ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection
Authors: Haowei Zhu
Tianxiang Pan
Rui Qin
Jun-Hai Yong
Bin Wang
Venue: NeurIPS 2025 spotlight ()
Keywords: diffusion model; object detection; data augmentation
Primary area: deep_learning

Abstract: The scale and quality of datasets are crucial for training robust perception models. However, obtaining large-scale annotated data is both costly and time-consuming. Generative models have emerged as a powerful tool for data augmentation by synthesizing samples that adhere to desired distributions. However, current generative approaches often rely on complex post-processing or extensive fine-tuning on massive datasets to achieve satisfactory results, and they remain prone to content–position mismatches and semantic leakage. To overcome these limitations, we introduce ReCon, a novel augmentation framework that enhances the capacity of structure-controllable generative models for object detection. ReCon integrates region-guided rectification into the diffusion sampling process, using feedback from a pre-trained perception model to rectify misgenerated regions within diffusion sampling process. We further propose region-aligned cross-attention to enforce spatial–semantic alignment between image regions and their textual cues, thereby improving both semantic consistency and overall image fidelity. Extensive experiments demonstrate that ReCon substantially improve the quality and trainability of generated data, achieving consistent performance gains across various datasets, backbone architectures, and data scales.

ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection
Haowei Zhu', Tianxiang Pan', Rui Qin' Jun-Hai Yong', Bin Wang 1,3 TTinghha University LLi Auto Inc. *BNRist
wangbins@tsinghua cn
Abstract
The scale and quality of datasets crucial for training robust perception models. However, obtaining large-scale annotated data is both costly and time-consuming. Generative models have emerged as powerful tool for data augmentation by synthesizing samples that adhere to desired distributions. However, current gener- ative approaches often rely on complex post-processing or extensive fine-tuning on massive datasets to achieve satisfactory results, and they remain prone to con- tent-position mismatches and semantic leakage. To overcome these limitations, we introduce ReCon, a novel augmentation framework that enhances the capacity of cruuturr--oontolllbb generative models for object detection. ReCon integrates region- -ooonnguieddd rectification into the diffusion sampling process, using feedback from a pre-trained perception model rectify misgenerated regions within dif- fusion sampling process. We further propose region-aligned cross-attention to enforce spatial-semanti alignment between image regions and their textual cues, thereby improving both semantic consistency and overall image fidelity. Exten- sive experiments demonstrate that ReCon substantially improve the quality and trainability of generated data, achieving consistent performance gains across var- ious datasets, backbone architectures, and data scales. Our code available httpss/ggghbbb.oommwwwwi//////
Introduction
Robust object detection and instance segmentation models are essential in modern computer vision (Bochkovskiy et al.) 22020 Carion et al.) 2020: Zhu et al. 22000] Liu al. (024). However, these models are highly dependent on large- scale. meticulously annotated datasets whose creation is
expensive and time consuming (Barkai al. 1993 Cherti al., 2023). For instance, annotating
single image in the Cityscapes dataset can take up to 60 minutes (Cordts et al. 2016). Consequently, there is pressing need for efficient and automated methods to synthesize high-quality annotated training data.
Data augmentation has emerged as vital strategy to alleviate data scarcity by increasing sample diversity and improving model generalization. Traditional augmentation methods (Zhong al. 2020 De Vries & Taylor 2017, Yun et al., [019 Cubuk al. 2018 Dvornik et al. 2018) typically introduce only minor local variations, falling short of generating truly novel content. Recent advances in generative modeling, especially structurally controllable frameworks, offer a promising alternative by leveraging Canny edges (Zhang et al. 2023 Zavadski et al. 2024), spatial layouts (Chen et al.
2023 Wang et al.) 2024b), instance masks (Wang al. 2024a Wu al. 2023
maintain
fine-grained control during image synthesis.
"Corresoonding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Structurally controllable generative models have achieved remarkable progress in geometric manipu- lation and are now extensively applied object-detection data augmentation (Fang al. 2024 Li
et al. 2025). One family of methods repaints original images using universal control models such as ControlNet (Zhang et al.
2023] Zavadski et al. 2024) or inpainting models (Rombach al. 2022
Lugmayr et al., )022). hese pipelines are often complex, requiring extra post-filters to remove noisy outputs (Fang et al. 2024) or multiple sampling processes (Kupyn & Rupprecht, 2024) to generate an image, for example, Kupyn & Rupprecht (2024) generating new samples for a single image containing ten objects via ten separate diffusion samplings.
Moreover, fine-tuning diffusion models conditioned on layouts or masks provides feasible way for precise end-to-end synthetic data generation for object detection. Recent works demonstrate that such generated datasets yield strong trainability in downstream tasks (Chen et al., 2023 Wang et al., 2024b). However, these approaches typically require fine-tuning on large-scale datasets, which incurs significant computational overhead and remains impractical when data are scarce, which is a common scenario in data augmentation tasks. Furthermore, these approaches often struggle with complex layouts, leading to mis-generated regions and semantic misalignment.
To address these challenges, we propose Region-Controllable (ReCon) data augmentation. By integrating region-wise rectification and alignment directly into the diffusion sampling process, ReCon enhances single-pass control over instance synthesis. Without any additional training, ReCon significantly improves consistency between generated content and its annotations. It should be noted that we are not claiming to introduce a novel structural-contr) generation framework. Instead, our method can, without any additional training, enhance the quality of object detection data produced by existing structural controllable generation models. Specifically, our method first performs Region- Guided Rectification (RGR), in which we detect mis- giieeeenedd regions by comparing the sampled image against ground-truth annotations using an off-the-shelf grounding model and then rectify those areas by injecting noisy real data points. By applying rectification to areas susceptible to be mis-generated, we boost the accuracy without sacrificing content diversity. Next, we introduce Region- Aligned Cross- AttsstAAtention (RACA) to mitigate semantic leakage. This mechanism aligns region-specific visual tokens with their corresponding textual descriptions (or other cues) during generation. By enforcing tight correspondence between image features and text embeddings at each diffusion step. ensures precise semantic fidelity in the output.
By incorporating these two components into every sampling iteration, ReCon provides fine-grained region control: region-guided rectification preserves spatial agreement with annotations, and region- aligned cross-attention guarantees semantic adherence. The resulting high-fidelity augmented samples significantly enhance downstream object-detection performance. For example, when combined with Canny-edge conditioned ControlNet model, our method achieves superior performance COCO dataset compared to models that have been specifically fine-tuned on COCO. In addition, our method demonstrates high augmentation efficiency: in data-scarce settings, tripling the dataset with our approach outperforms a sevenfold increase achieved by the bascline. Our contributions are as follows:
the
We propose ReCon, novel region cogtoolcontrollable data augmentation method that enhances the regional control capabilities of existing models without requiring additional training.
We introduce region-guided rectification and improve control ability during the diffusion sampling process.
cross- aoss-attention mechanisms to
Extensive experiments show that ReCon generates high-quality augmented data and substan- tially improves detection performance compared to both traditional augmentation techniques and current generative approaches.
Related Work
Conditional Generation Models. Recent advances in generative modeling have enabled the synthesis of high-fidelity images. Early research predominantly focused on Generative Adversarial Networks
(GANs) [Goodfellow et al.) 2020) for image generation. Conditional GANs, for example, were utilized to train classification heads, thereby demonstrating the capability of GANs to model data distributions and generate novel samples an unsupervised manner (Gurumurthy et al. 2017 7
Antoniou et al. 2017 Mariani et al. 2018 Zhang et al. 2021 Li ct al. 2022a Zhao & Bilen
2022
2

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Xu et al. 2023). However, GANs are often plagued by training instability, mode collapse, and limited controllability, particularly in low-data regimes.
Diffusion models have recently emerged as a robust alternative, offering enhanced controllability and adaptability. These models implement reverse denoising process that gradually removes noise from an initial Gaussian distribution to approximate the real data distribution (Yang et al. 2023a). Moreover, diffusion models can effectively handle a variety of conditioning inputs, including lext, images, layouts, edges, depth maps, points, and masks. This flexibility has enabled their application to a wide range of tasks such as exxt-to-image synthesis (Podell et al. 2023 Esser
et al. 2024). image editing (Meng et al. [222]] Rombach et al. (0222) image inpainting (Lugmayr al. 2022 Saharia et al. (022), and data augmentation (Fang et al.] 2024 He et al. (022). For instance, LAMA (Li et al., (202T) proposed a large mask inpainting strategy to enhance image quality, while Taming Transformers (Esser et al. (020) demonstrated that training in a latent space can outperform more complex baselines. Further innovations include GLIGEN (Li et al., (023c)) which incorporates gated self-attention for improved layout control, and LayoutDiffuse (Cheng et al. 2023), which employs layout-specific attention modules tailored for bounding box guidance. Additionally, methods like GeoDiffusion (Chen et al. 2023) and Instance Diffusion (Wang et al.) 2024a) integrate geometry-aware modules to encode spatial features, leading to superior generation outcomes. DetDiffusion [Wang et al. 2024b) introduces a perception-awe aware loss to effectively bridge
the gap between generation and perception.
In this paper, we exploit these advanced, controllable generative models to produce high-quality synthetic data without extra training, with the goal of enhancing downstream detection tasks.
Generative Data Augmentation. Recent advancements in generative models (Rombach et al. 2022 Esser et al. 2024 Tian et al. 2025) have paved the way for synthesizing high-fidelity images that introduce novel content beyond the capabilities of traditional augmentation techniques (Cubuk et al.) 2020 2018: Yun et al. 2019: Chen et al. 2020). This enhanced data diversity is instrumental in improving the training perceptual networks for tasks such as object detection.
Initial studies employed GANs (Goodfellow et al. 2020) for data augmentation. However, subsequent research has revealed several limitations of GAN-basee approaches. For example, training networks like ResNet50 (He et al., 2016) on data synthesized by models such as BigGAN (Brock et al. 2018) often results in suboptimal performance compared to training with real images. Moreover, the inherent instability in GAN training and the difficulty of generating data under complex conditions (Bansal &
Grover 2023 Gowal et al. 2021 Ravuri & Vinyals (219)) further constrain their effectiveness.
In contrast, diffusion models offer superior controllability and have gained widespread application in data generation. For image classification, methods such as LECF (He et al., (022)) use GLIDE (Nichll jet al. 202T) to generate images and subsequently filter out low-confidence samples to enhance zero- shot and few-shot performance. Similarly, SGID (Li et al. (223a) leverages BLIP (Li et al. 2022b to
ensure semantic consistency in generated outputs. Feng et al. (2023) filters samples based on feature similarity, while techniques like GIF (Zhang et al. 2022 and DistDiff (Zhu et al. (024) incorporate additional guidance during the sampling process to refine the quality of generated samples. For object detection, recent methods such as GeoDiffusion (Chen al. (2023) and DetDiffusion (Wang et al.) 2024b) have demonstrated the ability to synthesize high-quality images with precise layout control, specifically designed for training detection models. Additional strategies include using diffusion models with post-filterigg based on category-calibrated CLIP scores (Fang et al. (022) and applying background inpainting augment training data without extra annotations (Li et al. 2025)
Morcover, synthetic data has shown promise in other domains as well. For instance, MagicDrive (Gao et al. 2023) highlights the benefits of synthetic samples for 3D perception tasks, while TrackDiffu- sion et al. al, (2033)) focuses on data generation for multi-object tracking. X-Paste (Zhao et al. 2023)
and MosaicFusion (Xie et al. 2024 further contribute by producing samples with clear segmentation boundaries to boost instance segmentation performance.
Despite these advances, most current methods either require additional training of generative models or struggle to balance fidelity and diversity. In this work, we develop the generative data augmen- tation framework for object detection by leveraging emerging zero-shot recognition models (e.g.. GroundedS AM (Ren et al. 2024g) alongside versatile conditional generation models (e.g., Stable Diffusion (Rombach et al.
2022 and ControlNet (Zhang et al., 2023)). Our approach eliminates the
3

