though visually plausible, is not optimally aligned with the feature space of the detector. To bridge this gap, we propose an alignment strategy based on the duality of the two tasks.
Specifically, while the detector learns mapping from images to layouts (Ds x + 1), the generator leams the inverse (Gop + æz). We leverage this loop and propose an image- alige-tl loss C
ClA image =
|eo (2 I, fz(v)) u) - Eo (ar. i, fo()), u mrdd)
(13)

---

Published as a conference paper at ICLR 2026
where rnre is the multi multi-resolution feature maps constructed from the layout grod output by the detector Dz. The final training objective for optimizing the detector is given as:
LoD
Last A.
(14)
where Lae is the conventional object detection loss, À is balance factor. As such, L the detector for producing layouts that are insufficient for faithful image synthesis, and forces the detector to be robust to the features produced by the generator to deliver consistent predictions
image penalizes
4 RELATED WORK
Dataset Biases and Debiasing. Dataset bias occurs when training data is not representative sam- ples of the real-world scenarios. This misalignment causes models to learn dptaset- shortcuts instead of generalizable features (Torralba & Efros 2011 Geirhos et al. 2020). Efforts to mitigate dataset bias largely fall into two categories. Data- based strategies resample re- weight the training distribution to give more importance to rare instances (Cui et al. 2019 Cao et al. 2019). In contrast,
tearning-based strategies dynamically adjust gradients prevent common classes from dominating the learning process (Tan et al., 2020) Wang et al. 2021). In object detection, these biases manifest across axes like long-tailed category distributions where few classes dominate the dataset (Ouyang) et al.
2016), object size skew that favors normal and large instances over small ones (Herranz et al., Gilg al., 2023), and spatial bias where objects concentrate in center image zones (Zheng 2024). Accordingly, solutions commonly use resampling and re-balancing to enhance rare cat- egories (Gupta et al. 2019: Tan et al. (02T). scale-aware architectures to boost small objects (Lin) 2017: Singh & Davis 2018 Singh et al.) (018)), or copy-paste to increase the sample quan- (Ghiasi et al. (021). Despite the success, these approaches are primarily frequency-centric,
2016
et al.
eet al.
tities
treating instance counts the main proxy for biases. In this work, we propose generation- debiasing strategy, which contains a new image synthesis architecture, a bias- layout sampling strategy, and dynamic engine that adapts to evolving biases during training.
Controllable Diffusion Models. Diffusion probabilistic models (Sohl-Dickstein et al., 2015) have developed rapidly in recent years (Dhariwal & Nichol 2021: Ho & Salimans| 2022 Kingma al.; 2021 Rombach et al. 2022). Owing to their exceptional generation quality and controllability. diffusion models now become the dominant paradigm across a range of applications, including image editing (Brooks et al. 2023) Kawar et al.] al.] [202] Meng et al., 2221]] Hertz et al. (022), image- to-image translation (Saharia et al. [2022a] Tumanyan et al. 2023 Li et al. (023a). and text-to-
image (T21) generation (Nichol et al. [202]] Podell et al. 2023 Rombach et al. 2022 Saharia et al.)
20226 Li al. 2024a Jin et al. 2025), etc. Recent layout- layout-to-image to-image (I.21) synthesis (Zhao et al,,
2019 Li et a 2021 Yang et al. 2022] Sun & Wu 2019) aims at precise, instance-level placement by augmenting tre-tr T2I models with layout information (i.e., bounding boxes and category labels). Specifically, the layout is converted into text token sequence and then injected into pre-
trained T2I diffusion model (Cheng et al. 2023 Yang et al. 2023 Couairon et al. 2023) Xie et al.) 2023 Chen al. 2024b; Wang et al., 2025 Li et al. 2025 Cai et al. 2025). While this approach offers scalability, it introduces a textual bottleneck in which 2D spatial arrangements are converted
into 1D text sequences. Departing from this paradigm, our method encodes layouts in pixel-space as visual blueprint images. This provides the model with direct and unambiguous spatial and relational instructions guide the generation process with high fidelity and controllability.
Generation-Bar -ased Data Augmentation. Advanced strategies seek to enhance model generalization beyond simple resampling. Mixing-based techniques regularize model training by virtual samples created from interpolated images and labels (Zhang et al. 2018) or substituted regional patches YYun al. 2019). Erasure-ber based methods improve robustness by randomly masking image regions (De- Vries & Taylor 2017 Zhong et al.] (020). While label- lubee-preserving and simple to deploy. these meth- ods only recombine visual patterns already present in the training data, thereby constraining the diversity of generated samples. In contrast, recent work (Zhao et al. 20233 (Suri et al.] [023] Chen cet al. 2024a] Li et al., 2024b Xiang et al. (024) explores using synthetic data from generative mod- els to improve model performance. For example, X-Paste (Zhao et al.) 2023) scales
by synthesizing instances with diffusion models. Gen2Det (Suri et al. (023) leverages conditioned dif- fusion to directly synthesize scene-specific images. Layout-to-imaat -mooimmaee synthesis (Chen et al. 2024a Wang et al., 2024) reuses layouts in the training set and applies flip augmentation to synthesize ad- ditional samples for the detector. In contrast to these bias-agnostic approaches, this work introduces
7

