DiffuDETR: Rethinking Detection Transformers with Denoising Diffusion Process
Authors: Youssef Ahmed Nawar
Mohamed Badran
Marwan Torki
Venue: ICLR 2026 Poster (2026)
Keywords: Object Detection
Diffusion Models
DETR
Query Generation
Deep Learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: In this paper, we present DiffuDETR, a novel approach that formulates object detection as a conditional object query generation task, conditioned on the image and a set of noisy reference points. We integrate DETR-based models with denoising diffusion training to generate object queries' reference points from a prior gaussian distribution. We propose two variants: DiffuDETR, built on top of the Deformable DETR decoder, and DiffuDINO, based on DINO’s decoder with contrastive denoising queries (CDNs). To improve inference efficiency, we further introduce a lightweight sampling scheme that requires only multiple forward passes through the decoder. Our method demonstrates consistent improvements across multiple backbones and datasets, including COCO2017, LVIS, and V3Det, surpassing the performance of their respective baselines, with notable gains in complex and crowded scenes. Using ResNet-50 backbone we observe a +1.0 in COCO-val reaching 51.9 mAP on DiffuDINO compared to 50.9 mAP of the DINO. We also observe similar improvements on LVIS and V3DET datasets with +2.4 and +2.2 respectively. The code is available at https://github.com/MBadran2000/DiffuDETR.

Published as a conference paper at ICLR 2026
DIFFUDETR:
RETHINKING DETECTION TRANSFORMERS WITH DENOISING DIFFU- SION PROCESS
Youssef Nawar* 1,2.3 Mohamed Badran" 1.3 Marwan Torki
Alexandria University, Technical University of Munich Applied Innovation Center youssof wawasoff.awwr@eee um. d les-mohamedmostaff aabadraa, m. m.torki m.torki]@alexxe e0leexu.
ABSTRACT
In this paper, we present DiffuDETR, novel approach that formulates object detection as conditional object query generation task, conditioned on the im- age and of noisy reference points. We integrate DETR-based models with denoising diffusion training to generate object queries' reference points from prior gaussian distribution. We propose two variants: DiffuDETR, built on top of the Deformable DETR decoder, and DiffuDINO, based on DINO's de- coder with contrastive denoising queries (CDNs). To improve inference effi ciency, we further introduce lightweight sampling scheme that requires only multiple forward passes through the decoder. Our method demonstrates con- sistent improvements across multiple backbones and datasets, including COCO 2017, LVIS, and V3Det, surpassing the performance of their respective base- lines, with notable gains in complex and crowded scenes. Using ResNet-50 back- bone we observe a +1.0 in COcO-val, reaching 51.9 mAP on DiffuDINO com- pared to 50.9 mAP of the DINO. We also observe similar improvements LVIS and V3DET datasets with +2.4 and +2.2 respectively. The code available at https://githus. com htt../////..aaa//0/00//a00 wwt.://200000000000
INTRODUCTION
Object detection is fundamental task in computer vi- sion. has gained much attention in recent years for its wide use in real-world applications. Object detection can be decomposed to two more primitive tasks: object localization and object classification. Traditional meth- ods depend heavily on predefined bounding boxes (Liu
let al.
2016), CPU-intensive selective search (Girshick 2014,, and region proposals networks (Girshick Cai Vasconcelos, 2018) propose candidate locations. These methods, while effective, limit flexi- bility and generalizability network training.
et al.
2015
Initial deep learning approaches (Girshick) 2015 Cai & <Vasconcelos 2018) require a of predefined anchor boxes and heuristics to generate proposals. This method involves one-to-many label assignment strategy, wherein each ground-truth bounding box is matched with multiple points in the detector's predic- tions. Despite their strong performance, these detectors depend heavily on several manually designed components, such as the predefined anchor boxes or non-maximum suppression for postprocessing.
Figure 1: Performance of DiffuDINO against other DETR and CNN-based models using ResNet-50 (He et al. 2016) backbone.
Later, DEtection TRansformer (DETR) (Carion et al. 2020) proposed an end-to-end training ob- jective by solving the problem bipartite matching between set of predictions and ground truths. These architectures rely on object queries, where each query matches exactly one object what also known one-to-one matching. This approach simplified the training objective and achieved SOTA results in both object detection. However, suffers from query initialization problem.
'These authors contributed equally this work.

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Published as a conference paper at ICLR 2026
Denoising Diffusion Probabilistic Models (DDPM) (Ho al. 2020 Song et al. 2020) were introduced as a probabilistic framework for image generation, achieving state-of the-art art (SOTA) performance in this domain. The core idea of DDPM is to model the process of gradually adding noise to a clean image, eventually reaching pure noise. The model learns to reverse process: starting from noisy data, it attempts recover the original clean image at each timestep. To generate new images, DDPM samples random noise from a learned distribution and sequentially denoises it through the reverse process, ultimately producing high-quality image. Over time, this method has been successfully extended to a wide range of computer vision tasks, demonstrating its versatility and effectiveness.
Building on previous work, we propose DiffuDETR, a framework built upon DeformableDETR and DiffuDINO, built on DINO, which shares DETR's backbone that extracts multi-scale image features, an encoder that employs multi-scale deformable attention within its transformer layers, transformer decoder that applies cross-attention between initial object queries and the encoder's image features, and an MLP head that decodes each object query into a class label and bounding-box coordinates. We propose new query initialization technique that aligns with the objective of denoising diffusion models to sample from the normally distributed reference points. This method avoids the inconveniences of query reference points initialization in DETR variants. Figure shows the convergence of our proposed DiffuDINO against other DETR-based models, while it required more epochs due to the slow nature of training diffusion models. The performance surpasses DINO after 50 epochs of training on COCO dataset, which tends to deteriorate after 36 epochs.
We summarize our contributions in the following:
1. We represent object detection with detection transformers as a diffusion denoising process by denoising queries' reference points.
2. We introduce two models, DiffuDETR and DiffuDINO, built upon Deformable DETR and DINO, respectively.
3. We conduct extensive experiments with our models multiple benchmark datasets and conduct extensive ablation studies to validate their effectiveness.
2 RELATED WORK
2.1 DIFFUSION MODELS
Denoising diffusion models have emerged as powerful generative frameworks in computer vision, demonstrating exceptional performance in tasks such as image generation (Austin al. 2021 Avra- hami et al. 2022), super-resolution (Gao et al. (003)), inpainting (Lugmayr et al. 2022). and editing. These models progressively learn to reverse diffusion process that adds noise data, enabling them to synthesize high-quality and diverse samples.
Beyond generative tasks, diffusion models have also proven useful in discriminative settings, in- cluding image segmentation (Amit et al., [022 |Baranchkk et al., 2021 Brempong et al., (022). classification (Chen al., 2023a),, and anomaly detection (Wolleb al. 2022). Their ability to learn strong latent representations highlights their potential in broader representation learning con- texts, making them an increasingly popular choice across multiple vision domains (Croitoru al. 2023
While diffusion models have seen significant success generative and discriminative tasks, their application to object detection remains relatively underexplored. Only few works (Chen et al. 20236) have investigated generative diffusion models for detection, and the progress in this area noticeably lags behind that in segmentation. This discrepancy arises because segmentation tasks are naturally formulated in an image-to-image fashion, which aligns closely with the denoising and generative process of diffusion models. In contrast, object detection is inherently a set prediction problem, requiring the model to generate a discrete set of object candidates and assign them to cor- responding ground truth objects (Ren et al. 2015). This difference introduces unique challenges for diffusion-baal based approaches, as the generation of unordered object queries and accurate localization is conceptually less straightforward than reconstructing pixel-wise maps.
