ELDET: Early-Learning Distillation with Noisy Labels for Object Detection
Authors: Dongmin Choi
Sangbin Lee
EungGu Yun
Jonghyuk Baek
Frank C. Park
Venue: NeurIPS 2025 poster ()
Keywords: object detection
noisy label
early learning
Primary area: deep_learning
TL;DR: We propose a distillation-based training method for object detectors that mitigates both categorization and localization noise by leveraging the early-learning phase to guide robust detector learning across diverse datasets.

Abstract: The performance of learning-based object detection algorithms, which attempt to both classify and locate objects within images, is determined largely by the quality of the annotated dataset used for training. Two types of labelling noises are prevalent: objects that are incorrectly classified (categorization noise) and inaccurate bounding boxes (localization noise); both noises typically occur together in large-scale datasets. In this paper we propose a distillation-based method to train object detectors that takes into account both categorization and localization noise. The key insight underpinning our method is that the early-learning phenomenon - in which models trained on noisy data with mixed clean and false labels tend to first fit to the clean data, and memorize the false labels later -- manifests earlier for localization noise than for categorization noise. We propose a method that uses models from the early-learning phase (before overfitting to noisy data occurs) as a teacher network. A plug-in module implementation compatible with general object detection architectures is developed, and its performance is validated against the state-of-the-art using PASCAL VOC, MS COCO and VinDr-CXR medical detection datasets.