---

Published as a conference paper at ICLR 2026
Table 1: Quantitative results for fidelity MS COCO
(Lin et al
2014 and Nulmages (Caesar et al.
2020
MS COCO
Nulmages
Model
Res.
FID
mAP 1 APso
APrs 1
FID
mAP
so
AP7s 1 Apm
AP
Real Image
48.9
68.3
55.6
48.2
75.0
52.0
46.7
60.5
TAMA et 2021
31.12
13.4
19.7
14.9
63.85
3.2
8.3
1.9
2.0
9.4
Taming (Jahn el al.
202
33.68
32.84
7.4
19.0
4.8
2.8
18.8
TwFA Yang et al 2022
2562
22.15
28.2
20.1
GeoDiftusion (Chen et al.
2024a
20.16
29.1
38.9
33.6
14.58
15.6
31.7
13.4
6.3
38,3
DetDiffusion Wang et al 2024
19.28
29.8
38.6
34.1
GDCC Cai et al. 2025
18.02
31.4
41.2
36.4
12.54
17.5
33.5
15.6
8,3
40.3
Ours
16.35
33.6
46.6
36.8
12.43
19.8
38.9
16.9
T0.8
43.2
ReCo Yang al. 2023
29.69
18.8
33.5
19.7
27.10
17.1
41.1
11.8
10.9
36.2
GLIGEN (1 al. 202.6
21.04
22.4
36.5
24.1
16.68
21.3
42.1
19.1
15.9
40.8
ControlNet (Zhang et al. 2023
512²
28.14
25.2
46.7
22.7
23.26
22.6
43.9
20.7
17.3
41.9
GeoDiffusion (Chen
a
18.89
30.6
41.7
35.6
9.58
31.8
62.9
28.7
27.0
53.8
GDCC Cai et at. 2025
17.15
32.6
43.6
38.0
7.97
33.6
64.7
30.7
28.6
55.9
Ours
15.24
46.5
61.4
51.6
8.35
40.2
70.1
38.2
38.4
58.0
Table 2: Quantitative results for dehiasing on MS COCO
[Lin et al.
2014
w.n.t. different attributes.
Model
center middle outer
freq
comm
rare
large 1 normal f small 48.1
Faster R-CNN (Bascline)
37.4
37.7
33.9
28.3
31.2
38.9
43.2
41.0
21.2
Bias Agnostic
Copy Paste (Chiasi al.
202
37,9
38.2
35.5
28.8
31.4
39.4
43.6
48.8
"41.5"
25.5
ControlNct Zhang
2023
36.9
37.3
33.4
27.6
30.8
38.3
42.9
49.0
40.4
19.8
GeoDiftusion (Chen et ZUZ4a
38.4
38.6
35.0
29.5
32.0
39.9
44.3
50.3
42.1
19.7
Frequency Aware
ControlNet Resampling
36.9
Jo5
33.4505
27.9
.04
60.2
37.7
43.2
48.6
40.3
1o.
20.1 TuLi
GeoDiflusion Resampling
38.5
38.5
35.3
+1.
30.0-17
31.6
39.4
44.5
-13
49.9
42,2
112
20.0
.1.2
Ours
40.3 229 40.5
36.9
31.5
33.3
46.8
52.5
43.8
23.1
a bias-aware data augmentation framework. We begin by systematically diagnosing dataset biases across key axes including spatial location, category frequency, and object size. Inspired by this anal- ysis, we design a bias-aware layout sampling strategy, ensuring that the generated data not only diverse but also precisely aligned with the goal of mitigating specific, pre-identified dataset biases.
5 EXPERIMENT
Experimental Setup. Following existing work (Chen et al. 2024a Wang et al. 2024), the validation contains two setups: Fidelity: which assesses the quality generated images by applying pretrained detection models to images synthesized from ground-truth layouts in the validation set, using the proposed 1.21 model. We report the Fréchet Inception Distance (FID) to assess generation quality and mean Average Precision (mAP) to measure detection performance. Debiasing: which evaluates the ability of generated data to mitigate biased distributions across data groups. The baselines are SOTA L21 models, which synthesize new training sets using annotations from real training samples, with layout augmentations limited to random flip and slight perturbation (i.e., bias- agnnssggss........ On this basis, construct frequency- aware variants by relaxing the layout augmentations include the resampling strategy Gupta et al. (2019) fregurreyycyaweeee
Finally, we compare them against our proposed dynamic-debiasing and visual prompted 1.21 synthesis approach. To evaluate debiasing effectiveness, we measure not only the overall mAP but also the performance across spatial positions (i.e., mAP (cenler.miädle..uute))) ), category frequency (i.e., mAP (frqqeett.ommmun.rrre) ). and object size (i.e., mAP (1 (large marmall ). For all experiments, unless otherwise specified, we employ the Faster R- CNN (Ren et al.) 2015) with a ResNet-50 backbone (He et al., 20166). More implementation details regarding network architecture, training, testing, and training objectives are provided in Appendix.
Dataset. Our proposed L2I synthesis model and corresponding debiasing strategy are evaluated on MS COCO (Lin et al. 2014) which provides 1 118K training and 5K validation images for over 80 ob- ject categories, and Nu Nulmages (Caesar et al. 2020) which is derived from the nuScenes autonomous driving benchmark, containing 60K training and 15K validation samples from 10 semantic classes.
5.1 EXPERIMENTAL RESULTS
Fidelity. Our approach achieves significantly higher performance in fidelity (Table Tb. surpassing
prior SOTA (i.e., GeoDiffusion (Chen al. 2024a)) by 15.9 mAP, 19.7 APso. 16.0 AP7s on MS
8

---

Published as a conference paper at ICLR 2026
Table 3: Quantitative results for debiasing Nulmages Caesar et al
2020
w.E. low www-errormming categories.
Model
mAP
outer
rare
large
small
trailer
const.
ped. f
cone t
Faster R-CNN (Baseline)
36.9
27.9
38.5
50.7
25.1
15.5
24.0
31,3
32.5
Bias Agnostic
Copy Paste (Ghiasi et al.
2021
37,5
28.6
38.8
51.5
25.3
16.0
24.7
31.5
32.7
ControlNet Zhang et al.
2023
36.4
27.6
38.3
51.2
24.4
13.6
24.1
30.3
31.8
GeoDiftusion (Chen et 2024a
38.3
28.4
39.6
52.4
25.3
18.3
27.6
30.5
32.1
Frequency Aware
ControlNet Resampling
36.5 10.4
27.9
10.4
38.5
10.4
51.0
n0.3
24.5
10.4
13.6
10.4
24.2
10.4
30.4
31.9
10.4
GeoDifuusion Resampling
38.3
28.8
10.9
40.0
10.5
52.0
113 25.4
10.3
18.0
12.5
27.5
ya.5
30.8
jo.5
32.3
to.8
Ours
40.0
31.5
13.6
42.5
:4.1
27.4
19.5
29.7
15.7 32.1
33.0
Stant 2l0re
Wiiinng
CATT slan GIDE BUMO
chair
Sining
Steing table
V200 glase
Input Layout
Recalibrated Layout Synthesized Image
Input Layout
GeoDiffusion
Ours
Figure 5: Visualization recalibrated layouts, showing objects with updated sizes and positions, new instances (left). Our method can generate geometry-faithife images compared to prior SOTA (right).
COCO, and 8.4 mAP, 11.4 APT 4.2 AP' on Nulmages, under the 5122 resolution. also yields much lower FID scores (i.e., 15.24 18.89 of GeoDiffusion MS COCO), verifying the effec- tiveness of our blueprint-prompped synthesis and generative alignment strategies.
Debiasing. As seen in Tables 2 bias-agnostic methods including copy-paste (Ghiasi al. 2021
ControlNet (Zhang et al., 2023). and GeoDiffusion (Chen et al. 2024a), boost performance broadly but ineffective for underrepresented groups, leading a modest enhancement in the final mAP. Meanwhile, integrating generative methods with the resampling strategy (Gupta et al.) 2019) of- fers certain improvement for underrepresented groups. Our approach, by targeting biases through both frequency and representation diversity, delivers substantial improvements across the board. not only achieves significant performance gains for underrepresented groups (e.g.. 28.3-331.5 for mAP outer. 43.2 13.2- for mAP Pare
MS COCO), but also sets new SOTAs for overall scores, achieving 40.3 and 40.0 mAP on MS COCO and Nulmages, respectively. The comprehensive results validate the overall design and confirm the powerful debiasing effectiveness of our method.
Qualitative Results. As shown in Fig. our method can adjust object sizes and locations, and even add new instances according to model needs. Moreover, it generate ecometry--aitthfe images with complicated layouts containing over ten instances, outperforming prior SOTA.
5.2 DIAGNOSTIC EXPERIMENTS
We conduct series of ablation studies on MS COCO, all under the Debiasing setup.
Essential Components. We examine the efficacy of essential components in Table After re- placing textual layout conditions with visual blueprints, the mAP enjoys large improvement (37.0-> 38.9), indicating the effective preservation of spatial cues. Generative alignment enjoys moderate improvements, as its primary role enhance the fidelity of generated images, rather than directly boosting detection performance. Meanwhile, RS-driven layout recalibration and dynamic debiasing also deliver satisfactory improvements, particularly benefiting underrepresented data groups.
Dynamic Debiasing. We ablate the momentum parameter for dynamic debiasing in Table g A value O, which updates RS using only errors from the current batch, leads to unstable training and poor performance. Conversely, μ disables the dynamic update and reverts to static RS. We found that μ= 0.99 achieves the best performance. This demonstrates a stable yet responsive update for RS to dynamically reflect the evolving representation quality and mitigate emerging biases.
Conditional Input. We evaluate the impact of layout conditions on both synthesis fidelity (Table 6) and debiasing performance (Table ref). As shown in Table 6 replacing textual inputs with Visual