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Transformer Encoder
Time Step UU(0-1)
Transformer Decoder
Noisy References
Ground Truth Boxes
Multi Scale Features
Init Learnable Queries
The DiffuDETR model framework involves transformer encoder that encodes multi- scale visual features from the backbone. Ground-truth object reference points are introduced with Gaussian noise, where the noise level is controlled by time step. These noisy reference points, along with Iearnable content queries, are processed by the transformer decoder, which denoises them based the encoded features produce precise object localizations during inference.
Figure 2:
2.2 DETECTION TRANSFORMER
DEtection TRansformer (DETR) (Carion et al. 2020) revolutionized object detection by reformu- lating it direct prediction problem, eliminating the need for hand-crafted components such as anchor boxes, region proposals, or non-maximum suppression. At its core, DETR employs an encoder-decoder transformer architecture: the encoder processes image features extracted by CNN backbone, while the decoder operates on a fixed set learnable object queries. Each query acts as placeholder for a potential object and interacts with encoded features through cross-attention. Training is performed end-to-end using bipartite matching loss based on the Hungarian algorithm, which enforces one-to-one alignment between predictions and ground-trun objects. This design enables DETR to predict all objects in a single forward pass, making it both conceptually simple and architecture-agnene agnostic.
Despite its conceptual clarity, DETR faces practical challenges. Since its queries are initialized as zero embedding vectors without explicit spatial priors, the model must learn query-to-object align- ment entirely from scratch, leading to slow convergence and unstable training. These limitations have motivated two complementary directions of research: improving query initialization and de- signing more effective training objectives.
The first direction focuses on enhancing query representation and initialization. DAB-DETR (Dy- namic Anchor Box DETR) (Liu et al.) (202)) reformulates decoder queries explicitly anchor box coordinates- center, width, and height-which which are dynamically updated across decoder layers. By incorporating positional priors directly into the queries, DAB-DETT improves alignment with image features and refines localization progressively, resulting in faster convergence and higher accuracy. In parallel, Deformable DETR (Zhu al., 2020) introduces Deformable Attention, which restricts attention to sparse set of spatially relevant sampling points, allowing the model to focus on key ob- ject regions. Its two-stage variant further strengthens query content by generating region proposals in the encoder (topK proposals) and refining them in the decoder, making queries maage-dependent. Together, these works demonstrate that better-designed queries with spatial priors and dynamic up- dates can significantly enhance convergence and detection performance.
The second line of research focuses on auxiliary training tasks that enhance optimization and sta- bility. DN-DETR (Li et al. 2022) introduces a denoising auxiliary task, where noisy ground-truth boxes and labels are injected during training, and the model learns to reconstruct the correct tar- gets. This additional supervision alleviates instability in bipartite matching and accelerates conver- gence. Building upon this idea, DINO (Zhang et al. 2022) incorporates a contrastive denoising (CDN) auxiliary task combined with mixed query selection. Reference points are sampled from high-confidence encoder outputs, while their content queries are initialized with learnable class em- beddings, aligning better with the denoising objective. Furthermore, DINO introduces hard nega-
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tives strengthen the auxiliary contrastive loss, yielding improved robustness and overall detection performance.
Building these advances, introduce DiffuDETR, a diffusion-based object detector that lever- ages a generative denoising process to produce object query anchors from noise. By progressively denoising noisy reference points, DiffuDETR generates better-initializee queries that provide strong starting points for the decoder, effectively addressing limitations in query alignment and conver- gence. At the same time, the denoising process serves an improved training objective, guiding the model to recover precise object locations and class predictions. This dual advantage of enhanced query anchor initialization and a denoising-drre -riven learning signal allows DiffuDETR to achieve greater training stability and superior detection performance compared to existing DETR variants.
Beyond these two research directions, several additional approaches have explored alternative path- ways for improving DETR-based detectors. Many-to-one supervision strategies (Zhao et al.) [024] Ouyang-Zhang et al. 2022) relax the strict one-to-one matching constraint to provide richer supervi- sory signals. Other works introduce multi-route decoding to enhance information flow (Zhang al.) 2025) and improve optimization. Aside from architectural changes, loss-function refinements aim to better align classification and localization objectives (Cai et al. 2024). While these directions offer promising directions for advancing DETR-style models, they fall outside the scope this paper, which focuses on diffusion-bait based query initialization and denoising- driven training.
2.3 OBJECT DETECTION As GENERATION TASK
Recent studies have started to view object de- tection through the lens of generative modeling, moving beyond traditional discriminative for- mulations. this perspective, detection is cast the process of generating structured outputs (bounding boxes and class labels) conditioned on image features. Two notable directions are sequence generation and denoising diffusion.
Feed Forward Network
Multi Soale Dufermatie Attetton
Pix2Seq (Chen al.) 2021) one of the first works to explore this paradigm, formulating de- tection sequence generation task. Bound- ing boxes and class labels are represented as discrete tokens, and an encoder-decoder ar- chitecture is trained to autoregressively gen- crate these tokens conditioned on the image and previously generated outputs. By do- ing so, Pix2Seq eliminates the need for hand- crafted components such proposal genera- tion or bounding box regression, offering a sim- ple, generic formulation of detection.
Tie Stop Embedciing
Teee Mey -U3-11
Figure 3: DiffuDETR's decoder iteratively de- noises noisy object reference points using multi- scale encoded features, integrating self-attentinn, deformable attention, feed-forward networks, and time-step embeddings to refine queries across dif- fusion steps.
In contrast, DiffusionDet (Chen et al., 2023b) formulates detection as denoising diffusion process. Built on the Sparse R-CNN decoder (Sun 202Tp, DiffusionDet replaces fixed proposal boxes with noisy ones, which are progressively refined into accurate predictions. During training, ground-truth boxes are diffused into random distributions, and the model learns to reverse this noising process. At inference, boxes sampled from Gaussian distribution are iteratively denoised through multiple cascaded decoder stages, enabling progressive refinement and flexibility in the number predictions.
While Pix2Seq treats detection as language modeling and DiffusionDet as denoising noisy proposal boxes, our work DiffuDETR continues along the diffusion direction but adapts it to DETR. Specif- ically, we formulate object detection denoising diffusion process that generates DETR's 's object queries' anchors directly from noise, introducing diffusion-based query generation into transformer- based detection.
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3
METHOD
3.1 PRELIMINARIES
3.1.1 DETR
The DETR model CCarion et al. (2020) uses an image feature extracted as a backbone for feature extraction, followed by Transformer encoder that processes the feature map into sequences. The decoder, which utilizes learned object queries, predicts bounding boxes and object classes. The Hun- garian Matcher used to match predicted boxes with ground truth by minimizing cost matrix based on class and bounding box errors. Deformable DETR (Zhu et al. 2020) introduced deformable at- tention, where each query generates reference points facilitate more efficient computations. This approach allows the queries to leverage multi-scale features, improving object localization. Addi- tionally, Deformable DETR introduced 2-stage initialization method, where queries are initialized with the top-k encoder proposals.