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Step1: Prepare text embedding and instance masks
Step2: Diffusion sampling with region control
Class Prompt: Person; Snowbouts board; beat
Region A Alinned tlentio
c Text
Frozon
Context Prompt: An Image with person, snow board beat.
Encoder
R
1
10:00 Structural Control Diffusion Model
Region- Guided Rectification
+
Grounded + SAM
B
RG
Zeed
4
Figure 1: Overview of the ReCon Pipeline. ReCon enhances object-detection data generation by integrating region control into frozen, off-the-shelf models. It first compute the text embedding and instance masks, and then leverages a structural controllable diffusion model as the data generator and introduces region-guided rectification refine generated results during the sampling process. Additionally, region-aligned cross-attention is incorporated to mitigate semantic leakage. Our method plug-and-pla and-play and can integrated with existing iinettre-conttolllbbee models.
need to retrain generative models, which often impractical in data-scarce scenarios, and instead focuses simplicity and effectiveness in generating task-specific data for target detectors.
3 Method
Task Definition. This study enhances a downstream object detector through data augmentation. By leveraging pre-existing generator with original image æ, bounding boxes B, and class labels y. aim generate high-fidelity augmented dataset where objects appear within the specified B and carry the correct labels y. The primary challenge to preserve fidelity to the source while introducing useful novel variations (e.g., colors, styles, object poses) to increase content diversity and thereby improve downstream model performance.
Overview. Existing methods often face trade-off between diversity and fidelity in generating downstream data. Recent works improve data diversity by using in in-painting techniques to preserve certain image regions while redrawing others (Li et al. 2025 Ma et al. 2024a). Others improve fidelity by using perceptual models like CLIP (Radford al. 2021 filter out low-confidence samples (Fang et al., 2024 Zhao et al. 2023). In this paper, we propose novel approach that utilizes an off- the-shelf perceptual model to adaptively calibrate image content during sampling, achieving a better balance between diversity and fidelity.
As illustrated in Figure T our method builds upon existing structural control models (e.g., ControlNet) to establish an initial layout control. During the sampling process, we employ region- region-guided guided rectification strategy that refines instance-level content by automatically filtering out erroneous or low-confidence samples. Additionally, we introduce region-aligned cross-attention mechanism to facilitate effective interaction between image content and the corresponding textual features.
3.1 Preliminaries
Stable Diffusion is a generative model that synthesizes high-quality images from textual prompts by operating within a compressed latent space. comprises forward and reverse diffusion stages.
Forward Process. In the forward process, noise is gradually added to the latent representation Zo of image x, turning it into pure Gaussian noise zr after timesteps. This diffusion process is modeled by:
g(z 2(-1) = N N Va 21-1, (1 (1-ae) a4)
where 0g controls the balance between the previous latent state and the injected noise.
Denoising Process.
Starting with 27 (pure Gaussian noise), the model iteratively predicts and removes noise to generate the clean latent Zg. This reverse sampling process modeled by:
(0 2.) = -N(2= (N Mo 1), E. (1).
(2)

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where 9 is the denoising network, (a( t) and Ee (t) denote the predicted mean and covariance. Sequentially applying this reverse process from T down t effectively removes the noise, recovering for final image generation.
Cross-Attention Mechanism. In text-to-image generation, the text condition v, (encoded by text encoder like CLIP) is integrated into the latent space via cross-attention:
QK Vd
Attention Q K, V) softmax
V
where the query Q is derived from image features, and K and V derived from text embeddings. Here, dy denotes key dimension. This mechanism injects semantic text information into the latent features at each denoising step, guiding the image generation to reflect the text prompt.
During sampling, noisy latent representations progressively denoised while being continuously influenced by the text embeddings. Starting the denoising from different timesteps allows control over the editing intensity, balancing adherence the original image content. However, since the Stable Diffusion model lacks inherent structural control, additional structure-controllable models are required for generating object detection data.
3.2 Region-Controllable Data Augmentation
Structural Control with ControlNet. ControlNet (Zhang et al. 2023) enhances diffusion models (e.g., Stable Diffusion) by conditioning them structural cues such edge, depth, pose maps. integrates trainable control layers as follows:
+ 7 ControlBlock 0.)
where 3₁ are the latent features at layer I, C. is the structural conditioning map, O, are learnable parameters of control blocks, and y scales the control signal.
In our work, we use ControlNet with an edge canny map to enforce structural constraints during image generation, and demonstrate that our approach generalize other layout-to-image models for diverse guided generation.
Region-Guided Rectification. Existing generative models often encounter issues such as generating an incorrect number of target objects or unintended ones. These challenges significantly affect the quality of the generated data. To address these problems, propose a region-guided rectification method aimed at perceiving image content during sampling and applying region adjustments. This approach ensures consistency of the content and the layout.
Ry
Sompling
Grounded SAM
V2
Zapp-M))
Fa(cew)
RV
RVV
JKV
LAV
DDLM
Reetificatoo
Egg
Wgr
CA
CA
CA
CA
Paste
Forward
10
to
1u
fo
Crup
R
R
R
z!
Figure 2:
Sampling process with Region- Guided Rectification. We first identify incor- rectly generated regions by IoU matching detec- tion results with the original annotations using grounding model. Next, we derive rectifica- tion mask M and use z, orig sampled from the original data point to correct these errors.
Figure 3: Pipeline of our Region-Alignes Cross- Attention. We first crop region-specific features from z using predefined regions Rgt. For regions belonging to the same category, per- form cross-attention with the corresponding text features. Finally, the interacted region image features are concatenated to produce z.utt
As shown in Figure given an image annotation comprising multiple bounding boxes B and their corresponding labels y, we employ the Grounded-SAN model (Ren et al. 2024) to detect potential objects in the data point z, during the sampling process. We then apply loU-based matching to identify false positives and false negatives, allowing to segment regions that are potentially misgenerated.
5

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These out-of-contree regions are defined by a binary mask M. The identified regions are then replaced with their corresponding noised versions z, sampled from the original image, while the remaining parts of 2 are preserved. This region-guides rectification process can be formulated as:
2 M 2t
orig + (1-M)- 3 Z1,
where @ denotes the element wise multiplication, z represents the rectified latent data point, and
orig is the latent point obtained after applying 1 steps of noise addition the original image using 2,
Equation T This method leverages the intrinsic overridability property of diffusion sampling (Levin & Fried 2023 allowing regions in intermediate images to be replaced with external content drawn from the same distribution. Such rectifications can influence the final generated image without disrupting the overall inference process.
However, directly detecting the intermediate latent point Z₁ with a perception model is impractical due to the challenge of finding pre-trained detector that provides meaningful guidance when the input is noisy. To address this issue, we leverage recent cache-based diffusion acceleration method (Ma et al. (024b) to speed up sampling over N steps (with N = 5 by default). Furthermore, we utilize the capability of the diffusion model to predict the noise added to 2,-N. enabling the
prediction of clean data point 20jz-N- N- This process is formulated in Equation 6
ZO1t NI-N
-I-a0 (2,0
(6)
Va
Then we apply region rectification at T, timesteps (T, 4), corresponding to the early (0.75 T), middle (0.50 7"), latter (0.25 2) and final (0.10 T') stages of the diffusion process. At the early stage, when the overall object layout has begun to emerge, we can correct inaccuracies in the spatial distribution of objects. During the middle stage, incorrect semantic content in the objects. Finally, in the latter and final stages, we refine regions with suboptimal generation quality. More details of the sampling process are presented in Algorithm
the object starts to take shape, we rectify any
Region-Aligned Cross Attention. In text-to- -male generation, semantic leakage often occurs, where the content of target regions does not align with the actual textual descriptions. To address this issue, we introduce region-alignes cross attention to mitigate information leakage across regions.
Since attention within the text encoder operates on all prompt tokens, and these tokens may belong to different categories, interference between sapegory-ppecific features can occur (see the appendix Figure 8 To address this, we individually encode C' textual features for C' targett categories using prompts in the format: [CLASS], as shown in Figure [ Additionally, we employ a global context description to represent the overall scene, which interacts with the background region. For datasets like COCO, we can directly utilize the provided caption annotations or simply use a custom prompt, such as: "An image with two cars and three persons".
Next, as presented Figure we perform cross-attention interactions between the corresponding object regions and their associated textual features. This step alleviates information leakage in prompt descriptions by ensuring that region-specifer textual features influence only their respective regions. An alternative approach to implementing region-aware cross-attention is to use cross attention mask to suppress text features from unrelated categories, as proposed in (Xue et al. 2023). However, we have observed that, due to the lack of disentanglement during the encoding of textual features from different categories, the masked attention mechanism still suffers from semantic leakage. Besides, Instance Diffusion introduces an instance-mase masked attention and fusion mechanism to integrate region- specific conditions with corresponding visual tokens. However, it relies on additional region-speciff modules and requires retraining achieve satisfactory performance. In contrast, our approach mitigates the problem of semantic leakage and enhances the fidelity of generated images without the need for additional fine-tuning. Furthermore, demonstrate that method can be effectively combined with Instance Diffusion to further boost its performance, as shown in Table TableTaand and Figure g
Experiments
4.1 Experiment Settings
We evaluate our method by augmenting downstream object detectors with synthetic samples. We use Stable Diffusion v1.5 (Rombach et al. 2022) with 25-step DDIM sampler (Song et al. 2020) and