---

Published as a conference paper at ICLR 2026
Table 4: Ablative studies of essential components in proposed method on MS COCO 2017 (Lin et al. 2014).
Table 3: Ablative studies of dynamic debiasing MS
COCO 2017 (Lin et al. 2014).
Method
mAPf
outer
rare
large
small
"4
MAPt
outer
rare 1
large f
smallf
Baseline
37.0
27.8
43.0
47.9
20.5
0
38.6
29.4
44.2
49.7
21.5
Visual Blueprint
38.9
29.6
45.0
51.1
21.9
0.9
40.0
31.1
46,2
51.9
23.0
Generative Align.
39.1
29.9
45.2
51.3
22.1
0,99
40.3
31.5
46.8
52.5
23.1
RS-Driven Recali.
39.9
31.0
46.4
52.3
22.8
0.999
40.1
31.3
46.4
52.0
22.8
Dynamic Debias.
40.3
31.5
46.8
52.5
23.1
39.8
31.0
46.4
51.6
22.5
Table 6: Ablative studies of Blueprint design for L2I
Table 7: Ablative studies of Blueprint design for dcbi-
synthesis on MS COCO 2017 (Lin al. 2014).
asing on MS COCO 2017 (Lin et 2014).
Method
FID., mAP 1 APs0
AP
Method
mAP
outer 1 rare " large 1 small
Bascline
28.14
25.2
46.7
22.7
Textual Layout
37.0
27.8
43.0
47.9
20.5
Pixel Canvas
20.15
40.8
56.2
40.5
Pixel Canvas
38.5
29.1
44.6
50.5
21..4"
Instance Discrim.
17.05
44.5
59.5
48.8
+ Instance Discrim.
38.7
29.4
44.8
50.7
21.7
Overlap Aware.
15.24
46.5
61.4
51.6
+ Overlap Aware.
38.9
29.6
45.0
51.1
21.9
Table 8: Ablative studies of representation score for
Table 9: Experiments of more detectors MS COCO 2017 (Lin et al.
layout generation on MS COCO 2017 (Lin et al. 20144).
2014).
Score
mAPf
rare 1 large
small
Delector
mAP
outer
rare
large
smallf
Bias- Agnnstic
39.1
29.9
45.2
51.3
22.1
YOLOX-S
40.5
31.2
44.5
53.1
23.5
Dine
39.3
30.4
45.8
50.9
22.55
Ours
43.3
34.5
48.1
56.2
25.1
Dreqq + Deis
39.7
30.7
46.3
52.0
22.6
DINO
49.0
40.5
50.5
64.0
31.4
Dreqq Dar
39.5
30.9
46.1
51.2
22.7
Ours
51.8
44.1
54,2
67.2
33.0
Dvs Dex
39.5
30.6
45.9
51.7
22.4
"OODDTT
52.0
43.5
33.5
67.-
34.8
Drog + Doic + Dex
39.9
31.0
46.4
52.3
22.8
Ours
52.6
46.8
57.2
70.0
36.3
Blueprints significantly improves generation quality (FID decreases from 28.14 14 to 20.15). Adding
instance discrimination and occlusion awareness further enhances fidelity. Crucially, Table 7 shows that this improved generation quality directly translates to better detection performance (mAP in- creases from 37.0 to 38.9), validating our blueprint design.
Representation Score. We probe the design of representation score (RS) in Table 8 Relying solely on sample frequency (Dreg) yields limited gains. Incorporating visual (Dvs)) or contextual diversity (Dax) alongside frequency notably improves performance (e.g., + Dax improves mAP from 39.3 to 39.7). The best performance is achieved with the full RS (Direg + Dvis Dax), demonstrating that comprehensive scoring of both frequency and diversity is essential for effective debiasing.
Generalizability. To verify the general effectiveness of our framework beyond Faster R-CNN, We extend the evaluation diverse modern detection paradigms, including YOLOX et al. [202I) DINO Zhang et al. (2022), and CO-DETR Zong et al.] (202)). As seen Table 9 our method consistently yields performance gains of approximately 2.6 2.8 mAP across all architectures.
Component Dependence. We investigate the interde- pendence between RS-driven debiasing (what to gen- erate) and visual blueprint (how to generate faith- fully) in Table TO As seen, pairing our debiasing engine with visual blueprint unlocks higher perfor- mance boosts (+1.4 mAP) than pairing with prior textprompted L21 generators (e.g., GeoDiffusion). This confirms that high-fidelity synthesis is the prerequisite for unlocking the benefits of targeted debiasing.
Table 10: Analysis on the interdependence be- tween debiasing and generation strategies.
Method
Generator mAP
Baseline
37.4
GeoDiffusion
Text 38.4
Scoring-Driven Debiasing
Text
38.7
Visual Blueprint (Ours)
Visual
38.9
Scoring- Debiasing
Visual 40.3
6 CONCLUSION
In this work, we demonstrate that instance frequency is an incomplete proxy for representation needs and existing L2I synthesis methods suffer from a fidelity gap. To overcome these challenges, we proposed a scoring-driven debiasing engine, which captures both sample density and diversity to recalibrate layouts for sample generation. Furthermore, we replace ambiguous text prompts with visual blueprints and integrate duality--aaare generative alignment strategy. This contributes to high-fide fideliiiltty and geometry-faithe faithful synthesis of targeted samples. Empirical results reveal a signifi- cant improvement in object detection performance and reduction in bias across data groups
10