3.1.2 DENOISING DIFFUSION PROBABILISTIC MODELS
In Denoising Diffusion Probabilistic Models (DDPM), the forward process refers to the gradual addition noise to an image over a series of timesteps, which transforms a clean image Zp into noisy vector 24- This process is performed in series of steps, each adding small amount of Gaussian noise to the image, progressively degrading it. Mathematically, this forward diffusion process is defined as:
(<>||==11) = Wmx; V1 11--Ba 1, B,T)
(1)
where Be is the noise scheduler to control the mean of the added noise, and M is the normal distri- bution of the added noise.
During sampling, we can generate samples by sampling a noisy image ør ~ NN,,,), we update the noisy image using the following equation:
1 Vai
VI-ai ai 1 ā.
Tt-1
It
co( + oz.2
where a₁ = Br and ae = -I2T_ g= as and Be scheduled to control the mean of the noise added to the original image. NO, 1) and are used control the stochasticity of sampling.
3.2 DIFFUDETR
Our models build upon the Deformable DETR framework, retaining the encoder-decoder trans- former architecture with multi-scale deformable attention. DiffuDETR is built on Deformable- DETR and DiffuDINO adds the CDNs introduced in DINO (Zhang et al.] [2022). The key mod- ification is introduced at the training scheme. We adopt a diffusion-like training where reference points are treated as wwwddmmnnsioal latent diffusion variables and refined through iterative de- noising process to enhance object localization. During training, we use a diffusion schedule with 100 timesteps T 100, which is significantly fewer than the typical 1000 steps T 1000 used in standard diffusion models. This reduction made possible by the low dimensionality of the diffusion space, which allows efficient sampling during inference.
The complete architecture is illustrated in Figure The encoder is similar to that introduced in Zhu al. (2020), which extracts multi-scale visual features from the input image using deformable atten- tion modules, effectively capturing rich contextual information at various spatial resolutions. These encoded features serve the foundational representation for the detection pipeline. The transformer decoder layer shown in [egure]]tkke three inputs: the encoded multi-scale features Oene, the noisy reference points rt, and static learnable content queries. While the content queries encode seman- tic information about potential object classes, the noisy reference points provide spatial priors that guide the denoising process. The decoder iteratively refines these noisy queries conditioned on the encoded image features, effectively denoising and localizing objects.
gn FFFFFMMSAAA (SA(g (), Oane)
(3)
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Ground Truhh
DefomaNNe
DINIETRR
DINO
DYTLDNO
Figure 4: Qualitative comparison between Deformable DETR, DifiuDETR, DINO, and DifiuDINO on COCO 2017 Validation set. Only predictions with confidence scores above 50% are shown.
where yn represents the th layer queries, represents the timestep embedding, Oene is the output multi scale features of the encoder. SA stands for self-attention, MSDA stands for Multi-scale de- formable attention that takes the noisy reference points and interpolates the sampled points the encoded features.
We introduce the query denoising task as simply diffusion process on the normalized object refer- ence points r E N representing N ground-truth objects in an image. Specifically, we sample initial time step ((, 100) reference points r, from a posterior Noise distribution given by:
((ra/r) = f(rgr,,
(4)
where r, are noisy reference points at diffusion step t, and 0² denotes the variance controlling the noise intensity. This formulation encourages the model to learn the conditional distribution of object locations. f stands for the forward process refers to the noise function used for noising conveniently chosen be a normal distribution; however, more options could suit different problems (Nachmani ct al. 2021)
At inference time, we generate object proposals by applying a deterministic DDIM sampler over a small number of timesteps. Concretely, for N object queries, we first sample K = 4 reference points per query from a standard Gaussian at the final diffusion step:
(i.k) rT
I), i= 1 N, Is
4.
We then perform iterative denoising for / - -,T-I,..... residual é = eø (Te, t) via the transformer decoder and update the reference points according to the DDIM update rule (Song et al. 2020):
I,..., 1. At each step, we predict the noise
T--1 0 I't
VI Cre
+ V
āi-1 é
(S)
0
Different noise schedulers have been proposed for diffusion models (Chen 2023), with performance varying across tasks. In our setting, inference requires only S decoding evaluations, where S < T. The outputs [r(((()) from these steps are taken as the final bounding boxes. This efficient sampling scheme adds only a few extra computations using the decoder while the backbone and encoder are once, resulting in only small increase in computation (GFL.OP)) compared to DINO

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Table 1: Comparison of various object detectors on the COCO 2017 validation using ResNet-50 and ResNet- 101 backbones.
Model
Epochs
AP
AP50
AP75
APs
AP
AP;
Nes.e (He
al.
2016)
DETR-DC5 Carion al.
2020
500
43.3
63.1
45.9
22.5
47.3
61.1
DN-Deformable DETR
2022)
50
48.6
67.4
52.7
31.0
52.0
63.7
MS-DETR (Zhao et al. 2024)
24
50.9
68.4
56.1
34.7
54.3
65.1
Salience DETR cf al.
2024)
24
51.2
68.9
55.7
33,9
55.5
65.6
MR-DETR (Zhang et al. 2025)
24
51.4
69.0
56.2
34.9
54.8
66.0
Pix2Seq (Chen et al. 2021] 1)
300
43.2
61.0
46.1
26.6
47.0
58.6
DiffusionDer (Chen et al. (202b)
46.8
65.3
51.8
29.6
49.3
62.2
Deformable DETR (Zhu et al.
2020)
50
48.2
67.0
52.2
30.7
51.4
63.0
Align-DETR (Cai et al. (2024)
24
51.4
69.1
55.8
35.5
54.6
65.7
DINO (hang al. 2022
36
50.9
69.0
55.3
34.6
54.1
64.6
DiffuDETR (Ours)
50
50.2
66.8
55.2
33.3
53.9
65.8
DiffuAlignDETR (Ours)
24
51.9
69.2
56.4
34.9
55.6
66.2
DiffuDINO (Ours)
50
51.9
69.4
55.7
35,8
55.7
67.1
ResNet-101 (He et al.
2016)
DETR-DC5 Carion al. 2020
50
43.3
63.8
46.4
21.9
48.0
61.8
DAB-DETR RCR Liu et al. 2022
50
46.6
67.0
50.2
28.1
50.5
64.1
DN-DETR RDC5 (Li ct 2022)
50
47.3
67.5
50.8
28.6
51.5
65.0
MR-DETR İZang et al. 2025)
12
51.4
68.6
55.7
34.3
55.1
66.7
Pix2Seq (Chen al. 2021)
300
44.5
62.8
47.5
26.0
48.2
60.3
DiffusionDet (c (Chen et al.
2023b)
47.5
65.7
52.0
30.8
50.4
63.1
DINO (Zhang et al.) (2022)
50.0
67.7
54.4
32.2
53.4
64.3
Align-DETI (Car al. 2024)
12
51.2
68.8
55.7
32.9
55.1
66.6
DiffuDINO (Ours)
12
51.2
68.6
55.8
33.2
55.6
67.2
DiffuAlignDETR (Ours)
12
51.7
69.3
56.1
34.0
55.6
67.0
4 EXPERIMENTS
4.1
SETUP
We evaluated DiffuDETR multiple benchmark datasets to assess its performance across varying levels of object density, diversity, and scale.
COCO 2017. The COCO 2017 dataset (Lin et al. 2014) comprises 80 object categories, with 118,287 training images and 5,000 validation images. serves as a standard benchmark for object detection, segmentation, and captioning tasks, featuring diverse range of everyday scenes.