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ddge-conditionedd ControlNet (Zhang et al. 2023) to generate training images. These samples are combined with the original trainset and used to jointly train object detectors. We implement training and evaluation code based on the MMDetection framework (Chen et al.) 2019). For consistency with prior work (Wang et al. 2024b). our default detector is Faster R-CNN (Ren et al. 2015) with an R-50- FPN backhone trained for six epochs. We also evaluate our method with diverse detectors including RetinaNet (Lin et al. 2017), ATSS (Zhang et al., 2020), FCOS (Tian et al. 2019). YOLO-X (Ge) et al. 2021). and DEIM (Huang et al. (022).. Following previous works, we select images containing 3 to 8 objects for data generation, resulting data set comprising 47,200 images with 227,406 objects. For VOC benchmark, with evaluation performed on the VOC 2007 test set (4,952 images). We use mAP (mean Average Precision), mAR (mean Average Recall), FID to evaluate the performance. Extensive experiments are conducted across various datasets, backhone architectures, and data scales.
combine the training sets of VOC 2007 and VOC 2012 for model training,
4.2 Main Results
Compared with State-of-the-arr Methods. We compare our approach with state-of-the of-toh--aaeeee art structure- controllable generative diffusion models, as summarized in Table The results demonstrate that our method significantly enhances the effectiveness of structure-guides guided techniques for object detection data augmentation. Specifically, we evaluate both general-purposs control methods (e.g., ControlNet) and models fine-tuned on COCO (e.g., DetDiffusion). When combined with these methods, approach further improves their performance and establishes new state of the art. For instance, integrating ReCon with ControlNet yields a mAP of 35.5, surpassing GeoDiffusion' 's 34.8. Moreover, our method can act as a plug-and-pl requiring additional training. This is exemplified by the improvement in GLIGEN's mAP from 34.6 to 35.5. These findings validate that our approach enables the generation of higher-quality training samples, resulting in substantial boost in object detection performance.
enhancement for region-controlled diffusion models without
Table 1: Comparison with existing generative models on the COCO dataset. ReCon enhances the detector performance by integrating existing methods in training-free manner. The best results highlighted in bold, while the second-best outcomes are denoted by underlined italic.
Method
mAP
APsa
AP7s
APM
AP'
Real only
34.5
55.5
37.1
37.9
44.3
General Control
Layout Diffusion Zheng al.
2023
[CVPR23]
34.0
54.5
36.5
37.2
43.6
ControlNet (Zhang et al. 2023
[CC323)
34,9
55.5
37.7
38.2
45.5
Background marantnnn (Li et al
2025
[ECCV24]
35.1
55.1
37.7
38.2
45.8
ControlNet-X XS (Zavadski et al. 2024
[BCCV24]
35.1
55.8
37.6
38.6
45.0
b Fine tuned on COCO
ReCo Yang et al.
2023b
[CVPR23]
33.6
$3.2
36.2
36.7
44.0
GLIGEN (Li
2023c
[CVPR23]
34.6
S5.1
37.2
38.1
44.7
GeoDiffusion (Chen et al.
2023
[CLR24]
34.8
55.3
37.4
38.2
45.4
DetDiffusion Wang ct al.
2024b
[CVPR24]
35.4
55.8
38.3
38.5
46.6
Instance Diffusion (Wang et al.
2024a) [CVPR24]
35.0
55.4
37.6
38.4
45.7
ControlNet + ReCon
35.5
56.2
38.4
39.0
46.0
GLIGEN + ReCon
35.3
56.0
38.1
38.7
45.8
Instance Diffusion ReCon
35.6
56.0
38.4
39.0
46.4
Data-Scarce Scenarios. Data augmentation is crucial when training data is limited. To evaluate our approach under such conditions, we conduct experiments three data-scarce regimes by randomly sampling 1%, 5%, and 10% of the COCO training set and then doubling each subset through augmentation. Our method delivers consistent gains over baseline approaches in all regimes. As shown in Table 2 with only 10% of the data, mAP rises from 18.5% to 21.7%. Training- based generative models often struggle in data-scarce settings due to their dependence on large datasets. In contrast, we employ generic stracture-controlled diffusion model (ControlNet) to produce high- quality object detection samples. We further compare our method to traditional augmentation method Rand Augment (Cubuk al. 2020). Although RandAugment shows noticeable improvement, it
7

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remains inferior to our approach. Moreover, combining our method with RandAugment produces additional improvements. demonstrating compatibility with standard augmentation pipelines.
Few-Shot Scenarios. We also evaluated our method in a 30-shot training setting on YOLOX-S (Ge al. 2 using COCO dataset, following the few-shot split protocol of previous work (Wang et al. 2020 Our method performs well even under the few-shot setting, increasing the mAP from 5,4 to 6.7 and 6o from 10.3 to 12.3. More few-shot results are presented in Table [] in Appendix.
Table 2: Comparison in data-scarce scenarios across varying data proportions.
Method
1%
5%
10%
Real only
0.3
13.0
18.5
RandAugment
3.1
16.2
21.4
ControlNet
2.5
15.9
21.2
Recon
3.9
16.7
21.7
Recon + RandAugment
4.2
17.1
22.0
Comparison on Different Dataset. To validate the generalization capability of our method, we conducted additional experiments on PASCAL VOC datasets. We compared our approach against a baseline Faster R-CNN detector trained with 1x schedule. As demonstrated in Table traditional data augmentation methods like RandAugment (Cubuk et al. 2020) show limited effectiveness, while simply duplicate the original dataset leads to overfitting (76.2 vs. 77.1 mAP). Our method achieves superior performance (78.5 mAP) through synthetic generation of diverse high-fidelity images that maintain crucial semantic features.
Table 3: Comparison results on the PASCAL VOC dataset.
Method
Real only Simple Duplicate
RandAugment
ControlNet
ReCon
mAP
77.1
76.2
77.7
77.8
78.5
Data Scaling. To assess scalability we measure detection accuracy in low-data regimes (5% and 10%), summarized in Fig- 4 Repeating training data (real expan- sion) provides consistent improvements up to 3x: mAP increases from 13.0 to 17.1 on the 5% subset and from 18.5 to 21.1 on the 10% subset. However, further duplication (5x, 7x) leads to saturated performance and noticeable degradation, indicating over- fitting under extended training. By contrast, method generates diverse, annotation- consistent samples and continues to yield performance gains without overfitting. As the expansion scale grows, the relative accuracy improve- ments from our augmented data become more pronounced. Overall, our approach attains comparable or better performance than the ControlNet bascline using substantially fewer augmented examples, highlighting its efficiency for data augmentation.
se.cooo
coco
Real only Keal Exparson
Roal Expandoon
Contyale
BuCat
19
Data Scaling Factor
Daca Scalling Factor
(a) 5% Data
(b) 10% Data
Figure 4: Data scaling on different COCO subsets.
4.3 Ablation Studies
Effectiveness of Each Component. We conducted ablation experiments assess the contributions of each component in our proposed framework, as presented in Table The results clearly indicate that each component enhances the performance of the downstream model. In particular, the integra- tion of region- -guided rectification (RGR) and region-aligned cross attention (RACA) significantly improves the consistency between the generated samples and their corresponding annotations, thereby elevating the quality of the synthesized data. Consequently, our approach increases the baseline mAP from 34.9 to 35.5 and improves the FID from 13.82 to 12.85, demonstrating enhanced trainability and fidelity of the generated samples.
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Table Ablation results for different components of our pro- posed method.
Table 5: Performance comparison of different perception targets.
RGR
RACA
FID
mAP
AP50
AP
Apm
AP
Target
mAP
AP50
AP75
x
x
13.82
34.9
55.5
37.7
38,2
45,5
35.0
55.6
37.8
x
13.21
35.3
56.0
38.1
38.6
45.6
Soje
35.3
55.8
38.2
12.85
35.5
56.2
38.4
39.0
46.0
Fo((-N)
35.5
56.2
38.4
Table 6: Trainability comparison with DEIM-
Table 7: Comparison with different region- guided models.
D-FINE-N (Huang et al. 2024) on COCO.
Method
mAP
AP50
AP7s
mAR
Method
mAP
AP50
AP75
Real only
38.5
55.2
41.5
60.4
Swin-Tiny
35.5
56.2
38.4
ControlNet
39.1
55.8
42.1
60.6
Swin-Base
35.6
56.2
38.7
ReCon
39.8
56.6
42.5
61.0
Perception Target. Our method leverages cache-based fast sampling (Ma et al.) 2024b) to recover clean Zo. providing more accurate control signal for region-guided rectification. we compare different perception targets: Tt. Poje, and [o](t-N)- As shown in Table S While æ; yields modest gains due to low recall which in causes the model favor lower overall editing strength. In contrast, employing Toja further enhances performance, and the best results are achieved when using [t)(()-N) obtained via the fast sampling method.
Different Detection Backbone. We evaluate multiple object detectors and report results the state-of-the-ard DEIM (CVPR25) method Table Additional detector comparisons are provided in Tob the Appendix. Our experiments show that method consistently improve performance across different detectors, demonstrating its robustness and effectiveness.
CroandTruhh
Driginal
ComtrolNet
Gl IDN
Trai. Tiffisiion
ComtoeNee ReCon CHONN- ReConn
Figure 5: Visualization comparison of generated samples. Our methods show better image fidelity and content-amnotation consistency.
Perception Model. Our approach employs the Grounded-SANN (Ren al. 2024 region-guided model to provide region-award guidance. Additionally, we compare different backbone models for the detector within Grounded-SAM, better perception leads to improved performance, suggesting that our method stands to benefit from stronger foundation models.
detailed in Table The experimental results indicate that
4.4 Qualitative Results
Figure 5 shows that our method substantially improves both the fidelity and the localization accuracy of generated samples. Unlike prior structure-control methods such GLIGEN and ControlNet, which lack mechanisms for fine-grained region rectification and hence can exhibit imprecise localization and semantic leakage, our approach enforces strict consistency with the provided annotations. For