---

Published as a conference paper at ICLR 2026
Acknowledgement. This work supported by Zhejiang Provincial Natural Science Foundation of China (No. LD25F020001), Fundamental Research Funds for the Central Universities (226- 2025-00057), Beijing Natural Science Foundation (L252036), the National Natural Science Foun- dation of China (No. 625061 16,, 62472222), Natural Science Foundation of Jiangsu Province (No. BK20240080), National Defense Science and Technology Industry Bureau Technology Infrastruc- ture Project (USZL2024606C00))) the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2025A02), and Jiangsu Provin- cial Scientific Research Center of Applied Mathematics (No. BK20233002).
Ethics statement. Our research utilizes publicly available datasets (MS COCO and Nulmages) and does not involve human subject studies or sensitive data. While image generative models can be misused, our framework designed for data augmentation. It aims to mitigate biases in widely used technologies such object detection, rather than to enable unrestricted image generation.
Reproducibility. The implementation details, including the network architecture, training objective, training and testing strategies, are provided in Appendix. Moreover, ensure the reproducibility of our proposed approach, the implementation code is publicly available at htttps://githhe com/ NUST Machinee ne- Intel Laaeencc-Labbaatooryy Berrattorryyeyyd_rreeeeee
REFERENCES
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004. 10934, 2020.
Tim Brooks, Aleksander Holynski, and Alexei Efros. Instructpix2pix: Learning to follow image editing instructions. In CVPR, 2023.
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: multimodal dataset for autonomous driving. In CVPR, 2020.
Xinhao Cai, Qiuxia Lai, Gensheng Pei, Xiangbo Shu, Yazhou Yao, and Wenguan Wang. Cycle- consistent learning for joint layout-to-image generation and object detection. In ICCV, 2025.
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. Learning imbalanced margin loss. In NeurIPS, 2019.
datasets with aabel-distributionee
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321-357, 2002.
Kai Chen, Enze Xie, Zhe Chen, Yibo Wang, Lanqing Hong, Zhenguo Li, and Dit- Yan Yeung. Geod- iffusion: Text-prompted geometrie control for object detection data generation. In ICLR, 2024a.
Minghao Chen, Iro Laina, and Andrea Vedaldi. Training-free layout control with cross-attention
guidance. In WACV, 2024b.
Jiaxin Cheng, Xiao Liang, Xingjian Shi, Tong He, Tianjun Xiao, and Mu Li. Layoutdif- fuse: Adapting foundational diffusion models for layout-to-image generation. arXiv preprint arXiv:2302.08900 2023.
Guillaume Couairon, Marlene Careil, Matthieu Cord, Stéphane Lathuiliere, and Jakob Verbeck. Zero-shot spatial layout conditioning for text-to-image diffusion models. In ICCV, 2023.
Yin Cui, Menglin Jia, Tsung- Yi Lin, Yang Song. and Serge Belongie. blassebllanced loss based on effective number of samples. In CVPR, 2019.
Terrance Vries and Graham W Taylor. Improved regularization convolutional neural networks with cutout. arXiv preprint arXiv: 1708. 04552, 2017.
Prafulla Dhariwal and Alexander Nichol. NewrIPS, 2021.
Diffusion models beat gans image synthesis. In
Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun. Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430, 2021.
11

---

Published as a conference paper at ICLR 2026
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11):665-61) 673, 2020.
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung- Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph. Simple copy-paste is strong data augmentation method for instance segmentation. In CVPR, 2021.
Johannes Gilg, Torben Teepe, Fabian Herzog, and Gerhard Rigoll. The box size confidence bias harms your object detector. In WACV, 2023.
Agrim Gupta, Piotr Dollár, and Ross Girshick. Lvis: A dataset for large vocabulary instance seg- mentation. In CVPR, 2019.
Haibo He and Edwardo A Garcia. Learning from imbalanced data, IEEE Transactions on knowledge and data engineering, 2109):1263-1284, 2009.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. CVPR, 2016.
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In ICCV, 2017.
Luis Herranz, Shuqiang Jiang, and Xiangyang Li. Scene recognition with cnns: objects, scales and dataset bias. In CVPR, 2016.
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. prompt-to-prome image editing with cross attention control. arXiv preprint wrXiv:2208.01620 2022.
Jonathan Ho and Tim Salimans. arXiv:2207.12590 2022.
Classifier-free diffusion guidance.
arXiv preprint
Manuel Jahn, Robin Rombach, and Bjöm Ommer. High-resolution complex scene synthesis with transformers. arXiv preprint S.Xi:22055006555 2021.
Xin Jin, Yichuan Zhong, and Yapeng Tian. TP-blend: Textual-prompt attention pairing for precise object-style blending in diffusion models. Transactions Machine Learning Research, 2025.
Justin Johnson, Agrim Gupta, and Li Fei-Fei. Image generation from scene graphs. CVPR, 2018.
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. In CVPR, 2023.
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho. Variational diffusion models. In NeurlPS, 2021.
Bo Li, Kaitao Xue, Bin Liu, and Yu-Kun Lai. Bbdm: Image-to-image translation with brownian bridge diffusion models. In CVPR, 2023a.
Bonan Li, Yinhan Hu, Songhua Liu, and Xinchao Wang. Control and realism: Best of both worlds in layout-to-image without training. arXiv preprint arXiv:2506.1556 2025.
Liulei Li, Wenguan Wang, and Yi Yang. Human-object interaction detection collaborated with large relation-driven diffusion models. In NeurIPS, 2024a.
Ming Li, Taojianman Yang, Huafeng Kuang, Jie Wu, Zhaoning Wang, Xuefeng Xiao, and Chen Chen. Controlnct++: Improving conditional controls with efficient consistency feedback. arXiv preprint arXiv:2404.07988 2024b.
Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang. Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. CVPR, 2023b.
Zejian Li, Jingyu Wu, Immanuel Koh, Yongchuan Tang, and Lingyun Sun. Image synthesis from layout with locality-aware mask adaption. In ICCV, 2021.
12

---

Published as a conference paper at ICLR 2026
Tsung- Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014.
Tsung-Yi Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. CVPR, 2017.
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun- Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint 10Xiv2208,,0103: 2021.
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021.
Wanli Ouyang, Xiaogang Wang, Cong Zhang, and Xiaokang Yang. Factors in finetuning deep model for object detection with long-tail distribution. In CVPR, 2016.
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann. Tim Dockhorn, Jonas Müllrr, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 2023.
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In NeurlPS, 2015.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In CVPR, 2022.
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: image-to-image diffusion models. In SIGGRAPH, 2022a.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Bureu Karagol Ayan, Tim Salimans, et Photorealistic text-to-image diffusion models with deep language understanding. In NeurlPS, 2022b.
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open lcrge-scale dataset for training next generation image-text models. In NeurlPS, 2022.
Bharat Singh and Larry S. Davis. An analysis of scale invariance object detection: Snip. CVPR, 2018.
Bharat Singh, Mahyar Najibi, and Larry S. Davis. Sniper: Efficient multi-scale training. In NeurIPS, 2018.
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In ICML, 2015.
Wei Sun and Tianfu Wu. Image synthesis from reconfigurable layout and style. In ICCV, 2019.
Saksham Suri, Fanyi Xiao, Animesh Sinha, Sean Chang Culatana, Raghuraman Krishnamoor- thi, Chenchen Zhu, and Abhinav Shrivastava. Gen2det: Generate detect. arXiv preprint arXiv:2312.04566. 2023.
Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie
Yan. Equalization loss for long-tailed object recognition. CVPR, 2020.
Jingru Tan, Xin Lu, Gang Zhang, Changqing Yin, and Quanquan Li. Equalization loss v2: A new gradient balance approach for long-tailed object detection. In CVPR, 2021.
Antonio Torralba and Alexei Efros. Unbiased look at dataset bias. In CVPR, 2011.
Brandon Trabucco, Kyle Doherty, Max Gurinas, and Ruslan Salakhutdinov. Effective data aug- mentation with diffusion models. In ICLR, 2024.
13