LVIS. The LVIS dataset (Gupta et al. 2019) includes 1,203 object categories, with 100,170 train- ing images and 19,809 validation images. It is designed to address long-tail object distributions, featuring large vocabulary of object categories with varying frequencies.
V3DET. The V3DET dataset (Wang al. 2023) encompasses 13,204 object categories, with 183,354 training images and 29,821 validation images. a large-scale, richly annotated dataset featuring detection bounding box annotations for vast number of object classes.
For evaluation, we adopt the standard metrics defined by each benchmark. On COCO, we report mean Average Precision (AP) averaged across IoU thresholds from 0.5 to 0.95, together with AP loU thresholds of 0.5 (APs0) and 0.75 (AP7s), as well as scale-specific AP for small, medium, and large objects. On LVIS, follow its official evaluation protocols, which extend the COCO-style AP by also reporting performance across categories with different frequencies: rare (AP,), common (APc), and frequent (AP,). These standardized measures provide comprehensive view of detector performance across imbalanced category distributions.
4.2 RESULTS
Table compares our proposed models with existing detectors on the COCO 2017 validation set. Using ResNet-50, DiffuDETR improves upon its baseline Deformable DETR by achieving 50.2

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Table 2: Comparison of DINO and DIffuDINO on the LVIS validation set using ResNet-50 and ResNet- 101 backbones. All models are trained for 12 epochs.
Model
AP
AP
AP
AP.
APm
AP:
AP,
AP,
AP/
f
Net-00
He et al. 2016)
DINO (hang al.
2022
26.5
35.9
27.8
20.0
35.2
40.9
9.2
24.6
36.2
DiffuDINO (Ours)
28.9
38.5
30.8
20.7
37.5
46.4
13.7
27.6
36.9
ResNet- 101
(He al.
(0066)
DINO (Zhang et al. DiffuDINO (Ours)
2022
30.9
40.4
32.8
23.2
40.5
46.3
13.9
29.7
39.7
32.5
42.4
34.8
23.5
43.4
49.7
13.5
32.0
41.5
Table 3: Comparison of DINO and DIffuDINO on the V3DET validation set using ResNet-50 and Swin-B backbones. All models are trained for 24 epochs.
Table 4: DiffuDINO performance with dif- ferent diffusion noise distributions on the COCO 2017 validation set.
Model
AP
AP50
AP75
Diffusion Noise
AP
AP50
AP75
ResNet-50 (He ct al.
2016)
DINO Zhang al. 2022
33.5
37.7
35.0
Beta
49.5
66.7
53.8
Sigmoid Gaussian
50.4
68.0
54.7
DIffuDINO (Ours)
35.7
41.4
37.7
Gaussian
51.9
69.5
56.3
Swin-B (Liu et al.
20221)
DINO Zhang al. 2022)
42.0
46.8
43.9
DiffuDINO (Ours)
50.3
56.6
52.9
AP compared to 48.2, while also outperforming DN-Deformable DETR (48.6 AP), showing that diffusion-based denoising task provides stronger training signal than DN-DETR denoising strategy. Similarly, DiffuDINO surpasses its baseline DINO, reaching 51.9 AP versus 50.9, with consistent improvements across AP50. AP75, and scale-specific metries. When compared with other generation-inspired approaches, both DiffuDETR and DiffuDINO significantly outperform Diffu- sionDet (46.8 AP) and Pix2Seq (43.2 AP), highlighting the advantages of integrating the diffusion denoising process with DETR-style query generation. With ResNet-101, DiffuDINO further im- proves over DINO (51.2 vs. 50.0 AP), demonstrating that our approach consistently strengthens DETR-based detectors across different backbones. We additionally introduce DiffuAlignDETR, built upon Align-DETR Cai et al. (2024), and observe that diffusion refinement provides a con- sistent boost under standard COCO training schedules. With ResNet-50 backbone and the 2x schedule (24 epochs), Align-DETR achieves 51.4 AP, whereas DiffuAlignDETR improves this to 51.9 AP. Similarly, with ResNet-101 under the 1x schedule (12 epochs), DiffuAlignDETR reaches 51.7 AP compared to 51.2 AP for the baseline.
We show the results on LVIS validation set in Table 2 where compare DiffuDINO with its baseline DINO under both ResNet-50 and ResNet-101 backbones, trained for 12 epochs. With ResNet-50, DiffuDINO achieves 28.9 AP, improving over DINO's 26.5 by +2.4 AP. With ResNet- 101, DIN continues to outperform DINO (32.5 vs. 30.9 AP), with improvements in medium and large objects as well as common and frequent categories.
We present results on the V3DET validation set with ResNet-50 and Swin-B backbones in Table trained for 24 epochs. With ResNet-50, DiffiDINO surpasses its baseline DINO by +2.2 AP (35.7 vs. 33.5), alongside clear improvements in AP (+3.7) and APTs (+2.7). The performance gap becomes more pronounced with the stronger Swin-B backbone, where DIffuDINO achieves 50.3 AP, outperforming DINO's 42.0 by large margin of +8.3 points.
Figure visually compares detection results across four models: Deformable DETR, DiffuDETR, DINO, and DiffuDINO samples from COCO 2017 Validation. The qualitative examples high- light the consistent improvements introduced by our diffusion- models, particularly in crowded scenes with multiple overlapping objects. DiffuDETR exhibits more accurate and complete local- ization of instances compared to Deformable DETR, while DiffuDINO further refines predictions beyond DINO, reducing missed detections and improving boundary alignment. These visualizations support the quantitative results and confirm that the biffusion-based denoising process yields clearer and more precise predictions, especially in challenging, densely populated regions.
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4.3
ABLATION STUDY
4.3.1 DIFFUSION NOISE DISTRIBUTIONS
Table reports the results using different noise distributions. Among the tested, Gaussian distribu- tion consistently achieves the best performance across all metrics, yielding 51.9 AP. In comparison, Beta noise distribution underperforms relative to Gaussian noise, with up to 2.4 AP lower. Sigmoid Gaussian shows competitive results. Our primary intuition for using the sigmoid Gaussian distri- bution to avoid clipping values from the diffused Gaussian points (ensuring that reference points are valued between (0 and 1). However, it still lags behind Gaussian noise overall. This shows that empirically Gaussian distribution still prevails as the more suitable distribution even in detection tasks.
Table 5: DiffuDINO performance with different noise schedulers on the COCO 2017 validation set.
Original Sugral Proerved
Scheduler
AP
AP50
AP75
AP.
APm.
AP:
Cosine
51.9
69.5
56.4
35,9
55.8
67.1
Oandaloo Alyaa (60)
0.5
Boo
Linear
51.6
69.1
56.2
35.6
55.8
66.4
Square Root
51.4
68.9
56.0
35,3
55.4
66.0
0.6
Table 6: Comparison of DINO and DiffuDINO with differ- ent Decoder Evaluation on the COCO 2017 validation set. We report AP metrics along with FLOPs (in GigaFLOPs) and activations (in millions).
9.4
11.5
Model
D.E.
AP
FLOPs (G)
Activations (M)
Norashoed Step
DINO
50.9
244.5 1 25.5
673.1 1 66.5
DiffuDINO
51.6
244.5 25.5
673.1 = 66.5
Figure 5: Different Schedulers noise re- tained plot against timesteps.