---

example, it removes an extraneous zebra produced by GLIGEN outside the target bounding box (row 1) and superfluous sheep outside the region of interest (row 3), and it correctly restores person that ControlNet fails to generate (row 2). By aligning generated content with the original annotations, our method improves overall generation quality while maintaining high fidelity and sample diversity. More visualization results are provided in the Appendix.
5
Conclusion
This paper presents Controllont data augmentation (ReCon), mrainingffree, diffusion-basee method developed to generate high-quality, content-abbll-alinnel synthetic data for enhancing object detection models. Extensive experimental evaluations demonstrate that ReCon outperforms traditional augmentation and generative methods, ultimately leading to superior detection performance.
6 Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant 62021002.
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A Limitations and Societal Impacts
Limitations. Although ReCon produces better FID scores and improves detector mAP without additional training. it may increase computation time data volume grows. The requirement for an extra perception model also raises development costs. We have introduced acceleration techniques to lower these costs. Integrating fast sampler (Luo et al. 2023) and lightweight perception model offers a promising path to maximize the efficiency of diftusion based data augmentation for detector training.
Societal Impacts. Generative models (Podell et al., 2023 Zavadski et al. 2024 offer cost-
effective alternative to manual data collection and annotation for object detection. Our method enhances these models' ability to produce annotation-consistent content without additional training and improves downstream detector performance. This efficiency can benefit organizations and researchers who face limited data resources.
However, because generative models are pre-trained on large, uncurated Aision-languas datasets from the internet, they may inherit social biases and stereotypes (Cho et al. 2023 Naik & Nushi 2023 Ross al. 2020) and produce discriminatory outputs. It is therefore essential integrate bias detection and mitigation mechanisms. By applying region-wise rectification and alignment, our approach improves content accuracy and reduces bias.
Another concern is the potential misuse of synthetic imagery for purposes such as deepfakes (Lyu 2020), which spread misinformation and undermine societal trust. To address this risk, the community must establish regulations and best practices that ensure responsible creation and use of synthetic data models.
B Pseudo Algorithm
We present the pseudo algorithm our Region-Guided Rectification Sampling in Algorithm T In Stage 1, we use a pre-trained perception model to identify instance masks. If annotated masks are already available, this step can be skipped. Next, we apply exclusive dilation to mitigate boundary segmentation issues and obtain a refined mask region Rgt, which serves as the initial ground-truth region. If visual priors such as Canny edge maps are provided, we further suppress false-positivy regions in these priors, as they are prone to misidentification and may introduce noise the corresponding visual condition maps.
In Stage 2, we define a rectification interval of T, steps within the sampling process. First, we perform N' steps of fast sampling infer the latent z which is then decoded into an image. This image is passed through an object detector to identify false positives and false negatives. We then segment the corresponding regions: false positives are segmented on z'_N. and false negatives on the reconstructed image Ti- These regions are merged to produce region-guided correction mask M. To rectify errors, we generate a noisy latent orig by adding t steps of noise to the original image. Finally, we mix and orig using the -egion-guided mask to correct misgenerated areas.
C Experimental Setting
C.1 Metrics Definition
We provide a detailed explanation of the metrics used to evaluate the effectiveness of our method below:
mAP: The mean of the average precision values computed over multiple IoU thresholds (typically from 0.50 to 0.95 in steps of 0.05) and across all classes, reflecting overall detection accuracy under varying overlap requirements.
AP The average precision at a single IoU threshold of 0.50, measuring detection quality under a more lenient overlap criterion.
AP 7t The average precision at single loU threshold of 0.75, measuring detection quality under stricter overlap criterion.
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> Detect candidate boxes in original image Seleect false positives Algorithm Region-Guided Rectification Sampling Process
Require: VAE decoder Oa. Object detector DD segmentation model S
Require: Original image zo, initial latent 2g. control map c, text prompt p. initial noise Ground-truth boxes Bgt with labels Ygt
Require:
Require: Refinement time steps T,, fast-sampling step count N
Ensure: Final output image Eo
Stage Initial Mask preparation
2: Bpred, Upred <- (
3: Bip < match_ by BBred Byts Upred Bgt: J = 0.5)
4: (Rgt, Rpp) f- S( [Bgt: Byl)
>. Segment GT and regions
5: (Rgt, Rfp) f ExclusiveDilate (Rgt, Ryp- kernel 7)
Dilate masks and avoid overlap
6: с + C ① +1 (1- Rtp)
Keep control without FP regions
7: Stage Sampling with region-guided rectification
8: for = TT
0 do
24 = Vai 2++1
01+1 co (3 t+ + vT- ar co c0(4111 L 1+1 1)
9:
Denoise
v+11
if T, then
11:
4'I- +- FastSample(z: N)
Accelerated N-step sampling
= + :- vI
āi co(2,tt)
12:
13:
1011-N & 00(=6)e-N)
14:
Bpred: Upred & D( (-N)
15:
(Bips Bfn) +- match_hyp ToU B Ppred, Bgt Upred, Ugt, 0.5)
D. Select false
positives and false negatives bboxes
16:
M f- merge(S (1 N. [B Bynl))
Merge false positive and false negative
region to build regiionrectification mask
17:
2t v0z2n + vI age
orig
18:
21 - 2rjo
+ 4 (1- M)
> Region- Guided Rectification
19:
end if
20: end for
21: 4o 0a(20)
22: return &0
AP The average precision for medium-sized objects (area E 962) pixels), indicating perfor- mance on objects of moderate scale.
AP' The average precision for large objects (area > 962 pixels), indicating performance on larger targets that are generally easier localize.
FID: Fréchet Inception Distance (FID) measures the similarity between real and generated images.
We compute FID using 5,000 samples. Lower FID indicates higher image quality and diversity.
C.2 More Training Details
In this study, we train several object detection models on downstream detection datasets, including
Faster R-CNN (Ren al. (2015) with an R50 FPN backbone, ATSS (Zhang et al. 2020) with an R50 FPN backbone, FCOS (Tian et al. 2019) with an R50 FPN backbone, YOLOX-S (Ge et al. (021). RetinaNet with Swin-Ting Tiny (Liu al. (02T) FPN backbone and DEIM-D-FINE -N (Huang al.
2024) model. For Faster R-CNN, ATSS, FCOS, and RetinaNet, we follow the standard 1x training schedule, running 12 epochs for all experiments, except for Faster R-CNN on the COCO dataset, where the training is reduced to 6 epochs. We use random flipping as the default data augmentation strategy. For YOLOX-S, we follow the official training setup with 300 epochs and apply a stronger augmentation pipeline, including mosaic, random affine transformations, mixup, random flipping, and HSV random augmentation. For DEIM, we follow the official training configuration to train model for 40 epochs.
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D More Visualization Results
D.1 Visualization Samples Analysis
As shown in Figure 6 we present additional samples generated by our method, which illustrate its ability to produce novel content with high image-annotation consistency and strong visual fidelity. Our method remains robust even in challenging scenarios involving tiny object boxes and densely distributed objects, ultimately improving the effectiveness of downstream detection models.
In Figure 0 we notice that some the smaller objects our generated images remain strikingly similar to those in the originals (the plant in the second row). This is partly due to the Canny- conditioned ControlNet, which helps preserve source-like like structures, a result we see much less often with GLIGEN. More fundamentally, when region proves difficult to synthesize accurately, our method continuously applies eegion-guided corrections to bring it closer to the original image. We lower the editing intensity in these challenging areas in order to maintain their structural and semantic integrity, so that any differences from the source appear only in texture and lighting.
Texture-level perturbations hard-to- tord--er-eeeeee samples also help us produce valuable corner cases. For example, Figure F shows a truck on the left that is heavily occluded and has an implausible aspect ratio. By introducing controlled texture disturbances into the real image, we can create new corner cases that further enhance the model's 's robustness.
Additionally, a case study is provided in Figure which demonstrates that although Instance Diffusion incorporates instance-level descriptions with specific visual tokens, it still suffers from semantic leakage. Our method can further mitigate this issue and enhance performance in such cases.
D.2 Rectification Mask Analysis
Our method introduces region-guide rectification correct misgenerated areas during the diffusion sampling process. We quantify the rectification mask's 's area at different sampling stages to analyze how much of the image each correction step influences. Across 1,000 samples, measured the ratio of mask area to total image area at the 75%, 50%, 25%, and 10% sampling steps, obtaining 12.16%, 8.87%, 7.12%, and 6. 6.77%, respectively. This trend demonstrates that rectification procedure progressively reduces misgenerated regions as sampling proceeds.
E
More Experimental Results
E.1
Comparison of Rectification at Different Diffusion Timesteps
We study the sensitivity of our region-rectification schedule to the choice of diffusion timesteps. Coneretely, we apply rectification at single timesteps well as several multi-stage schedules and report mean average precision (mAP), AP and AP7s in Table g Applying rectification only at an early stage (0.757) produces modest improvement over the ControlNet baseline (mAP 35.2 vs. 34.9), but is not optimal. We attribute this to two factors: (1) early-stage rectification can create shortcuts that allow spurious regions be propagated and "fixed" during later synthesis steps, and (2) features at 0.75l' remain highly diverse and only partially developed, so single early rectification cannot fully suppress all errors.
When rectification is applied at multiple stages we observe consistent improvements. A two-stage schedule [0.5T, 0.25T" raises mAP to 35.3, and a three-stage schedule (0.5T, 0.25T, 0.17] further increases mAP to 35.4. Our four-stage schedule [0.7 T 0.5T, 0.25T, 0.17] achieves the best balance across metrics (mAP 35.5, AP5o 56.2, AP75 38.4). Extending the schedule to six stages yields only marginal additional benefit (mAP 35.5, APTs 38.5), indicating that performance gains saturate beyond four rectification stages. These results justify our chosen four-stage schedule as effective and parsimonious design choice.
F.2
Comparison with Image Editing Method
Existing methods suffer from semantic leakage and discrepancies between generated content and the original annotations. To address these issues, our approach calibrates intermediate sampling results using latent point sampled within the diffusion sampling process. We also explore the effectiveness
18