---

Published as a conference paper at ICLR 2026
Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel. Plug-and- aodg-and-lay diffusion features for text-driven poageett-imaage translation. In CVPR, 2023.
Jiaqi Wang, Wenwei Zhang, Yuhang Zang, Yuhang Cao, Jiangmiao Pang, Tao Gong, Kai Chen, Ziwei Liu, Chen Change Loy, and Dahua Lin. Seesaw loss for long-tall long-tailed instance segmentation. In CVPR, 2021.
Ruyu Wang, Xuefeng Hou, Sabrina Schmedding, and Marco F Huber. Stay diffusion: Styled layout diffusion model for diverse layout-to-image generation. In WACV, 2025.
Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou, Yingjie Cai, Lanqing Hong, Zhenguo Li, Lihui Jiang, Dit- Yan Yeung, Qiang Xu, et al. Detdiffusion: Synergizing generative and perceptive models for enhanced data generation and perception. In CVPR, 2024.
Weijia Wu, Yuzhong Zhao, Hao Chen, Yuchao Gu, Rui Zhao, Yefei He, Hong Zhou, Mike Zheng Shou, and Chunhua Shen. Datasetdm: synthesizing data with perception annotations using diffu- sion models. In NeurIPS, 2023.
Shuai Xiang, Pieter M Blok, James Burridge, Haozhou Wang, and Wei Guo. Doda: Adapting object detectors to dynamic agricultural environments in real-time with diffusion. arXiv preprint arXiv:2403. 18333444..1, 2024.
Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, and Mike Zheng Shou. Boxdiff: Text-to-image synthesis with training-free box-constrained diffu- sion. In ICCV, 2023.
Zhengyuan Yang. Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, et al. Reco: Region-controlled text-to-image generation. In CVPR, 2023.
Zuopeng Yang, Daqing Liu, Chaoyue Wang, Jie Yang, and Dacheng Tao. Modeling image compo- sition for complex scene generation. In CVPR, 2022.
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: Regularization strategy train strong classifiers with localizable features. In ICCV, 2019.
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.0366 033605, 2022.
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz. mixup: Beyond empiri- cal risk minimization. In ICLR, 2018.
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-image diffusion models. In CVPR, 2023.
Bo Zhao, Lili Meng, Weidong Yin, and Leonid Sigal. Image generation from layout. In CVPR, 2019.
Hanqing Zhao, Dianmo Sheng, Jianmin Bao, Dongdong Chen, Dong Chen, Fang Wen, Lu Yuan, Ce Liu, Wenbo Zhou, Qi Chu, et al. X-paste: Revisiting scalable copy-paste for instance segmen- tation using clip and stablediffusion. In ICML, 2023.
Guangcong Zheng, Xianpan Zhou, Xuewei Li, Zhongang Qi, Ying Shan, and Xi Li. Layoutdiffusion: Controllable diffusion model for layout-to-iman generation. In CVPR, 2023.
Zhaohui Zheng, Yuming Chen, Qibin Hou, Xiang Li, Ping Wang, and Ming-Ming Cheng. Zone evaluation: Revealing spatial bias in object detection. IEEE TPAMI, 46(12):636-8651, 2024.
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmen- tation. In AAAI, 2020.
Zhuofan Zong, Guanglu Song, and Yu Liu. Detrs with collaborative hybrid assignments training. In CVPR, 2023.
14

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 USE OF LARGE LANGUAGE MODELS (LLMS)
We confirm that LLMs were used solely for minor grammatical corrections and phrasing sugges- tions. They were not involved in providing research ideas, including motivation, algorithm design, or the development of the core method. Furthermore, they were not used in generating any scientific content, such as the introduction, methodology, experimental results presented in this paper.
A.2 METRIC DEFINITION
For spatial location, we partition images into center, middle, and outer regions, each covering 33% of the image area, and then compute mAP for bounding boxes whose centers fall within correspond- ing regions, yielding cePeenter. miclle. and mAP For object category, we group the 30% most occurring categories as frequent, 30% least occurring categories as rare, and the remaining as common, yielding mAPfrequent- mAP and common For object size, we group the objects with the size of bounding box larger than 96x96 as large, smaller than x2x32 small, and the remaining as normal, yielding mAPiarge mAPsmal small, and mAP normall
A.3 EXPERIMENTAL SETUP
Training. For all detection experiments, unless otherwise specified, we employ the Faster R- CNN (Ren et al.) 2015) with a ResNet-50 backbone (He et al., 2016). The models are trained following the standard X schedule using a batch size of 16 and an initial learning rate of 0.02. For debiasing experiments, we merge the debiasing datasets with the original training sets into a unified training set. The L2I synthesis model is built upon Stable Diffusion (Rombach et al. 2022). pre- trained on LAION-5B (Schuhmann et al. 2022). The model is first trained for 100,000 iterations on 256x256 resolution images. The resulting checkpoint is then used to initialize the 312x512 model, which is subsequently fine-tuned. Both resolutions use a batch size of 16 and a constant learning rate of 1e-5.
Testing. To assess generation fidelity, we adhere to the protocol established in prior work (Li et al. 2021 Chen et al. 2024a). For MS COCO, we filter the validation set include only images contain- ing 3 to 8 objects, resulting in a split of 3,097 images, which are then evaluated using a pre-trained YOLOv4 detector (Bochkovskiy al. 2020). For Nulmages, the validation set is filtered to images with no more than 22 objects, yielding a total of 14,772 images, which are evaluated using Mask R-CNN HH et al. 2017). Test-time augmentation disabled for all evaluations.
Training Objective. For L2I synthesis models, we optimize it with the Lvisunl defined in Eq. I1 while for object detection, we optimize the detector with Lod defined in Eq,
Quantization of Representation Score. For box size we discretize object sizes into small, medium, and large three categories following the standard MS COCO definitions (Small: area < 322; Medium: 322 < area < 962; Large: area > 962). For horizontal position we nor- malize the horizontal center coordinate (0, 1]. and discretize it into = 10 uniform bins, to ensure each group G contains statistically significant samples for RS calculation.
Synthesized Debiasing Dataset. To facilitate a fair comparison with prior L21 synthesis methods, the scale of generated debiasing samples is aligned with the original MS COCO and Nulmages training sets, comprising 120K/60K images and 840K/540K instances, respectively.
A.4 ADDITIONAL ANALYSIS
Ablation on Recalibration Strategy. We examine the effectiveness of bias-aware layout recali- bration in Table ST A bias-agnostic strategy, which randomly recalibrates layouts, yields modest improvements across metrics. In contrast, targeting biases along single attribute leads to large improvement for its corresponding metric but only modest gains for others. Our full strategy, which jointly considers all attributes for layout recalibration, achieves the best overall performance.
15