DIffuDINO
51.9
285.2
+ 27.1
27.1
871.0 = 72.7
DiffuDINO
51.8
326.0
28.7
1068.9 = 79.0
DiffuDINO
10
51.4
427.9
32.7
1563.6 + 94.7
4.3.2 DIFFUSION NOISE SCHEDULERS
We compare the impact of different noise schedulers on DiffuDINO performance in Table 5 The cosine scheduler achieves the best overall results, reaching 51.9 AP. The linear scheduler performs competitively, particularly on medium-sized objects where it matches cosine at 55.8 AP m. but slightly trails behind in other metrics. The square root scheduler shows the lowest performance among the three, with a drop of around 0.5 AP compared cosine. This is expected because cosine schedulers tend to retain more of the original signal in later timesteps as shown in Figure 5 These results indicate that cosine scheduling provides a smoother distortion for reference points as time steps < 100 while still completely distorting the reference points at 100.
4.3.3 NUMBER OF DECODER EVALJUATIONS
Table/[shows the effect of varying decoder evaluation on DiffuDINO performance compared to the DINO baseline on the COCO 2017 validation set. With a single decoder evaluation, DIN already surpasses DINO by +1.7 7 AP while maintaining identical computational cost in both FLOPs (244.5 = 25.5 G) and activations (673 + 66 M). Increasing the number of decoder evaluations to three yields the best overall results, reaching 51.9 AP with improvements across all object scales. However, this comes with a moderate increase in computation to 285.2 27.1 GFLOPs and 871 73 M activations. Further increasing the decoder evaluations to five ten does not yield additional per- formance gains, with AP slightly declining while computational costs continue to grow significantly, reaching 428 + 33 GFLOPs and 1564 + 95 M activations at ten steps. This demonstrates that our approach enables effective sampling. with only three decoder evaluations being sufficient to achieve the best results while adding minimal computation overhead compared the baseline.

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Table DiffuDINO performance with different numbers of decoder evaluations on COCO val2017, averaged over 5 random initializations. Results are reported mean + standard deviation.
D.E.
AP
AP
AP75
AP,
AP.
APt
I
51.68 + 0.02
69.28 + 0.02
55.89
+ 0.04
35.50 + 0.09
55.58
+ 0.04
66.96 + 0.10
51.95 = 0.03
69.54 0.02
56.32 = 0.05
35.92 = 0.08
55.81 0.01
67.18 0.05
51.83 0.01
69.24
56.21 = 0.05
35.82 0.06
55.71 0.06
67.02 0.05
10
51.49 + 0.08
68.54 0.09
56.02 = 0.08
35.56 - 0.12
55.46 + 0.15
66.83 + 0.04
Table 8: Comparison of DiffuDINO performance across varying numbers of decoder evaluations COCO Dense and Sparse scene subsets. The first row of each section reports the baseline DINO results, followed by DiffuDINO results using different numbers decoder evaluations. Metries are reported as mean + standard deviation over 5 random initializations.
D.E.
AP
AP50 COCO Sparse Scenes
AP75
APS
APM
APL
DINO
57.00
73.37
62.63
36.14
55,95
59.26
58.48 + 0.05
74.68
0.031
6 0.02
37.78
= 0.10
57.66
68.34
0.13
3
58.65
+ 0.03
74.75
0.03
63.85 + 0.04
38.07
- 0.19
57.81
+ 0.05
68.55 0.06
5
58.50
+ 0.04
+ 0.05
63.71 + 0.08
37.72
+ 0.15
57.57
+ 0.14
68.46 + 0.04
10
58.16 + 0.04
73.94 0.03
63.41 + 0.07
37.36
= 0.14
57.27 0.15
68.10 + 0.04
COCO Dense Scenes
DINO
43.72
62.29
47.81
33.98
51.95
66.63
1
44.53 + 0.07
6
47.87 0.10
34.45 0.11
53.15
0.16
61.48 + 0.16
3
44.88
+ 0.02
48.39 0.09
34.96
53,40
0.06
61.60 + 0.06
5
44.85
1 0.05
63.36
48.40
0.11
3.07
53.46
61.61 0.07
44.57 0.07
62.69
48.23 0.11
34.79 0.15
61.31 0.09
4.4
SENSITIVITY INITIALIZATION NOISE
To assess the robustness of DiffuDINO with respect to initial noise, evaluate the model across five independent runs with different random seeds and report the mean and standard deviation of AP and related metrics. As shown in Tables DiffuDINO exhibits consistently low seed-to-seed variance across all decoder evaluation settings (1, 3, 5, and 10 steps). On COCO with a ResNet- 50 backbone, the variation across seeds remains below +0.2 AP, demonstrating that the model's predictions are highly stable despite changes in initialization noise.
To further analyze stability under different scene complexities, split the COCO validation into a sparse scenes subset (images with 10 or fewer objects) and a dense scenes subset (images with more than 10 objects). DiffuDINO maintains the same level of robustness in both subsets. As shown in the Dense and Sparse results in Table g the standard deviation remains below +0.2 AP across all metrics, even under large variations in object density. Moreover, DiffuDINO consistently improves over the baseline DINO in both crowded and sparse scenes, demonstrating that diffusion- based refinement not only enhances accuracy but also preserves stability across seeds.
CONCLUSION
In this work, we represented object detection with detection transformers as a diffusion denois- ing process by progressively denoising queries' reference points. We introduced models, Dif- fuDETR and DiffuDINO, built upon Deformable DETR and DINO, respectively. Our approach en- ables effecctive sampling inference, where only three decoder evaluations are sufficient achieve the best results while adding minimal computation overhead compared to the baseline. Extensive experiments on COCO, LVIS, and V3Det demonstrate that our method consistently improves over the baselines across all datasets. Furthermore, we show that Gaussian noise provides the most suitable training signal, consistently outperforming alternative noise distributions. In addition, we observe that cosine scheduler achieves the best performance among the different noise scheduling strategies we tested. Moreover, our multi-seed analysis confirms that DiffuDINO is highly robust to initialization noise. Finally, believe this work opens up new directions for integrating generative and autoregressive approaches into object detection, offering fresh perspectives beyond traditional discriminative formulations.
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A APPENDIX
A.1 IMPLEMENTATION DETAILS
All experiments were implemented using the Detrex framework (Ren et al. 2023 For data pre- processing, we followed the default COCO augmentations provided by Detrex across all datasets to ensure consistent training pipelines. Unless otherwise stated, all models were optimized using the AdamW optimizer (Loshchilov & Hutter 2017) with initial learning rate of 1 x 10-1, weight decay x 10-1, and backbones, used batch size of 16, while models with the Swin-B backbone were trained with batch size due to memory constraints. Across all settings, we adopted four-scale feature maps from the backbone to ensure consistent multi-scal representation for detection.
(0.9, 0.999). For models trained with ResNet-50 and ResNet-101
A.1.1 TRAINING ON COCO 2017
For COCO 2017, we trained models with ResNet-50 and ResNet- 101 backbones (He et al. 2016) Models with ResNet-50 backbones were trained for epochs, applying a learning rate decay at epoch 40. Models with ResNet-101 backbones were trained for 12 epochs, with the learning rate decayed at epoch 11. We additionally retained the baseline DINO with ResNet-50 backbone for 50 epochs provide consistent reference. We found DINO after 36 epochs starts to deteriorate, so we compare our results to the best results achieved by the authors in their paper (Zhang et al. 2022
A.1.2 TRAINING LVIS
On the LVIS dataset, we trained both ResNet-50 and ResNet-101 backbones for 12 epochs, with total of 270k iterations. The learning rate was decayed by factors of 0.1 and 0.01 at 210k and 250k iterations, respectively, before terminating 270k iterations. To ensure comparability, we also trained the baseline DINO models with Re and ResNet-101 backbones following the same schedule.