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GroundTruth
Original
GLIGEN- ReCon
ControlNet- ReCon
plort
bockpock
Figure 6: Visualization results of samples generated with our methods.
Original Image
Instance Diffusion
Ours
Figure 7: Successful case analysis. Existing state-of-the-an methods suffer from semantic leakage, while approach effectively mitigates this issue.
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Original Image
Ours
Figure 8: Corner case analysis. For corner cases with severe occlusion and unrealistic aspect ratios (truck on the left side of image), our method can augment them generate new corner-case examples.
Table Ablation study of region rectification at different diffusion timesteps.
Method
mAP
AP50
AP75
ControlNet
34.9
55.5
37.7
0.757
35.2
55.7
38.0
0.57
35.3
56.0
38.0
0.257
35.3
55.8
38.1
0.17
35.2
55.8
38.0
00.5, 0.25]
35.3
55.8
38.2
0.5, 0.25, 0.1]T'
35,4
56.0
38.4
0.75, 0.5, 0.25, 0.1]T
35.5
56.2
38.4
00.75, 0.625, 0.5, 0.375, 0.25, 0.1]T
35.5
56.2
38.5
of image-editing methods which modify specific regions the original image while preserving the remainder. We compare method against an image editing method: SDEdit (Meng al. 2021). Notably, SDEdit applies a uniform editing strength across the entire image, which may result in certain regions being either over-enhanced under-enhanced. As demonstrated in Table 9 our method, which leverages region-controllable data augmentation guided by a perceptual model, achieves superior performance.
Table 9; Comparison with image editing method.
Method
mAP
AP
AP75 75
Real only
34.5
55.5
37.1
SDEdit 35.2
55.9
38.1
ReCon
35.5
56.2
38.4
E.3
Comparison with More Detectors
As shown in Table I0 we compare additional object detectors, and the results demonstrate that our method consistently improves the performance of each.
E.4 More Data Scaling
In the main text, we conducted data-scaling experiments under data-scarce settings. We further validated this phenomenon in few-shot scenarios. As shown in Table TT we compare ReCon with simple duplication-basse Our findings indicate that as the seale of data expansion increases, the corresponding performance improvement becomes more evident. Moreover, observe that augmenting with duplicated original
augmentation of original images under different expansion ratios.
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Table 10: Trainability comparison for more detectors on COCO.
Detector
Method
mAP
AP
AP75
Real only
34.0
54.0
35.8
RetinaNet Swin-T
(Liu et al.
2021
ReCon
35.1
54.9
37.0
Real only ReCon 37.3
36.6
56.0
39.1
FCOS R50
Tian et
2019
56.4
39.7
Real only
39.4
57.5
42.6
ATSS R50 Zhang et al.
2020
ReCon 40.0
58.3
43.2
images provides only marginal gains, whereas our method achieves comparable results to 10x duplication by using only 2 generated data.
Table 11: Few-shot data augmentation results across different data scales.
Method
mAP
AP30
AP73
Apm
AP
Real only
5.4
10.3
5.0
5.2
8.9
Expanded 2
Real Expansion
5.7
10.9
5.5
5.8
9.7
ReCon
6.7
12.3
6.5
6.7
11.2
Expanded 5x
Real Expansion
6.1
11.3
6.1
6.3
10.2
ReCon
7.7
14.1
7.6
7.7
12.5
Expanded 10x
Real Expansion
6.4
11.5
6.5
6.3
10.2
ReCon
8.0
14.4
7.9
7.7
13.2
E.5 Robustness Evaluation
We perform three independent runs with diffcrent random seeds and report the mean and standard deviation in Table I2 The results show that method consistently outperforms the baseline and demonstrates lower variance, indicating improved stability and robustness.
Table 12: Performance stability across three runs with different random seeds. We report the mean and standard deviation of mAP and 5050-
Method
mAP (%)
AP50 (%)
Baseline
34.9 + 0.08
55.3 + 0.12
ReCon
35.5 0.05
56.3 0.14
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E.6 Runtime and Inference Efficiency
Table 13 reports per-sample inference times on the COCO dataset for baseline control methods and for those methods augmented with our method. All rumtime measurements were collected on single NVIDIA RTX 3090 GPU. On average, our approach introduces only an additional 179-1.04 seconds per sample compared to the corresponding baseline control model. Compared to the training-free layout-to-image control method LayoutCuudancce, our method does not rely on backward guidance and therefore avoids the substantial time cost incurred by its aptimization-basee procedure. In contrast to training-based alternatives that require additional fine-tuning on downstream datasets, framework can be applied directly at inference time without introducing extra training overhead. Finally, our method is fully compatible with structural controllable models such as ControlNet, yielding improved performance while incurring less than one second of additional inference time per sample.
We note that further acceleration is possible: replacing the grounding component with more efficient model (e.g., EfficientSAM (Xiong et al. (024)) or integrating memory- and compute-efficient attention libraries (e.g.. xFormers (Lefaudeux et al. 2022)) should reduce the added overhead. We will include these quantitative runtime results and the above discussion in the revised manuscript to clarify the practical efficiency of our approach.
Table 13: Inference time comparison (seconds per sample) on COCO measured on an NVIDIA RTX 3090, Values in parentheses indicate the baseline time plus the additional overhead introduced by our method.
Method
Inference time (s)
ControlNet
2.55
Layout Guidance
12.58
Instance Diffusion
8.98
ControlNet + ReCon
3.34 (2.55 + 0.79)
Instance Diffusion ReCon
10.02 (8.98 1.04)
E.7 More Ablation Experiments
To evaluate the contribution of region-alignee tross-attention (RACA) independently from the full rectification pipeline, we augmented the InstanceDiffusion baseline with RACA while keeping all other components unchanged. The RACA layer replaces the standard cross-attention with region- aligned cross-attention mechanism and introduces no additional trainable parameters, allowing direct deployment pretrained generators without fine-tuning. Table 14 summarizes the results: adding RACA yields consistent improvements over the InstanceDiffusion baseline, confirming that region- aligned attention alone contributes meaningfully region alignment and downstream detection performance.
Table 14: Effect of region-aligned cross-attention (RACA) within InstanceDiffussin.
Method
mAP
AP30
AP7s
InstanceDiffusion
35.0
55.4
37.6
InstanceDiffusiae w/ RACA
35.2
55.7
38.0
E.8 Rationale Analysis of Our Method
Prior work has applied perception models to filter and then re- re-generate low-quality synthetic samples in two-step generate-the -hen-filter pipelines (Fang et al., 2024). While effective in improving sample fidelity, such repeated generate-and- and-filter cycles incur substantial computational overhead. In contrast, our approach integrates grounding feedback directly into the diffusion trajectory, enabling on-the-fly rectification of misaligned regions within single forward pass. This integrated rectification avoids repeated re- re-sampling and produces high-quality, aligned image-label pairs far more efficiently than multi-round generate-and and-filter procedures.
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Our rectification mechanism (RGR) identifies mismatched regions via grounding basdd IoU matching between generated regions and target annotations, and selectively injects controlled perturbations into those mismatched regions during the diffusion trajectory. This targeted intervention encourages subsequent denoising steps to correct region appearance and spatial extent while leaving well-aligned regions largely untouched.
The proposed design achieves three complementary properties that are critical for large-scale synthetic data pipelines:
Usability. The method does not require model retraining, multi-round generation, or post- hoc filtering; operates during single forward pass and can be applied to pretrained generators out-of-the-box.
Effectiveness. Experiments across multiple datasets and generator architectures show consistent improvements in both image quality and detection metries.
Efficiency. The rectification strategy introduces limited additional computational overhead to inference, making suitable for large-scale synthetic data generation.
These benefits stem from two lightweight, jointly designed modules rrggion-aligned attention and boounding-guider rectification) that together ensure improved alignment without compromising generation speed or flexibility. To reach the final design we explored and quantitatively compared variety of correction and alignment strategies; the selected combination offers favorable trade-off between alignment quality, computational cost, and ease of integration.
In summary, the combination of on-the-fly grounding feedback, parameter-free region-aligned attention, and targeted rectification yields practical and effective solution for producing well- aligned image-label pairs. The plug-and-play compatibility with pretrained generators and consistent empirical gains across baselines and datasets underscore the substantive contributions of this work.
E.9 Further Analysis of Rectified Regions
We analyzed the relationship between object area and the likelihood of rectification under our proposed Region-Guided Rectification strategy. Specifically, examined 500 objects identified as requiring rectification and plotted their area distribution using kernel density estimation, as shown in Figure g The results reveal that smaller regions more likely be rectified. This is because small objects are generally more difficult to synthesize accurately, diffusion models tend to generate artifacts or errors in such regions.
1e-5
Kernel Density Estimation of Rectified Region Areas
4.0
3.0
Density
2.5
1.0
Region Area
Figure 9: Kernel density estimation of rectified region areas.
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Reviews:
[{"review_id": "1rgJXgtIVd", "reviewer": "Reviewer_uPpr", "summary": "This paper proposes a training-free framework that improves object detection data augmentation by enhancing existing structure-controllable diffusion models like ControlNet.\nIt consists of a list of building blocks, Grounded SAM, Structure control models like ControlNet or GLIGEN, CLIP Text Encoder, DDIM Sampler. And also propose their novel designed pipelines, Region-Guided Rect. (RGR) and Region-Aligned CA (RACA). The authors conduct extensive and carefully designed experiments across datasets (COCO, VOC), detection models (Faster R-CNN, YOLOX, DEIM), and data regimes (few-shot, low-resource), along with thorough ablation studies to validate each componen", "questions": "In general, this paper is well written and the problem is clearly motivated. The proposed method is practical, training-free, and shows strong results. For future improvements, I have a few questions: Can the method be extended to incorporate real annotated data during training, and how would it perform in mixed real and synthetic settings? Could the rectification mechanism be extended to handle more complex object layouts or occlusions, possibly beyond bounding boxes? How well does the method generalize to domains beyond object detection? It would be valuable to test the method on other tasks such as instance segmentation or visual grounding to explore its generalization ability beyond object detection.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengthen:\nTraining-free pipeline: While the components (e.g., ControlNet, Ground-SAM) are existing tools, their training-free integration into the generation loop is novel and practical.\nModular and Plug-and-play: Can be combined with existing structure-guided models (ControlNet, GLIGEN, Instance Diffusion), making it easy to adopt.\nComprehensive experiments and ablations: The paper includes experiments on multiple datasets, detectors, data scales, and includes thorough ablations to validate the contributions of RGR and RACA.\nStrong empirical results: The method shows consistent improvements over baselines across various benchmarks. Demonstrated improvements across multiple detection backbones and data scarcity regimes.\n\nWeakness:\nMissing system and runtime analysis: While computational cost is mentioned in the limitations, the paper lacks details on GPU settings and sampling time. Including quantitative runtime comparisons would strengthen the practical value of the method.\nStage selection design: The choice of applying region rectification at early, middle, latter, and final stages seems heuristic (0.75, 0.5, 0.25, 0.1 T). It would be helpful to provide ablation or sensitivity studies to show whether early-stage corrections (on blurry layouts) meaningfully impact the final generation quality.\nWriting and explanation of visual results: The writing is clear and easy to follow. But the qualitative results (like in Figure 5) could use more explanation, especially how they show better image quality and content-label alignmen", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the time and effort you have dedicated to reviewing our paper. We value your feedback on our method, noting that our method is **novel and practical** and that it is **easy to adopt**. We also thank you for recognizing that our method achieves **strong empirical results** over baselines across various benchmarks.