---

Published as a conference paper at ICLR 2026
Table S1: Ablative studies of recalibration strategy for layout generation on MS COCO 2017 (in et al.
2014
Attribute
mAP
outer
rare
large f
small
Bias- -gnnstte
39.1
29.9
45.2
50,4
22.3
Position
39.4
30.9
45.6
50.6
22.7
Size
39.6
30.0
45.6
51.8
21.9
Category
39.5
30.1
46.3
50.6
22.6
All
39.9
31.0
46.4
52.3
22.8
Ablation on Hyperparameter 3. We further investigate the impact of the hyperparameter | in Eq. 2 which balances the weight between visual diversity and context diversity. As shown in Ta- ble S3, when 5 = O, the context diversity term Dax is disabled. The performance remains stable as varies, achieving optimal results at 3 1.0, which adopted as our default setting.
Table S2: Ablative study of hyperparameter / in Eq.
Table S3: Stability analysis over independent runs.
3 Value
0.0
0.5
1.01
1.5
2.0
Method
Mean mAP1
Std Dev.
AP outer
30.4
30.7
30.9
30.8
30.5
w/o Alignment
40.1
0.32
mAP
39.7
39.8
39.9
39,9
39.7
w/ Alignment
40.3
0.06
Analysis of Generative Alignment. We further analyze the impact of duality-aware generative alignment training stability. Synthetic data often introduces stochastic artifacts that vary with random seeds. Without alignment constraints, the detector risks overfitting to these artifacts, leading high variance across runs. The alignment loss (Linage) acts structural anchor enforce feature consistency. To verify this, conducted three independent training runs with different random seeds. As shown in Table S4, the inclusion of generative alignment reduces the standard deviation (from 0.32 to 0.06), which demonstrates improved robustness against generative noise.
Debiasing. In Table S4 we present a comparison of our proposed method against more bias- agnostic generative data augmentation approaches. As shown, our method outperforms prior work across all metrics, further demonstrating the effectiveness of our design.
A.5 VISUAL BLUEPRINT.
Standard simple class maps typically represent categories using adjacent integer IDs, leading to low numerical vari- ance. For example, the Person class corresponds to RGB values (0, O, 0) and Sheep to (0, 19). As shown in the left panel, these numerically proximate values appear visually indistinguishable (near-black), providing a weak signal that Vi-
persoe
Icheep
person
Sheep
may ambiguity for the encoder. In contrast, sual Blueprint projects class labels onto the HSV unit circle maximize signal separation. This results in significantly Figure 6: Comparison of layout repre- distinct pixel values: Person is rendered as (255, 19, 0) and sentations. Left: Simple class map with Sheep (255, 215, 0). As shown in the Fig. 6 this ap- low numerical variance. Right: Our Vi- proach creates high-variance, unambiguous signal that is sual Blueprint with high-variance RGB significantly easier for the encoder to discriminate.
Simple Class Map
Visual Bluuepeint
signals for better discrimination.
A.6 COMPUTATIONAL COST ANALYSIS
The computational cost our framework consists of three components: (i) debiasing engine, which computes representation scores involves only basic statistical operations on low-dimensional lay- out data, introducing negligible overhead to the pipeline; (ii) blueprint-prompped L2I synthesis, which involves the Layout Renderer and the L2I Generator. The Layout Renderer is computation- ally lightweight, while the L.1 Generator (fine-tuned via adapters) requires training time compara- ble to ControlNet (Zhang et al., 2023). In implementation, training the generator MS COCO (112x512) takes approximately 74 GPU hours. In contrast, prior generation based augmentation SOTA like GeoDiffusion needs 640 GPU hours for training.
16

---

Published as a conference paper at ICLR 2026
Table S4: Quantitative results for debiasing MS COCO with more bias-agnostic L21 synthesis methods.
Lin
2014 and Nulmages Caesar al. 2020
MS COCO
Nulmages
Model
mAP
30
1
TP 1
AP" 1 AP
MAP
car 1 truck
bus 1 ped. 1 conet
Faster R-CNN (Baseline)
37.4
58.1
40.4
41.0
48.1
36.9
52.9
40.9
42.1
31.3
32.5
Bias Agnostic
LostGAN Sun
2019
35.6
51.7
39.6
41.3
30.0
31.6
LAMA Li al. al.12220
2021
51.7
40.5
30.0
31.3
Taming Jahn ci al. 2021
51.9
39.3
41.1
30.4
31.6
ReCo Yang et al. 2023
36.1
52.2
40.9
418
29.5
31.2
L.Diffusion Zheng et a
2023
36.5
57.0
39.5
39.7
47.5
D.Diffuse Cheng el al 202.1
36.6
57.4
39.5
40.0
47.4
GLIGEN Li al
202.b
36.8
57.6
39,9
40.3
47.9
36,3
52.8
40.7
42.0
30.2
31.7
NontrolNet Zhang et al.
2023
36.9
57.8
39.6
40.4
49.0
36.4
52.8
40.5
42.1
30.3
31.8
GeoDifusionn (Chen et
2024a
38.4
58.5
42.4
42.1
50.3
38.3
53.2
43.8
45.0
30.5
32.1
Frequency Aware
ControlNet Resampling
36.9
57.8
39.7
47.6
36.5
53.1
40.3
41.6
30.4
31.9
GeoDiffusion Resampling
38.5
58.6
42.4
42.2
49.9
53.3
39.8
44.6
30.8
32.3
Ours
40.3
61.0
44.0
43.8
52.5
40.0
55.1
46.5
47.1
32.1
33.2
A.7 QUALITATIVE RESULTS
Category Frequency. We present spider chart in Fig. ST] illustrate the improvements achieved for various categories. As seen, our method yields substantial performance gains in these categories.
Class Distribution. Fig. S3 visualizes the instance distributions with respect to different class categories. While the original dataset (blue) exhibits severe long-tail bias, our generated data (red) effectively supplements the tail region. This targeted enrichment flattens the overall distribution, confirming that strategy successfully mitigates class imbalance.
Visualization. We provide three sets visualizations to demonstrate the effectiveness of our ap- proach in layout recalibration, geometry-faithful generation, and debiased object detection. First, Figures S2 $4 illustrae the recalibrated layouts based on representation scores ($3.I), which effec- tively adjust object positions and sizes as needed, and generate new objects of desired categories. Next, Figures S5 show that our method can generate geometry- faittuul images from conditional layouts. In contrast, GeoDiffusion (Chen et al., 2024a) fails to render complex scenes with multiple objects. Finally, these advancements lead to superior detection performance (i.e., Fig. S7). where our method delivers significantly more precise detection results.
suitcase (+4,6%)
(+4.7%)
snowboard (14.6%)
knife (-5.1%)
spoon
6.8%)
(+3.2%)
hot dog (+5.35)
umbrella (+4.0%)
refrigerator (+3.45)
baseball bat 5%6)
skateboard
vase
(+4.6%)
skis
(+7.6%)
toaster
Real Only
Real Syn
Figure S1: Spider chart illustrating improvements in mAP across various categories.
17