A.1.3 TRAINING V3DET
For V3DET, we trained models with both ResNet-50 and Swin-B backbones (Liu et al. (02T) l for
24 epochs, reducing the learning rate epochs 16 and 22 by a factor of 10. A repeated sampler was used with the repeat factor set x 10-3 following the dataset protocol. For bascline compar- isons, used the official DINO results reported in the V3DET dataset paper and replicated their hyperparameters in our DiffuDINO experiments to ensure fair comparison.
A.1.4 DIFFUSION-BSED VARIANTS
All DETR-based models were trained with 900 object queries by default. Our DiffuDETR and DiffuDINO models introduced diffusion process with 100 decoder evaluations, where gaussian noise was progressively denoised. During inference, we reduced this to only 3 denoising steps for efficiency. In addition, adopted cosine learning rate scheduler for diffusion- models. We also conducted ablation studies to analyze the impact of different noise disturbances, diffusion schedulers, the number of queries, and inference decoder evaluations.
In the baseline DINO and DiffuDINO models, we employed 300 CDN denoising queries. For DifTuDETR, which is built upon Deformable DETR, did not adopt the two-stage variant but instead incorporated the encoder loss to enhance representation learning. Furthermore, we applied exponential moving average (EMA) updates to both DiffuDETR and DiffuDINO, EMA stabilizes training and improves convergence under the diffusion denoising objective.
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A.2 VISUALIZATION PREDICTIONS WITH DIFFERENT DECODER EVALUATION STEPS ON COCO
Figure 6 illustrates the effectiveness of DiffuDINO under varying numbers of decoder evaluation steps. Increasing the number of evaluations enables the model to detect more objects, particularly complex and crowded scenes. Notably, even with a single evaluation step, DiffuDINO already surpasses the baseline DINO, demonstrating stronger localization and recall in challenging scenar- ios. This highlights the model's 's robustness and its ability to scale detection quality with additional decoding steps. The quantitative results are summarized in Table 9
Cound
Obko
Cocoder Drakation
Decoce Dvt calonn 3
Docoder Enakation
Decodar S Snalation
Figure 6: Qualitative comparison of object detection results COCO 2017 validation set across different numbers decoder evaluation steps (t = 1, 3, 5, 10), showing DiffuDINO, baseline DINO with ResNet-50 backbone, and ground truth annotations. Only predictions with confidence scores above 50% are shown.
Table 9: Comparison of DINO and DiffuDINO with different decoder evaluation steps the COCO 2017 validation set.
Model DINO DiffuDINO
Decoder Evaluation
AP
AP 50
AP75
APs
APM
AP, L
50.9
69.0
55.3
34.3
54.1
64.6
51.6
69.2
55.8
35.5
55.5
66.9
DiffuDINO
3
51.9
69.5
56.3
35.9
55.8
67.1
DiffuDINO
51.8
69.2
56.2
35.8
55.7
67.0
DIffuDINO
10
51.4
68.5
56.0
35.5
55.4
66.8
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A.3 VISUALIZATION PREDICTIONS ON LVIS
Figure presents qualitative examples from the LVIS validation set, showing how DiffuDINO ben- efits from additional decoder evaluation steps. The model consistently produces more accurate de- tections in dense and diverse scenes, capturing objects that DINO often misses. Even at a single evaluation step, DifuUDINO demonstrates clear improvements over the baseline, highlighting its advantage in handling long-tail distributions and fine-grained categories. The corresponding quan- titative results are provided Table I0
rn Wihh Evelationn
1u with Decodo
Decoder Exilution
Figure 7: Qualitative comparison of object detection results on LVIS validation set across different numbers of decoder evaluation steps (1 1,3, 5). showing DiffuDINO, baseline DINO with ResNet- 50 backbone, and ground truth annotations. Only predictions with confidence scores above 50% are shown.
Table 10: Comparison of DINO and DiffuDINO with different decoder evaluation steps on the LVIS validation set.
Model DINO
Decoder Evaluation
AP
AP50 50
AP 75
AP.
AP
AP;
AP,
AP
AP,
26.5
35.9
27.8
20.0
35.2
40.9
9.2
24.6
36.2
DiffuDINO
27.0
36.4
28.6
19.5
35.8
44.1
12.0
26.1
34.7
DIffuDINO
3
28.9
38.5
30.8
20.7
37.5
46.4
13.7
27.6
36.9
DiffuDINO
28.2
37.6
30.2
20.2
36.5
45.6
11.9
26.9
36.7
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A.4 NUMBER OF QUERIES
We ablate the number of queries in Table [1 varying the number of queries DiffuDINO per- formance on the COCO 2017 validation set, with all models trained under a linear noise scheduler. Increasing the number of queries from 300 to 900 yields noticeable improvements, particularly for small (35.6 AP,) and medium (55.8 APm) objects. As expected, scaling more to 1500 queries pro- vides the best overall AP (51.9), demonstrating that approach continues to scale effectively with more queries. For consistency and fair comparison with baselines, however, all main experiments in this work conducted with the conventional 900 queries.
Table 1: DIffuDINO performance with different numbers of queries on the COCO 2017 validation set. All models are trained with linear noise scheduler.
No. of Queries
AP
APs
50
AP75 75
APg 8
AP,
AP
300
51.2
68,8
55.6
35.0
55,2
66.4
900
51.6
69.1
56.2
35.6
55.8
66.4
1500
51.9
69.5
56.4
35.0
55.5
66.6
A.5 NOISE DISTRIBUTIONS
In section 4.3.1 we ablate on different noise distributions. We use the following PDFs for sampling Gaussian Distribution
cxp ( (x (x-1)²²
fx(x) = 0v
-00 < <><<<<>>>> < 0.5, o = 0.5.
(6)
20²
and beta distribution
1 (0-1(1-2)2 0-11 0<<<l, 2
fx(x) B(a, 3)
Sigmoid Gaussian is simply
exp 1 (- (x (x=-)) p1) (x-1)² 20²
))
(x( sigmoid(
-00 <0
2v
Notice that due to the nature of gaussian distribution, noise would need to be clipped to ensure that normalized reference points are between and 1. Beta and Sigmoid Gaussian ensure that the limit is within the distribution itself and our chosen hyperparameters.
A.6 COMPUTATIONAL COMPARISON OF DETR VARIANTS.
In Table 12, we provide a detailed comparison of computational requirements across several DETR- based detection models, all using ResNet-50 backbone. We report both FLOPs (in GigaFLOPs) and activations (in millions). As shown, models that introduce denoising mechanisms such as DN DETR and DN Deformable DETR incur a noticeable increase computation compared to their non-denoising counterparts. This trend is consistent with the broader progression of DETR variants: newer models typically achieve stronger performance by introducing architectural refinements or additional training signals, but the cost of increased FLOPs and activation memory.
Within this landscape, DiffuDINO follows a predictable computational pattern. With a single de- coder evaluation, its cost matches that of its baseline DINO. Increasing the number of denoising steps naturally increases computation, yet the growth linear and controllable, allowing practition- to balance accuracy and efficiency depending on deployment constraints. This extended compar- ison provides a clear view the trade-offs across DETR variants and demonstrates that Diffu-DINO fits cleanly within the expected computational spectrum of modern DETR models.