\n\n**Re Weakness #1:**\n\nThanks for your suggestion. We have evaluated our method on an NVIDIA RTX 3090 GPU and report inference times in Table 1 below. On average, our approach adds only 0.79 – 1.04 seconds per sample compared to the baseline control models. Compared to the training‑free layout‑to‑image control method LayoutGuidance, our method does not rely on backward guidance and thus avoids its substantial time cost. In contrast to existing training‑based approaches that require additional fine‑tuning on downstream datasets, our framework can be applied directly without introducing extra training overhead. Moreover, our method is fully compatible with structural controllable models such as ControlNet, yielding superior performance while incurring less than one additional second per sample. \n\nWe also note that further acceleration is possible by integrating more efficient grounding models (for example, EfficientSAM [1]) or memory‑efficient attention libraries (such as xFormers [2]). We will incorporate these quantitative runtime results and discussions into the revised manuscript to clarify the practical efficiency of our approach.  \n\n\nTable 1: Inference time comparison with existing methods on COCO dataset.\n| Method                         | Inference time (s)    |\n| ------------------------------ | --------------------- |\n| ControlNet                     | 2.55                  |\n| Layout Guidance                | 12.58                 |\n| Instance Diffusion             | 8.98                  |\n| ControlNet + ReCon             | 3.34 (2.55 + 0.79)    |\n| Instance Diffusion + ReCon     | 10.02 (8.98 + 1.04)   |\n\n**Re Weakness #2:**\n\nThank you for the suggestion to perform an ablation study on the choice of rectification stages. We have evaluated rectification applied at individual timesteps as well as in combinations, and the results are reported in Table 2. Applying rectification only at the early stage (0.75 T) yields a modest improvement over the ControlNet baseline (mAP 35.2 vs. 34.9), but it is not optimal. We attribute this to two factors: first, early-stage rectifiaction can create shortcuts for spurious regions to rectify through later synthesis; second, features at 0.75 T remain highly diverse and partially undeveloped, so a single early rectifiaction cannot fully suppress all errors. When we combine rectification at multiple stages, we observe consistent gains: two stages rectifiaction ([0.5, 0.25 T]) raises mAP to 35.3, and using three stages ([0.5, 0.25, 0.1 T]) further increases mAP to 35.4. Our full four‐stage schedule ([0.75, 0.5, 0.25, 0.1 T]) achieves the best balance of all metrics (mAP 35.5, AP50 56.2, AP75 38.4). Extending to six stages yields only marginal benefit (mAP 35.5, AP75 38.5), suggesting that gains saturate beyond four rectifiactions. We will include these quantitative sensitivity results in the revised manuscript to demonstrate that our chosen schedule is both effective and well justified. \n\nTable 2: Ablation study of region rectification at different diffusion timesteps.\n| Method                                  | mAP  | AP50 | AP75 |\n|-----------------------------------------|------|------|------|\n| ControlNet                              | 34.9 | 55.5 | 37.7 |\n| 0.75 T                                  | 35.2 | 55.7 | 38.0 |\n| 0.5 T                                   | 35.3 | 56.0 | 38.0 |\n| 0.25 T                                  | 35.3 | 55.8 | 38.1 |\n| 0.1 T                                   | 35.2 | 55.8 | 38.0 |\n| [0.5, 0.25] T                           | 35.3 | 55.8 | 38.2 |\n| [0.5, 0.25, 0.1] T                      | 35.4 | 56.0 | 38.4 |\n| [0.75, 0.5, 0.25, 0.1] T                | 35.5 | 56.2 | 38.4 |\n| [0.75, 0.625, 0.5, 0.375, 0.25, 0.1] T  | 35.5 | 56.2 | 38.5 |\n\n**Re Weakness #3:**\n\nThank you for this insightful suggestion. In Figure 5, our method removes the extraneous zebra that GLIGEN generates outside the specified bounding box (row 1) and the superfluous sheep outside the region of interest (row 3). In addition, it correctly restores the person that ControlNet fails to generate (row 2). To make these improvements clearer, we will expand the main text and the figure caption with detailed descriptions of each row and add overlay labels directly on the images to highlight the rectified regions.\n\n**Re Question #1:**\n\nThank you for this constructive question. We have explored training with mixed real and synthetic data by augmenting our real dataset with varying proportions of synthetic samples, as shown in Figure 4. These experiments demonstrate how different mixing ratios affect detector performance. In future work, we plan to investigate more sophisticated strategies, such as adaptive re‑weighting of real and synthetic samples during training, to further improve robustness in mixed-data scenarios.\n\n**Re Question #2:**\n\nThank you for this insightful question. While our current rectification mechanism does not explicitly model highly complex layouts or occlusions, it nevertheless demonstrates surprising robustness in challenging scenarios, as illustrated by the results in Figure 5 and Figure 6 of the manuscript. To handle more complex object layouts and occlusions in future work, we plan to explore a layer‑based generation strategy, in which objects are rendered in a defined depth order and refined progressively. This approach would allow the model to reason about occlusion relationships and ensure that foreground and background elements are composed correctly.\n\n**Re Question #3:**\n\nThank you for this valuable question. Our experiments demonstrate that the proposed ReCon method significantly improves alignment between generated content and annotations. We therefore anticipate that our method can be extended to other vision tasks where content‑annotation consistency is critical, for example instance segmentation and visual grounding. In future work, we plan to conduct comprehensive evaluations on these tasks to validate and refine our approach.\n\n[1] Xiong, Yunyang, et al.  \"Efficientsam: Leveraged masked image pretraining for efficient segment anything.\" In CVPR 2024.\n\n[2] Lefaudeux, Benjamin, et al. \"xformers: A modular and hackable transformer modelling library.\" 2022."}, {"review_id": "bLV6w9F5jd", "reviewer": "Reviewer_1h8Z", "summary": "This paper introduces a generative data augmentation solution for improving object detectors. Specifically, the authors tackle the issue of groundtruth / generation fidelity in controllable diffusion models (such as ControlNet), where not always the diffusion process generates all the right objects in the right locations, potentially hindering the performance of the downstream trained detector. To improve the fidelity to the region control, the authors propose two solutions: First, a Region Guided Rectification mechanism intervenes at four timesteps throughout the sampling trajectory, by decoding the image and probing a perception model (GroundedSAM) for region fidelity. Regions with poor generation are then resampled by injecting noisy latents from the real image. Secondly, the authors propose Region-Aligned Cross Attention, where text embeddings of the target class interact exclusively with the target regions for better grounded generations. The solution is tested on two augmentation benchmarks and proves successful with several underlying generators.", "questions": "See weaknesses section", "limitations": "Yes, in the supplementary material.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strenghts**\n\n- The problem of groundtruth / generation fidelity is a real current issue in generative data augmentation for object detection. The paper proposes a neat solution to the issue.\n- The idea of probing the generation through the diffusion sampling and of using feedback from a grounded model is both novel and practical.\n- The paper has an excellent experimental section: the ReCon model improves three different generators (ControlNet, GLIGEN, InstanceDiffusion), and the authors tested both COCO and PascalVOC dataset. An ablation study verifies the importance of both ReCon components, plus other analyses verify its impact in lower data regimes or with different detectors or feedback models.\n\n\n**Weaknesses** (order of decreasing severity)\n\n- Tab 4 reports the main ablation of the paper, where the two components of the proposed model are tested separately. However, it is not clear to me whether the mAP results are meant in terms of fidelity (i.e. the generated data is used for evaluation with a pretrained detector) or trainability (i.e. the generated data is used to train a detector, and mAP is reported on a real validation set). The presence of FID in the table suggests that fidelity is reported. In that case, it is very important to report the ablation on trainability as well, as the two might not correlate. \n- Tab 3 and Tab 6. miss the augmentation performance of ControlNet only, without ReCon, to successfully conclude that ReCon is helpful in the tested settings.\n- the authors should discuss the computational overhead that Region-Guided Rectification has on sampling. Indeed, it involves, four times in the trajectory, to fast-track the latents to $t_0$ and to decode an image. What is the impact in terms of generation latency.\n- In Fig. 4, the real-only baseline should not be constant, as replicating the data 2x, 3x, 5x and 7x times increases the number of training iterations on a fixed epoch schedule. This might lead to the model benefitting from longer training, or overfitting. It would be interesting to see these variations in the baseline for better context.\n\nSummary: the paper tackles an important open problem in generative data augmentation, namely the region fidelity between the intended groundtruth / control and the resulting augmentation generation. The method is sound and novel. Some aspects of the experimentation could be improved and polished (especially the ablation study, see comment above) but the strenghts clearly outweight the weaknesses.", "was_revised": true, "final_justification": "The authors have addressed the few minor comments I had on the paper, that I already considered ready for publication. I confirm my original accept recommendation.", "rebuttal": "We sincerely thank you for their careful evaluation of our work.  We appreciate your recognition of the importance of improving generation fidelity in data augmentation for object detection and the **novelty and practicality** of our method. We also appreciate the positive assessment of our experiments, which demonstrate **consistent improvements** across multiple baselines and datasets, alongside thorough ablation studies and analyses.\n\n**Re Weakness #1:**\n\nThank you for pointing out the ambiguity in Table 4. To clarify, the FID score measures the fidelity of our generated training data. The mAP values reflect trainability, as we use the synthetic data to train a detector and evaluate its performance on a real validation set.  As shown in Table 4, our method improves both fidelity and trainability. We apologize for any confusion caused by the current description and will revise the table and corresponding description to make this distinction clear in the final version of the manuscript.\n\n**Re Weakness #2:**\n\nThank you for the helpful suggestion. We have now added the missing ControlNet-only results to Table 3 and 6, as shown below. These results clearly demonstrate the performance gains brought by ReCon over the ControlNet baseline in both settings. We will include the updated tables and clarify this comparison in the final version of the paper.\n\nTable 1: Comparison results on the PASCAL VOC dataset.\n| Method             | Real only | Simple Duplicate | RandAugment | ControlNet | ReCon |\n|--------------------|-----------|------------------|-------------|------------|-------|\n| mAP            | 77.1      | 76.2             | 77.7        | 77.8       | 78.5  |\n\n\nTable 2: Trainability comparison with DEIM-D-FINE-N on COCO.\n| Method      | mAP  | AP50 | AP75 | mAR  |\n|-------------|------|------|------|------|\n| Real only   | 38.5 | 55.2 | 41.5 | 60.4 |\n| ControlNet  | 39.1 | 55.8 | 42.1 | 60.6 |\n| ReCon       | 39.8 | 56.6 | 42.5 | 61.0 |\n\n\n\n**Re Weakness #3:**\n\nThank you for raising this important point. We have measured the computational overhead introduced by our method on an NVIDIA RTX 3090 GPU. As shown in Table 3, our method adds only 0.79 to 1.04 seconds of inference time per sample compared to the corresponding baseline control models. While our approach performs four intermediate decoding steps during sampling, the overall latency remains practical.\n\nTable 3: Inference time comparison with existing methods on COCO dataset.\n| Method                         | Inference time (s)    |\n| ------------------------------ | --------------------- |\n| ControlNet                     | 2.55                  |\n| Layout Guidance                | 12.58                 |\n| Instance Diffusion             | 8.98                  |\n| ControlNet + ReCon             | 3.34 (2.55 + 0.79)    |\n| Instance Diffusion + ReCon     | 10.02 (8.98 + 1.04)   |\n\nCompared to training-free methods such as LayoutGuidance, which rely on time-consuming backward guidance, our method avoids heavy computation and achieves significantly faster inference. Unlike training-based methods that require additional modules and fine-tuning on downstream datasets, our framework is plug-and-play and does not introduce extra training costs.