---

Published as a conference paper at ICLR 2026
Numbor of Instances (Log Scale)
Original Generated (Ours)
Figure S3: Visualization of class distribution.
Algorithm 1 Visual Blueprint Construction
Require: Layout set = {(b,, ci) ) total classes Nate transparency factor a, decrement step 8
Ensure: Visual Blueprint Loond
1: Toond + zero-initialized image of size x W x 3
2: counts +- hash map initialized to 0
3: Sort L based on area of b; in descending order
Handle occlusion: larger objects first
4: for i 1 to N do
5:
(b,c) + T.(
6:
h (c+ (c+1)/N
Inter-class discrimination via Hue
7:
r + counts
8:
v + max(0.2, x 8) // Intra-class distinction via Value decrement
9:
counts c) t co +1
color HSV2RGB (h, 1.0,v)
11: ROI + Leona(b) (b)
12: Ioond [b] - a color + (1 a) ROI Occlusion-aware transparency blending
13:
end for
14:
return Leond
18

---

Published as a conference paper at ICLR 2026
jefeggmaoe
Jefigerstre
boild
krite
bowi
UFFE
busll
candsoch
howe
Restaan
Imi
Input Layout
Original Image
Recalibrated Layout
Synthesized Image
Figure S2: Visualization results for layout recalibration based on representation scores 3.1 and
L2I synthesis using our proposed visual blueprint-prompted prompted method ($3.2
19

---

Published as a conference paper at ICLR 2026
06g
Semsi
SEAE
diniing GA0K
Staing
giotonpyele
elephant
elepbaaee
elephant clephart
Slephant elephank
elephont alephink skpphaii
elephase
Input Layout
Original Image
Recalibrated Layout
Synthesized Image
Figure S3: Visualization results for layout recalibration based on representation scores 3.1 and
L2I synthesis using our proposed visual blueprint-prompted method ($3.2
20

---

Published as a conference paper at ICLR 2026
13M
piizn
NOCK
truck
bal
hal
bock Book
pers0
hed
Iraliic
bus
Truck
Irain
Truck
mick
TLUC
Truck
car
Input Layout
Original Image
Recalibrated Layout
Synthesized Image
Figure S4: Visualization results for layout recalibration based on representation scores 3.1 and
L2I synthesis using our proposed visual blueprint-prompted method ($3.2
21

---

Published as a conference paper at ICLR 2026
bcols
book
godted glani
potted plasi
Biok
patied
Staiii
c0
Eooai
tv
laplop
keplop
teddys
laptop
keyboard
laptop
Cup
key
RAOK
mouse
KeуboaT
EXI
arylaze
emmmm
airplas
arriing
J38
arplane
sign
affe Ight
Input Layout
Original Image
GeoDiffusion
Ours
Figure S5: Comparison against GeoDiffusion under the Fidelity setup MS COCO, where the L2I synthesis model should generate fcithery-faithful images conditioned on given layouts.
22

---

Published as a conference paper at ICLR 2026
Sale
gland
WIDEU aline
TLEE elan
FiVe
Jesife
slock
Neysle
lire byshani
Bining mebl
banana
lphp
Aeybeam
atrin
Input Layout
Original Image
GeoDiffusion
Ours
Figure S6: Comparison against GeoDiffusion under the Fidelity setup MS COCO, where the L2I synthesis model should generate faithtry-faithful images conditioned on given layouts.
23

---

Published as a conference paper at ICLR 2026
unhell
Faster R-CNN
Enhanced Faster R-CNN
Faster R-CNN
Enhanced Faster R-CNN
Figure S7: Visualization results for object detection MS COCO under the Debiasing setup.
24