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Compute cost comparison across detection models using ResNet-50, FLOPs are reported in GigaFLOPs and activations in millions (mean + std).
Table 12:
Model
D.E.
FLOPs (G)
Activations (M)
DETR
9.3
722.93
DN DETR
89.2
253.87 + 26.10
Deformable DETR
171.3
484.02 -2.166
DN Deformable DETR
231.3 25.1
602.40 = 64.72
Align DETR
25.5
673.05 = 66.46
MR Deformable DETR
258.0
709.06 = 73.95
DINO
25.5
673.06 = 66.46
Diffu-DINO
25.5
673.06 66.46
Diffu-DING
3
28.
870.96 = 72.74
Diffu-DING
5
326.0
28.7
1068.86 79.01
Diffu-DINO
10
427.9
+ 32.7
1563.61 = 94.70
Table 13: COCO results for DINO (baseline) and DiffuDINO across seeds under varying numbers of decoder evaluations using ResNet-50 backbone.
Model DINO
D.E.
Seed
AP
AP50
AP75
APs
APm
API
50.58
68.41
55.46
34.26
53.85
65.25
DiffuDINO
51.64
69.25
55.83
35.38
55.60
66.92
DiffuDINO
51.71
69.28
55.94
35.64
55.52
67.08
DiffuDINO
3
51.68
69.28
55.87
35.53
55.59
66.80
DiffuDINO
51.70
69.33
55.89
35.51
55.63
67.00
DiffuDINO
5
51.68
69.29
55.93
35.47
55.60
67.02
DiffuDINO
3
51.97
69.55
56.30
35.85
55.81
67.21
DiffaDINO
51.98
69.55
56.36
35.95
55.84
67.26
DiffuDINO
3
3
51.95
69.52
56.36
35,93
55.79
67.11
DIffuDINO
51.96
69.58
56.36
36.06
55.82
67.21
DiffuDINO
3
5
51.90
69.51
56.23
35,84
55.80
67.14
DIffuDINO
51.85
69.28
56.25
35.86
55.65
66.97
DiffuDINO
5
2
51.82
69.22
56.11
35.93
55.66
67.08
DifiuDINO
3
51.85
69.29
56.24
35.78
55.77
67.07
DiffuDINO
5
4
51.84
69.21
56.26
35.77
55.79
66.99
DifiuDINO
5
5
51.81
69.21
56.20
35.79
55.69
66.99
DiffuDINO
10
1
51.40
68.43
55.97
35.53
55.29
66.82
DiffuDINO
10
2
51.60
68.68
56.11
35.44
55.65
66.85
DIffuDINO
10
3
51.56
68.58
56.13
35.60
55.59
66.85
DiffuDINO
10
51.48
68.54
55.98
35.76
55.36
66.86
DINOuDINO
10
51.45
68.48
55.95
35.50
55.42
66.76
A.7 COMPREHENSIVE MULTI-SEED RESULT
To provide complete view of model stability under different complexities and data distri- butions, we report full results for three evaluation settings: COCO Dense scenes. COCO Sparse scenes, and the full COCO validation set. The results are Tables 13 14 and [5] where the first row of each table contains the baseline DINO scores, followed by DiffuDINO performance under different numbers of decoder evaluations. Across all three tables, DiffuDINO consistently surpasses the baseline DINO model both in Dense scenes and Sparse scenes while exhibiting remarkably small seed-to-seed variability.
A.8 USE OF LARGE LANGUAGE MODELS
Large Language Models (LLMs)) were utilized for minor edits and revisions during the writing of this paper, primarily to correct grammar, spelling, and improve clarity in certain passages. No content was generated or substantively altered by the LLMs.
18

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Published as a conference paper at ICLR 2026
Table 14: COCO sparse scenes results for DINO (baseline) and DIffuDINO across 5 seeds under varying numbers of decoder evaluations using ResNet-50 backbone.
Model
D.E.
seed
AP
AP50
AP75
APs
APm
API
DINO
57.00
73.38
62.64
36.14
55.90
59.26
DiffuDINO
58.47
74.70
63.51
37.91
57.62
68.22
DiffuDINO
2
58.52
74.70
63.52
37.69
57.73
68.35
DiffuDINO
3
58.42
74.66
63.51
37.87
57.59
68.23
DiffuDINO
58.47
74.65
63.55
37.68
57.60
68.40
DiffuDINO
5
58.56
74.72
63.57
37.76
57.78
68.54
DiffuDINO
58.65
74.75
63.86
38.10
57.91
68.53
DiffuDINO
2
58.64
74.72
63.91
37.77
57.80
68.59
DiffuDINO
58.72
74.77
63.89
38.31
57.78
68.63
DiffuDINO
4
58.63
74.80
63.81
38.08
57.78
68.46
DiffuDINO
58.65
74.73
63.80
38.11
57.79
68.55
DiffuDINO
58.57
74.57
63.82
37.81
57.64
68.45
DiffuDINO
2
58.50
74.56
63.66
37.92
57.48
68.42
DiffuDINO
3
58.44
74.45
63.61
37.51
57.53
68.44
DiffuDINO
58.52
74.55
63.78
37.65
57.79
68.48
DiffuDINO
5
58.48
74.49
63.72
37.73
57.43
68.54
DiffuDINO
10
58.15
73.95
63.33
37.54
57.27
68.03
DiffuDINO
10
2
58.18
73.96
63.35
37.30
57.05
68.13
DiffuDINO
10
3
58.22
73.99
63.49
37.42
57.43
68.10
DiffuDINO
10
58.18
73.90
63.41
37.40
57.40
68.14
DiffuDINO
10
5
58.11
73.92
63.48
37.14
57.22
68.13
Table 15: COCO dense scenes results for DINO (baseline) and DiffuDINO across 5 seeds under varying numbers of decoder evaluations using ResNet-50 backbone.
Model DINO
D.E.