\n\nIn addition, our method is compatible with widely used control models such as ControlNet and consistently improves performance with minimal latency increase. We also note that further acceleration can be achieved by using more efficient grounding models (e.g., EfficientSAM [1]) or memory-efficient attention libraries (e.g., xFormers [2]). We will include this runtime analysis and discussion in the revised manuscript to better highlight the efficiency of our approach.\n\n**Re Weakness #4:**\n\nThank you for this insightful comment. We have evaluated how increasing the real-only baseline by simple duplication affects detector performance under a fixed epoch schedule. The results in Table 4 below show that duplicating the dataset up to 3x yields steady gains (for example, mAP increases from 13.0 to 17.1 on the 5 % subset and from 18.5 to 21.1 on the 10 % subset). However, increasing duplication to 5x and 7x leads to saturated performance and even significant degradation, indicating overfitting under extended training. By contrast, as shown in Figure 4 of the manuscript, our method generates novel samples with improved content–annotation consistency and continues to provide performance improvements without overfitting. We will include these additional baseline curves and discussion in the revised manuscript to give a clearer context for our comparative results.\n\nTable 4:  Results of dataset duplication on detector performance under a fixed‑epoch training schedule.\n| Setting | original | 2x   | 3x   | 5x   | 7x   |\n|-----------|----------|------|------|------|------|\n| 5% Data   | 13.0     | 15.2 | 17.1 | 17.4 | 17.0 |\n| 10% Data  | 18.5     | 20.7 | 21.1 | 20.5 | 19.7 |\n\n[1] Xiong, Yunyang, et al.  \"Efficientsam: Leveraged masked image pretraining for efficient segment anything.\" In CVPR 2024.\n\n[2] Lefaudeux, Benjamin, et al. \"xformers: A modular and hackable transformer modelling library.\" 2022."}, {"review_id": "yt1e96goJG", "reviewer": "Reviewer_z3N1", "summary": "This paper introduces ReCon, a training-free method that enhances structure-controllable diffusion models for object detection data augmentation. The key insight is that existing generative approaches often produce content-position mismatches and semantic leakage when generating training data. To address this, ReCon integrates two main components into the diffusion sampling process: (1) Region-Guided Rectification (RGR), which uses feedback from a pre-trained perception model (Grounded-SAM) to identify and correct misgenerated regions during sampling, and (2) Region-Aligned Cross-Attention (RACA), which enforces spatial-semantic alignment between image regions and their textual descriptions. The method is plug-and-play, requiring no additional training, and demonstrates improvements across various datasets (COCO, PASCAL VOC) and detection architectures.", "questions": "1. Could you provide detailed timing comparisons? How much does ReCon increase generation time compared to baseline methods? What is the trade-off between generation quality and computational cost?\n2. You rectify at 4 timesteps (75%, 50%, 25%, 10%). Have you experimented with fewer rectification steps? What is the minimum number needed to maintain performance?\n3. Can you provide analysis of failure cases? Are there specific object categories or scene types where ReCon doesn't help or even hurts performance?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n1. The method addresses a real problem in generative data augmentation - the mismatch between generated content and annotations. The training-free nature makes it immediately applicable to existing models.\n2. The experiments are extensive, covering multiple datasets (COCO, PASCAL VOC), various data regimes (1%, 5%, 10%, few-shot), different detectors (Faster R-CNN, YOLO-X, DEIM, etc.), and includes proper ablations.\n3. The paper is well-written with clear figures illustrating the method and visualization of results.\n\n**Weaknesses**\n\n1. The core ideas are relatively straightforward applications of existing techniques: 1） Using a grounding model to detect errors is a natural extension. 2) Region-specific attention has been explored before (Instance Diffusion) 3) The rectification mechanism is essentially selective noise injection based on IoU matching\n\n\n2. Computational Overhead: The method requires to run Grounded-SAM multiple times during sampling (at 75%, 50%, 25%, 10% timesteps) and additional cross-attention computations. This significantly increases generation time, which is barely discussed.\n\n\n3. The performance gains, while consistent, are relatively small: such as COCO: 34.9 → 35.5 mAP (+0.6)\nThese improvements may not justify the added complexity in practice.\n\n4. The paper doesn't deeply analyze when and why the method fails. \n5. The method's effectiveness heavily relies on the quality of Grounded-SAM. How does performance degrade with weaker perception situation?", "was_revised": true, "final_justification": "I thank the authors for their detailed and timely response. After reviewing their feedback, I believe that most of my concerns have been addressed. I have therefore decided to raise my original rating, leaning toward acceptance.", "rebuttal": "We sincerely appreciate the time and effort you invested in reviewing our paper. We address the raised concerns as follows:\n\n**Re Weakness #1:**\n\nThank you for this thoughtful comment. We address each point in turn to clarify the originality and value of our contributions.\n\n1) First, although prior works have applied perception models to filter and then re‑generate poor‑quality samples [1], such two‑step pipelines incur substantial overhead. In contrast, our method integrates grounding feedback directly into the diffusion trajectory, allowing a single forward pass to rectify misaligned regions as they arise. This on‑the‑fly rectification yields high‑quality outputs far more efficiently than repeated generate‑and‑filter cycles.\n\n2) Second, InstanceDiffusion introduces region‑specific modules and requires full retraining to adapt to new layouts, our approach achieves region alignment by simply replacing the standard cross‑attention with a region‑aligned cross‑attention (RACA) layer. As detailed in lines 239–243 of the manuscript, this design incurs no additional trainable parameters and can be applied to pre‑trained models without further fine‑tuning. As shown in Table 1 of the manuscript, augmenting InstanceDiffusion with our method delivers consistent performance gains. To isolate the effect of RACA, we also conducted experiments in which only RACA was added to the original InstanceDiffusion pipeline and present the results as below.  These results confirm that region‑aligned attention alone can improve the performance.\n\nTable 1. Ablation study of RACA based on Instance Diffusion.\n\n| Method                         | mAP  | AP50 | AP75 |\n|--------------------------------|------|------|------|\n| InstanceDiffusion              | 35.0 | 55.4 | 37.6 |\n| InstanceDiffusion w/ RACA      | 35.2 | 55.7 | 38.0 |\n\n\n3) Third, we appreciate the reviewer's insightful comment, which touches on a fundamental issue in generative data for object detection: the inconsistency between image content and annotations. \nOur core contribution lies in proposing an easy-to-use, lightweight, and effective rectification strategy to address this inconsistency. While our approach is inspired by a motivation similar to that of the reviewer, the specific design and implementation of our method have been carefully crafted. We introduce a rectification mechanism that uses grounding-based IoU matching to identify mismatched regions and selectively injects noise into them via the proposed RGR method. This mechanism integrates three key advantages rarely achieved simultaneously in prior works:\n\n* **Usability**: Our method does not require model retraining, multi-round generation, or post-hoc filtering. It produces aligned image-label pairs in a single forward pass.\n* **Effectiveness**: Experiments on multiple datasets show consistent improvements in both image quality and detection performance.\n* **Efficiency**: The design introduces limited overhead to the generation process, making it suitable for large-scale synthetic data pipelines.\n\nThese benefits are enabled by our two carefully designed modules, which jointly ensure alignment without compromising generation speed or flexibility. To the best of our knowledge, this combination of usability, effectiveness, and efficiency has not been explored in prior literature.\n\nFinally, although the individual components may appear intuitive, we rigorously evaluated a variety of correction and alignment strategies before arriving at our final design. We furthermore note that **Reviewer uPpr** and **Reviewer 1h8Z**  have characterized our solution as both **novel and practical**, with **Reviewer 1h8Z** describing it as  **\"a neat solution\"**.  We believe that the combination of effectiveness, plug‑and‑play compatibility, and demonstrable gains across multiple generators and datasets underscores the substantive contributions of our work.\n\n**Re Weakness #2 :**\n\nThank you for raising this important point. We have measured the computational overhead introduced by our method on an NVIDIA RTX 3090 GPU. As shown in Table 2, our method adds only 0.79 to 1.04 seconds of inference time per sample compared to the corresponding baseline control models. While our approach performs four intermediate decoding steps during sampling, the overall latency remains practical.\n\nTable 2: Inference time comparison with existing methods on COCO dataset.\n| Method                         | Inference time (s)    |\n| ------------------------------ | --------------------- |\n| ControlNet                     | 2.55                  |\n| Layout Guidance                | 12.58                 |\n| Instance Diffusion             | 8.98                  |\n| ControlNet + ReCon             | 3.34 (2.55 + 0.79)    |\n| Instance Diffusion + ReCon     | 10.02 (8.98 + 1.04)   |\n\nCompared to training-free methods such as LayoutGuidance, which rely on time-consuming backward guidance, our method avoids heavy computation and achieves significantly faster inference. Unlike training-based methods that require additional modules and fine-tuning on downstream datasets, our framework is plug-and-play and does not introduce extra training costs.\nIn addition, our method is compatible with widely used control models such as ControlNet and consistently improves performance with minimal latency increase. We also note that further acceleration can be achieved by using more efficient grounding models (e.g., EfficientSAM [2]) or memory-efficient attention libraries (e.g., xFormers [3]). We will include this runtime analysis and discussion in the revised manuscript to better highlight the efficiency of our approach.\n\n\n**Re Weakness #3:**\n\nThank you for raising this concern. While the absolute mAP increase may appear modest, even small gains are highly valued in object detection benchmarks, where competitive state‑of‑the‑art methods often advance by less than one point (e.g., DEIM (CVPR'25) beats D-FINE [4] with 0.7 mAP gains on COCO). Moreover, our improvements are consistent across multiple generators, datasets and data regimes, demonstrating that our method reliably enhances both fidelity and trainability. Importantly, these gains come at minimal overhead: our method requires no additional fine‑tuning and adds less than one second of inference time per sample on a standard GPU. In practice, this means that practitioners can adopt our framework to achieve measurable performance boosts without a large increase in computational or implementation complexity.\n\n\n\n**Re Weakness #4 and Question #3:**\n\nThank you for this important suggestion. In our current experiments, we have observed that ReCon is less effective when the input images contain severe motion blur or strong degradation. In these cases, the initial noise estimates can be too large, and rectification may inadvertently reinforce erroneous patterns rather than correct them. We also note that extremely small objects or heavily cluttered scenes can pose challenges, since the grounding model may struggle to localize tiny regions or distinguish overlapping instances. To address these shortcomings, we will include a dedicated failure‑case analysis in the revised manuscript, complete with visual examples and a discussion of category‑ and scene‑specific limitations. This addition will help clarify the method's boundaries and guide future improvements.\n\n**Re Weakness #5**\n\nThank you for raising this important point. Table 7 compares two backbone variants of Grounding model: Swin-Tiny with 172 M parameters and Swin-Base with 232 M parameters. With Swin-Tiny, our method achieves an mAP of 35.5, compared to 35.6 with Swin-Base. This minimal decrease confirms that ReCon remains effective even with a relatively weaker perception model, while a stronger backbone offers a slight performance gain. We will expand our discussion on robustness to grounding quality in the revised manuscript.