Reviews:
[{"review_id": "Vxhsc6rmIS", "reviewer": "Reviewer_8Zww", "summary": "This paper presents a novel debiasing framework for object detection, named \"Beyond Frequency: Scoring-Driven Debiasing for Object Detection via Blueprint-Prompted Image Synthesis.\" The key contributions are:\nRepresentation Score (RS): A new metric that goes beyond mere frequency counts to diagnose representational gaps in the training data by considering both sample density and representation diversity.\nVisual Blueprint-Prompted Image Synthesis: A method that replaces ambiguous text prompts with precise visual blueprints to guide the generation of high-quality, unbiased images.\nGenerative Alignment Strategy: A mechanism that fosters communication between the detector and generator, ensuring high-fidelity synthesis of debiased samples.\nDynamic Debiasing Engine: A system that continuously refines the RS based on detector errors, ensuring adaptive and targeted debiasing throughout training.\nThe method significantly improves the performance for underrepresented object groups and achieves state-of-the-art results in terms of both debiasing effectiveness and generation fidelity.", "questions": "1. What is the computational cost of the proposed methods compared to existing techniques? Are there any trade-offs in terms of training time or resource requirements?\n2. Could the authors provide visualizations of the class distributions in both the original and generated datasets? This would help in understanding how the proposed method alters the distribution to achieve debiasing.\n3. Could the authors provide more details on how the parameters of the representation score (e.g., β, τ) were chosen? Were any experiments conducted to analyze their sensitivity?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces a novel approach to diagnosing and addressing dataset biases by integrating representation scores with visual blueprints and generative alignment. This method is innovative as it overcomes limitations found in existing techniques.\n2. The research is characterized by rigorous experiments and comprehensive analysis. The proposed methods are well-implemented, validated, and demonstrate excellent performance improvements.\n3. The contributions are significant as they tackle a major challenge in object detection by providing a practical solution that enhances model performance and fairness.", "weaknesses": "I appreciate the great efforts for this paper with clear motivation, thoughtful analyses, detailed strategies and comprehensive evaluations. Even so, after carefully considering the contributions of this work, I have some main concerns on the insights the paper conveys, besides some concerns on details in the paper.\n1. *The insights*\n  - a) The study for the motivation in Section is too empirical and heuristic. Those observation and analyses mainly focus on the performance comparison. The performance gains/gap motivates this work. This idea is somewhat reasonable. But, it is strongly suggested to provide deeper analyses with qualitative evaluation. E.g., do object detectors need those generated images that are not always well generated by models? What fresh impacts or findings those generated images would bring to detectors, in comparison with conventional augmentation methods? What generated images are more valuable to object detectors?\n\n  - b) Because of a), although this paper is well written, it would be an unconscious misguidance to the main idea proposed in this paper: the layout generation and L2I generation. The core work of this paper is to focus on the L2I generation and there is very few discussions on the object detectors, e.g., only “duality-aware generative alignment” returns to the main task-object detection. If so, why not clearly introduce the work of L2I? Overall, I) I appreciate the whole work, but I sincerely suggest that we can make a different effort for more insights and more essential findings on the research topic, not just performance gap or gains that motivate us. II) For the concerns aforementioned, it is not easy to response or well address in this paper. But, if the authors have more insights or analyses (e.g., mentioned in a)), we can discuss in the rebuttal stage.\n\n *Concerns on technical details:*\n\n2. The paper could benefit from a more detailed analysis of the sensitivity of the representation score's parameters (e.g., βin RS(G) = Dfreq(G) · (Dvis(G) + β · Dctx(G)) .). Understanding how these parameters affect the results would provide more insight into the robustness of the method.\n\n3. The paper does not discuss the computational cost of the proposed methods. Given the complexity of the generative models and the dynamic debiasing engine, it would be useful to include an analysis of the computational requirements and potential trade-offs.\n\n3.\tFor the layout generation, it is hard to ensure the accurate and plausible generation results. So layout recalibration is important. But, the strategy in Line180-185 and even the part of “Layout recalibration” worth more considerations.\n\n4.\tThe layout generation and L2I generation is not always perfect. It is evitable that some negative impacts they make. Thus, it is strongly suggested that those impacts and analyses can be truly provided and analyzed. Those analyses would be a valuable suggestion or lesson for other researchers.\n\n5.\tSome details are suggested to provide. E.g., 1) Line263-267: it is kind to explain the more details of the implementation for the reproduction.", "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": "HnLFxUO5ln", "reviewer": "Reviewer_yyBg", "summary": "This paper proposes a generation-based debiasing framework for object detection. It claims that frequency is an incomplete proxy and previous methods lacks fidelity. Then it designs a representation score to replace frequency, this score can quantify how well a concept is represented across both sample density and representation diversity. This paper also presents a visual blueprint to replace text prompts so that offer a clear instruction to improve the fidelity.", "questions": "See weakness above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The performance is good. The improvement is substantial compared to previous methods, such as GeoDiffusion.\n2 . This method is simple yet effective. The two main contributions, the representation score and visual blueprint, are easy to understand and significantly improve the performance.\n3. The motivation is clear. The observations in Sec. 2 explain why frequency is not enough and fidelity is important.", "weaknesses": "1. Lack a figure to describe the overall pipeline in detail. Figures 2 and 3 are used to explain the layout recalibration and blueprint construction; however, there is no figure to explain the complete pipeline, which can be confusing.\n2. The effect of Generative Alignment is negligible. Can you explain its necessity?\n3. There is no connection between the two contributions in this paper, which makes the paper an incremental industry-focused work rather than a cohesive academic study.", "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": "3yfhEIMOBA", "reviewer": "Reviewer_sxvq", "summary": "This paper rethinks the effectiveness of generated data for training object detectors, and proposes the representation score (RS) as a better metric than frequency to select more beneficial layouts, followed by layout recalibration. To enhance the generation quality, the authors propose to utilize the visual blueprint as the task prompt and then design a new loss term to enhance detector-generator dual awareness. Extensive experimental results demonstrate the effectiveness of the proposed method.", "questions": "In Figure 4 (right), 1st row and 2nd column, the `dining table` class name is missing.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The problem setting is fairly clear, including enhancements for both layout and image generation.\n- The preliminary exploration of layout frequency is extensive.\n- The authors conduct experiments on both fidelity and trainability on both COCO and NuImages.", "weaknesses": "- About Representation Score:\n  - In line 161, considering that the box size s and horizontal position u are both continuous, do you conduct any quantization to construct the RS group?\n  - In Sec. 2, the authors conduct extensive experiments to demonstrate that frequency is not the best metric for layout selection, which, however, cannot directly connect with the complicated definition of RS in Equ. 1.\n  - Considering `Freq-Aware Gen` is still a solid baseline, I would expect to see a comparison between it and the complicated RS, with or without the more complicated RS calibration.\n- About Visual Blueprints:\n  - Besides comparing with the textual prompting of GeoDiffusion, a more direct comparison would be using ControlNet, but the visual prompt is a simple class binary map (i.e., a map of size HxWxC, and the area belonging to class C will be 1 in the C-dimension). It seems that this simple baseline is quite similar to the visual blueprint.\n  - Duality-Aware Generative Alignment is proposed to enhance the detector instead of the generator. Do I understand correctly?\n- Overall, this paper studies an interesting problem. The first half is good, but the solution is mostly based on intuition. Considering the complexity of the proposed method, a more detailed step-by-step ablation is necessary. I would like to review the author's rebuttal to inform my final decision.", "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": "Y8er542c4h", "reviewer": "Reviewer_d5R9", "summary": "This paper proposes a generation-based debiasing framework for object detection that goes beyond frequency-based methods. The authors introduce a Representation Score (RS) to quantify representation quality by combining instance frequency, visual diversity, and contextual diversity, enabling targeted layout recalibration to synthesize unbiased data. To improve synthesis fidelity, they replace textual layout prompts with visual blueprints that provide unambiguous spatial guidance and propose a duality-aware generative alignment mechanism to align the detector and generator. Experiments on MS COCO and NuImages demonstrate substantial gains in both detection performance and generation fidelity, establishing a new state of the art in debiasing for object detection.", "questions": "1. Using the Visual Blueprint as the control condition is exactly the same as in ControlNet. So why is the performance of ControlNet + Resampling so poor?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivating study in Section 2 is carefully designed and convincingly demonstrates the “frequency trap” and “fidelity gap.” This provides a solid empirical foundation for the proposed approach.\n2. Extensive experiments on MS COCO and NuImages show large and consistent improvements. The ablation studies are thorough, demonstrating the contribution of each component.\n3. The proposed framework integrates multiple innovations:\n(i) an RS-driven layout recalibration module,\n(ii) a visual blueprint-prompted synthesis mechanism, and\n(iii) a duality-aware generative alignment strategy.\nTogether, these components form a well-engineered pipeline with clear rationale and internal consistency.", "weaknesses": "1. The experiments focus primarily on Faster R-CNN + ResNet-50. It is unclear whether the proposed method generalizes to other detection paradigms (e.g., DETR, YOLOv11). Low performance caused by data bias may not be significant in these modern models.\n\n2. When the number of categories is large (e.g., 1000 classes), the colors representing different categories in the Visual Blueprint become highly similar, leading to confusion in the generated features. Therefore, compared with layout-based image generation methods, this approach is only suitable for scenarios involving a limited number of categories.\n\n3. While RS integrates frequency, visual, and contextual diversity, the weighting and combination (Eq. 2) seem heuristic. No formal analysis or ablation on hyperparameter sensitivity (e.g., β) is provided.\n\n4. The paper is dense, with long mathematical sections and multiple notations introduced in quick succession. Some figures (e.g., Fig. 2 and Fig. 3) could be clearer in explaining how each component interacts dynamically during training.", "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)