seed
AP
AP50
AP75
APs
APm
API
43.72
62.30
47.81
33.99
51.96
66.63
DiffuDINO
44.47
63.22
47.77
34.30
53.26
61.47
DiffuDINO
2 44.51
63.25
47.81
34.46
53.00
61.48
DiffuDINO
3
44.64
63.29
47.99
34.50
53.36
61.24
DiffuDINO
4 44.48
63.12
47.82
34.38
52.98
61.68
DiffuDINO
5
44.59
63.34
47.98
34.61
53.16
61.58
DIffuDINO
44.88
63.71
48.53
35.01
53.40
61.54
DIffuDINO
2 44.93
63.65
48.44
34.88
53.48
61.62
DIffuDINO
44.86
63.63
48.30
35.02
53.31
61.63
DiffuDINO
4
44.88
63.65
48.37
34.85
53.43
61.69
DiffuDINO
44.89
63.66
48.31
35.07
53.39
61.55
DiffuDINO
1
44.83
63.36
48.29
34.83
53.40
61.63
DIffuDINO
44.77
63.22
48.28
34.89
53.39
61.60
DiffuDINO
3 44.88
63.37
48.46
34.90
53.50
61.70
DiffuDINO
44.88
63.45
48.44
35.00
53.45
61.63
DiffuDINO
5
44.91
63.44
48.53
34.99
53.58
61.50
DiffuDINO
10
44.49
62.59
48.16
34.74
53.29
61.32
DiffuDINO
10
2
44.64
62.71
48.38
34.61
53.54
61.35
DiffuDINO
10
3
44.63
62.86
48.27
34.91
53.32
61.16
DIffuDINO
10
44.63
62.64
48.25
34.98
53.35
61.31
DIffuDINO
10
5
44.49
62.66
48.10
34.73
53.29
61.42
19


Reviews:
[{"review_id": "v0Oyl6dMIy", "reviewer": "Reviewer_z7De", "summary": "In this paper authors introduced two models, DiffuDETR and DiffuDINO, to built upon Deformable DETR and DINO, respectively. Experiments enables effective sampling at inference, where only three decoder evaluations are sufficient to achieve the best results while adding minimal computation overhead compared to the baseline.", "questions": "The improvement suggestions are as follows:\n1.  Increasing research literature from 2024 to date，\n2. To add a comparison of the computational overhead of different algorithms， and\n3. Table 2 only selected DINO Zhang et al. (2022) for comparison. Why not compare more methods like in Table 1? More benchmark methods are needed.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "I believe this work opens up new directions for integrating generative and autoregressive approaches into object detection, offering fresh perspectives beyond traditional discriminative formulations.", "weaknesses": "The baseline chosen in the paper are not the current state-of-the-art method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "a6xqWzauE1", "reviewer": "Reviewer_tVk9", "summary": "This paper introduces DiffuDETR, a diffusion-based extension of the DETR to improve object detection. The model uses DETR’s transformer-based architecture, including a multi-scale feature extractor, deformable attention encoder, and cross-attention decoder, but introduces a new query initialization mechanism inspired by diffusion processes. Instead of relying on manually defined anchor or reference points, queries are sampled from a normal distribution, aligning with the diffusion objective and simplifying initialization. Experiments on the COCO dataset show that while diffusion training converges more slowly, DiffuDETR eventually surpasses DINO after sufficient epochs, demonstrating improved robustness and detection performance.", "questions": "Please refer to the weakness section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The topic is interesting and relevant, as it reformulates the detection transformer’s query prediction as a denoising process, offering a fresh perspective on object detection.\n2. The paper is generally well written and clearly organized, making it easy to follow and understand the proposed framework.\n3. The comparison tables demonstrate that the proposed method achieves competitive performance against baseline models.", "weaknesses": "1. Regarding the 100-timesteps schedule, recent diffusion models typically sample a single timestep during training since the model learns to denoise from one distribution to another. Therefore, the claimed efficiency improvement seems more relevant to inference rather than training.\n2. It is not clearly stated whether the paper uses a diffusion-specific loss function or a standard detection loss, clarifying this would help understand how diffusion is actually integrated into the training objective.\n3. The paper would benefit from visualizations of intermediate timesteps or distributions to better illustrate the denoising process and the behavior of the model during denosing.\n4. An analysis of the model’s sensitivity to the initialization of sampled queries would also strengthen the paper, as this is an important aspect of diffusion-based generation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7H611KcmKi", "reviewer": "Reviewer_4qDU", "summary": "This paper draws on the ideas of DDPM and transfers the generation process of image denoising to object detection. Different from the traditional DiffusionDet method, which denoises noisy boxes and then combines them with RCNN for detection, this paper integrates the denoising process into the attention process. Furthermore, based on the deformable DETR and the contrastive denoising queries (CDN) of DINO, it proposes two models, namely DiffuDETR and DiffuDINO, respectively. Comparisons with baselines and some contemporaneous methods on three datasets (COCO2017, LVIS, and V3Det) demonstrate that the proposed models achieve the best accuracy.", "questions": "Q1: Does the generation of initial queries still adopt the top-k approach? In Line 76 of the paper, it is mentioned that \"We propose a new query initialization technique that aligns with the objective of denoising diffusion models to sample from the normally distributed reference points.\n\", yet the illustration in Figure 2 still indicates the use of learnable initial queries.\nQ2: Is the denoising process reasonable? The method section shows that during training and inference, noisy boxes are not the main body of iteration; instead, they are used in the form of K to compute cross-attention with Q, assisting the iteration of Q. This step encourages the model to generate Q that conforms to the distribution of K, given that K represents noisy boxes. However, during inference, noisy boxes are directly sampled from a Gaussian distribution and iterated step-by-step following the DDPM paradigm. This iteration of noise does not align with the distribution of targets, yet the model still generates Q with a similar distribution to the noise, which may mislead the model.\nQ3: How are noisy boxes embedded? Are noisy boxes directly used as K for cross-attention computation, or are they mapped to the dimension of Q?\nQ4: Are the noisy boxes r at different time steps t independently and identically distributed (i.i.d.)? Or is the noise accumulated incrementally like in DDPM? If the latter is the case, I believe this should be more explicitly represented in Equation 4, and the meaning of \"q\" therein should also be clarified.\nQ5: In Equation 3, does the time step t embedding have different effects at different positions?\nQ6: Could you provide a detailed definition of \"Decoder Evaluation\"?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Integrates the denoising process with the query decoding mechanism of attention.\n2. Validates the two proposed models on three sufficient datasets.", "weaknesses": "1 The flow chart fails to effectively illustrate the network architecture, and it also appears somewhat small.\n2 The symbol \"f\" is used in both Equation 3 and Equation 4 but represents different meanings, which should be avoided.\n3. The methods used for comparison are somewhat outdated, as the latest version of DiffusionDet was released in 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1YxnLnXMAS", "reviewer": "Reviewer_feAq", "summary": "The paper proposes DiffuDETR, which reframes DETR-style object detection as denoising diffusion over object-query reference points. Two variants are introduced: DiffuDETR (on Deformable-DETR) and DiffuDINO (on DINO with CDNs). Training treats query anchors as low-dimensional diffusion variables; inference uses a lightweight sampler that runs only a few decoder passes. The method yields improvements across  different datasets.", "questions": "1. How sensitive are results to the random seed / initial noise used in the diffusion process? Please report mean ± std AP over multiple seeds, and, if possible, the seed-to-seed variance of key categories (crowded vs. sparse scenes).\n\n2. What is the effect of different sampling strategies (e.g., DDIM vs. DDPM, or other schedulers) on accuracy and compute? Do certain samplers yield better AP–FLOPs trade-offs or different optimal D.E. settings?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Visualizations suggest fewer misses and tighter localization in crowded scenes, matching the intended strengths of iterative refinement over queries.", "weaknesses": "1. Limited practical gain from diffusion sampling for detection. As Table 6 shows, increasing decoder evaluations to D.E.=3 introduces a sharp compute/activation rise (FLOPs ≈ +16%, Activations ≈ +29%) while delivering only a modest accuracy bump (≈ +2 AP). The accuracy–efficiency trade-off is therefore not clearly favorable for real-world deployment.\n\n2.Unclear procedure for choosing D.E. (decoder evaluations). Performance does not monotonically improve with larger D.E. in Table 6, yet the paper offers no principled rule (or validation protocol) for selecting D.E. This undermines reproducibility and makes the method harder to operationalize.\n\n3. Convergence claim vs. evidence mismatch. Lines 74–75 state the approach helps “address convergence,” yet Figure 1 suggests DiffuDINO requires more epochs to reach its performance than DINO. This weakens the claimed convergence advantage unless the authors clarify training protocol differences or provide additional curves.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)