\n\n**Re Question #1  and Question #2:**\n\nThank you for this question. As shown in Table 2 above, ReCon increases inference time by less than one second per sample on an NVIDIA RTX 3090 GPU. To illustrate the trade‑off between generation quality and computational cost, we present in Table 3 an ablation over the number of region rectification steps. Adding up to four rectification stages steadily improves mAP, but further stages bring only marginal benefit. These results indicate that three to four rectification steps strike a practical balance, delivering substantial quality gains while maintaining modest overhead. We will include this detailed timing and quality‑cost analysis in the revised manuscript.\n\n\nTable 3: Ablation study of region rectification at different diffusion timesteps.\n| Method                                  | mAP  | AP50 | AP75 |\n|-----------------------------------------|------|------|------|\n| ControlNet                              | 34.9 | 55.5 | 37.7 |\n| 0.75 T                                  | 35.2 | 55.7 | 38.0 |\n| 0.5 T                                   | 35.3 | 56.0 | 38.0 |\n| 0.25 T                                  | 35.3 | 55.8 | 38.1 |\n| 0.1 T                                   | 35.2 | 55.8 | 38.0 |\n| [0.5, 0.25] T                           | 35.3 | 55.8 | 38.2 |\n| [0.5, 0.25, 0.1] T                      | 35.4 | 56.0 | 38.4 |\n| [0.75, 0.5, 0.25, 0.1] T                | 35.5 | 56.2 | 38.4 |\n| [0.75, 0.625, 0.5, 0.375, 0.25, 0.1] T  | 35.5 | 56.2 | 38.5 |\n\n[1] Fang, Haoyang, et al. \"Data augmentation for object detection via controllable diffusion models,\" in WACV 2024.\n\n[2] Xiong, Yunyang, et al.  \"Efficientsam: Leveraged masked image pretraining for efficient segment anything.\" In CVPR 2024.\n\n[3] Lefaudeux, Benjamin, et al. \"xformers: A modular and hackable transformer modelling library.\" 2022.\n\n[4] Peng Yansong, et al. \"D-fine: Redefine regression task in detrs as fine-grained distribution refinement.\" arXiv, 2024."}, {"review_id": "E48plc5Gm6", "reviewer": "Reviewer_QS5e", "summary": "This paper proposes ReCon, a finetuning-free method for generating images to augment object detection datasets. Two methods, named Region-Guided Rectification (RGR) and Region-Aligned Cross Attention (RACA), are proposed for addressing the region and semantic misalignment problems, respectively. Extensive experiments show the effectiveness of the ReCon method.", "questions": "1. Line 148, what does \"informational value\" mean?\n2. How does the region rectification schedule (hyperparameter $T_r$) affect the performance? Some ablations are needed, for example, appling rectification at early / middle / final stages only, and appling rectification at more timesteps (P.S. I find in line 64 that the proposed components are incorporated \"into every sampling iteration\", but in practice it seems that RGR is only used $T_r=4$ times).\n3. Could we use the original text-to-image Stable Diffusion model for this method? (i.e., without using ControlNet and using Stable Diffusion + RGR + RACA only)\n\nOverall, I'm satisfied with the proposed finetuning-free solution in this paper, leading to a borderline-accept score. Meanwhile, I have some technical detail related comments and questions listed above. My score may be modified according to the authors' rebuttal and discussions with other reviewers.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. The paper is well-written and easy to follow.\n2. The proposed methods (RGR and RACA) do not require an additional fine-tuning stage on object detection datasets, making the framework helpful for data-scarce scenarios.\n3. Experimental results demonstrate that the proposed method outperforms previous baselines in both standard and data-scarce benchmarks.\n\n**Weaknesses**\n1. In the RGR method, using the middle state $\\mathbf{z}_t^{orig}$ from the real images may limit generation diversity.\n2. The one-step estimation of $\\mathbf{z}_0$ (Eq. (6)) is not accurate compared to the full diffusion reverse process, as shown in Section 4.3 (Figure 6) of the DDPM paper [*], especially when $t$ is large (close to noise). I'm worried that the estimated images $\\mathbf{x}_0$ in Eq. (6)'s way (e.g., maybe blurred) mismatch the Grounded-SAM model's training distribution (clean images), leading to inaccurate detection results sometimes.\n3. It would be better if the paper could provide some insights into why existing methods cause mis-generated regions and semantic misalignment during the sampling process. (Not mandatory)\n4. The motivation of using ControlNet with edge canny map is not very clear to me. Why not use ControlNet with segmentation map, which seems to be more natural to the current Grounded-SAM-based framework?\n\n[*] Ho et al. \"Denoising diffusion probabilistic models.\" NeurIPS 2020.\n\nTypos:\n- Line 144: \"an\" -> \"a\"\n- Table 4 last line: the $AP^m$ number (39.0) is not aligned with that in Table 1 (39.6)?", "was_revised": true, "final_justification": "Most of my concerns have been addressed. After reading the authors’ responses to both my comments and those of the other reviewers, I have decided to raise my rating to a clear accept.", "rebuttal": "We sincerely appreciate the time and effort you have dedicated to reviewing our paper. We value your feedback on our method, noting that our method is helpful to data-scarce scenarios. We also thank you for recognizing that our method outperforms previous baselines in both standard and data-scarce benchmarks.\n\n**Re Weakness #1:**\n\nThank you for raising this point. We agree that diversity is important for data augmentation, but uncontrolled diversity can reduce the utility of synthetic data. For example, in Figure 5 (row 1), GLIGEN produces unexpected zebra patterns outside the target box despite its high diversity. By using the intermediate latent state $z_t^{orig}$ from real images, our method offers a corrective shortcut that curbs misgenerated regions without suppressing the model's ability to explore novel content. As noted in lines 147–148 of the manuscript, this design seeks to balance diversity and fidelity. Empirically, ReCon outperforms the baselines in both FID and mAP, demonstrating that preserving fidelity in this way leads to higher‑quality training data.\n\n**Re Weakness #2:**\n\nThank you for this insightful comment. We agree that a single‑step inversion at very large $t$ can produce somewhat blurred reconstructions that deviate from the clean‑image distribution Grounded‑SAM was trained on. Nevertheless, the detector achieves a high recall of regions that the generator initially omits in the early‑stage rectification, allowing us to identify and rectify these errors, which still enables effective rectification at the early stage of sampling. As shown in Table 1 below, rectification at $t=0.75T$ alone yields a modest mAP gain of 0.3. When we combine this early‑stage rectification with rectifications at mid and late stages, we achieve the better overall improvement.  We will include this analysis and discussion in the revised manuscript.\n\n**Re Weakness #3:**\n\nThank you for this insightful suggestion. In our analysis, we observed that most existing layout‑to‑image methods rely on either global conditioning or only a single region cue during sampling. As a result, the model often treats all spatial locations uniformly, which can lead to content appearing outside its intended region. In addition, diffusion sampling refines images gradually from noise, and without intermediate, region‑specific feedback, early misplacements can persist through later steps. These factors combine to produce mis‑generated regions and semantic misalignment. By contrast, our rectification mechanism injects targeted corrections at multiple timesteps, ensuring that region boundaries remain faithful to the desired layout throughout sampling. This insight underlies the design of our approach and explains why it outperforms prior methods in maintaining semantic alignment.\n\n**Re Weakness #4:**\n\nThank you for this question. Conditioning on segmentation maps would restrict us to a fixed set of object categories and would require costly fine‑tuning for each downstream task. In contrast, the Canny edge map used with ControlNet provides a general, category‑agnostic spatial prior that applies across a wide range of detection scenarios without additional training. Moreover, as shown in Table 1 of the manuscript, our ReCon method consistently improves generation quality whether the underlying control signal is an edge map (ControlNet), a layout (InstanceDiffusion), or grounding tokens (GLIGEN). This demonstrates that our method is broadly compatible with different types of conditioning, including but not limited to edge‑based guidance.\n\n**Re Typos:**\n\nThank you for highlighting these errors. We will correct these typos and perform a comprehensive proofread to ensure consistency across the manuscript.\n\n**Re Question #1:**\n\nThank you for the question. By \"informational value\" we refer to novel content that differs from the original image, such as new colors, styles, or object poses, and which can be understood as content diversity. We will revise this wording in the manuscript to make the meaning clearer.\n\n**Re Question #2:**\n\nThank you for suggesting an ablation study on the rectification schedule. We have evaluated rectification at individual timesteps (0.75 T, 0.5 T, 0.25 T, 0.1 T) as well as in various combinations. As reported in Table 1 below, rectification at 0.75 T alone raises mAP modestly from 34.9 to 35.2. Adding a second stage at 0.5 T and 0.25 T brings mAP to 35.3, and a three-stage schedule at 0.5, 0.25, and 0.1T increases mAP to 35.4. Our full four-stage schedule at 0.75, 0.5, 0.25, and 0.1 T delivers the best overall performance (mAP 35.5, AP50 56.2, AP75 38.4). Extending to six stages yields no further mAP gain and only a marginal AP75 increase to 38.5. These findings demonstrate that four rectification steps provide the optimal balance between quality and efficiency. We will include these quantitative results in the revised manuscript. \n\nWe will also clarify in the manuscript that region‑aligned cross‑attention is applied at every sampling iteration, while region‑guided rectification occurs at four selected timesteps to manage computational cost.\n\nTable 1: Ablation study of region rectification at different diffusion timesteps.\n| Method                                  | mAP  | AP50 | AP75 |\n|-----------------------------------------|------|------|------|\n| ControlNet                              | 34.9 | 55.5 | 37.7 |\n| 0.75 T                                  | 35.2 | 55.7 | 38.0 |\n| 0.5 T                                   | 35.3 | 56.0 | 38.0 |\n| 0.25 T                                  | 35.3 | 55.8 | 38.1 |\n| 0.1 T                                   | 35.2 | 55.8 | 38.0 |\n| [0.5, 0.25] T                           | 35.3 | 55.8 | 38.2 |\n| [0.5, 0.25, 0.1] T                      | 35.4 | 56.0 | 38.4 |\n| [0.75, 0.5, 0.25, 0.1] T                | 35.5 | 56.2 | 38.4 |\n| [0.75, 0.625, 0.5, 0.375, 0.25, 0.1] T  | 35.5 | 56.2 | 38.5 |\n\n\n**Re Question #3:**\n\nThank you for the thoughtful question. We have explored this direction by first comparing to LayoutGuidance, which introduces multi-step forward and backward guidance within a standard text-to-image Stable Diffusion pipeline. As shown in Table 1, the results are unsatisfactory. The generated images often lack coherence and fail to align semantically or spatially with the layout and prompts, resulting in poor downstream detection performance.\nWe also applied our ReCon framework directly on top of the original Stable Diffusion model without ControlNet, and similarly observed suboptimal results. We attribute this to the absence of explicit structural conditioning in the base model, which makes it difficult to enforce accurate region-level control during generation.\nIn contrast, our approach leverages structurally conditioned diffusion backbones such as ControlNet, which enable stronger spatial guidance and better content–annotation alignment. We will clarify this design choice in the revised manuscript."}]

Author rebuttal:

Meta-review: Summary: 
This paper presents ReCon, a training-free augmentation framework that improves region controllability in diffusion-based data generation for object detection. When adding rectification and alignment modules during sampling, it achieves consistent gains across datasets and detectors.

Strengths: 
The method is simple, modular, and requires no finetuning. It is validated thoroughly with diverse benchmarks and consistently improves both fidelity and detection performance.

Weaknesses: 
Reviewers initially raised concerns about heuristic design choices, runtime overhead, and unclear ablations. The rebuttal provided timing analysis, clarified experiments, and added missing results, which largely resolved these issues.

Overall, this paper provides a practical and well-supported contribution. ACs recommend acceptance.
Decision: Accept (spotlight)