ELDET: Early-Learning Distillation with Noisy Labels for Object Detection
Dongmin Choi!"
Sanghin Lee
EungGu Yun!
Jonghyuk Baek
Frank C. Park
SAIGE SSoul National University
FFlitto
{dm choi, ab. lee, eg. yun>-saigeeai, jonghyuk. baw.kfiitto....oooooooooooo..o.. com, fcpdanu. ac kr
Abstract
The performance of learning- based object detection algorithms,, which attempt to both classify and locate objects within images, is determined largely by the quality of the annotated dataset used for training. Two types of labelling errors are prevalent: objects that are incorrectly classified (categorization noise) and inaccurate bounding boxes (localization noise); both errors typically occur together in large-scale datasets. In this paper, we propose a distillation-bl object detectors that takes into account both categorization and localization noise. The key insight underpinning our method is that the carly-learning phenomenon in which models trained on noisy data with mixed clean and noisy labels tend first fit to the clean data, and memorize the noisy labels later manifests earlier for localization noise than for categorization noise. We propose a method that uses models from the carly-learning phase (before overfitting to noisy data occurs) as teacher network. plug-in module implementation compatible with general object detection architectures is developed, and its performance is validated against the state-of-the-as using PASCAL VOC, MS COCO and VinDr-CXR medical detection datasets.
method to train
Introduction
Object detection is a fundamental task in computer vision that requires both accurate classification and precise localization of objects within images. Object detection essential for autonomous driv- ing [12 [3), medical imaging (20) (58 37 and
person
person
person:
many other applications that rely on knowing both the type and location of objects in images.
With few exceptions, methods for object detection are now almost entirely based on neural network trained on
(a) Localization nosse (b) Categorization moise (c) Combined moise
models, e.g., [43 46
35
6
44
26
Figure Comparison label noise types object detection: (a) Localization only. (b) Categorization
large image datasets such as PASCAL VOC [1]] and MS COCO (30). While network architectures and algorithms for data processing and training may still affect object detection performance, by and large the performance of object detectors is determined mostly by the size and quality of the datasets (38].
only. (c) Combined localization and catcgorization noise.
These authors contributed equally to this work.
"This work was primarily conducted while the author was at SAIGE.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Training data with noisy annotation
Teacher
Teacher
CLS Head
CLS Head
Backbone
Backbone
LOC Head
LOC Head
Distillation
Distillation
kdoy
EMA
BMMA
Student
Student
CLS Head
CLS Head
CLS Head
Backbone
Backbone
Backbone
LOC Head
LOC Head
LOC Head
febl
Time
Figure 2: Overview of the ELDET workflow. The early-stage model is initially trained dataset with noisy labels until memorization occurs at the the training iteration. Once the early-learning phase terminates for the localization task, teacher model (blue), initialized from the current state of the student model (orange), is employed to guide subsequent training through knowledge distillation with its parameters frozen. The teacher model is progressively updated using an exponential moving average (EMA) of the student parameters after each iteration. Concurrently, the classification head actively updated with a small momentum prior to the memorization phase for classification at iteration tels-
Acquiring high- gish-quaity annotations for large-scale datasets is difficult. Even with recently available methods for auto outo-labelling and label assist [64 10,, some manual effort is unavoidable. and human labeling errors are often the main source of noisy labels [5 52). The two main types of labeling errors misclassification (categorization noise) and inaccurate bounding boxes (localization noise).
In Icage-scale, crowd-sourced annotations [3] 63 both errors frequently occur together; for instance, labeling errors of the type shown in Figure are quite common. As observed in [31 3] the performance of object detection models that are overfit to such incorrect labels can be significantly degraded.
For the simpler task of image classification, numerous methods address noisy labels in 32 34 23 22 (|)] In comparison, only a few works address noisy labels in object detection, with the focus typically on a single type of noise (categorization or localization) rather than both simultancously. For example, Liu al. (36] propose method to mitigate categorization noise by excluding unreliable samples, while Bär et al. B introduce localization label refinement network to correct box errors. These approaches are limited in real-world settings where both types of noise often appear together.
In this paper, we propose method to train object detectors that takes into account both categorization and localization noise in the annotations. The key insight underpinning our method is the early- learning phenomenon [32 in which models trained on noisy data with mixed clean and false labels have been observed to first fit the clean data, and that the false labels memorized later manifests differently for classification and localization errors. Specifically, we observe that models tend to memorize localization error earlier than categorization noise.
Leveraging this observation, we propose an object detection training algorithm based on knowledge distillation [9 (50, in which models from the early-learing phase (before overfitting occurs to noisy data) act as teacher networks. Specifically, once the larly-leannig phase terminates, we copy the model to create a frozen teacher network, while a student model continues training on the noisy data. To detect the transition from early-learning to memorization, we fit an exponential parametric curve to training metrics and identify the transition point when the curve's 's gradient significantly decreases [31 33]. The teacher network is updated by an exponential moving average (EMA) of the student's s parameters to train informative features from clean samples while maintaining robust knowledge. We further adjust EMA momentum for the classification and localization heads to handle the distinct carly-learning termination points between these tasks effectively. This self-distillation workflow (21) helps the current model focus on learning from clean data while avoiding overfitting to noisy labels. The overview is illustrated in Figure 2
We develop practical implementation of our proposed method in the form of a plug-in module, compatible with various object detection architectures including both anchor-based [46 44 and anchor-free models [49]. Our approach does not require architecture-specffi modifications, making it widely applicable. We evaluate our approach on PASCAL VOC and MS COCO with noise

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simulations. Additionally. we assess the domain robustness of our framework on the VinDr-CXR medical detection dataset [39] demonstrating its adaptability to specialized domains. Extensive experimental results demonstrate that our method significantly improves detection performance in noisy environments, effectively handling simultaneous classification and localization noise in real-world object detection tasks.
Related Works
Object Detection.
Object detection has primarily evolved along two directions: anchor-based [35
65
[
and anchor-free detectors [22 66 6). Anchor-based detectors such as RetinaNet [46] and
Faster R-CNN [44] use pre-defined anchor boxes reference points to predict objects. In contrast, anchor-free detectors like FCOS [49] remove the dependency on anchor boxes by directly predicting
object locations, which results in simpler architectures and reduced computational demands.
These methods can also be categorized into one-stage and two-stage detectors. One-stage detectors (e.g,, RetinaNet [46] and Generalized Focal Loss (GFL) (6) aim to directly predict class probabilities and bounding box coordinates from the entire image in a single shot. Two-stage detectors (e.g.. Faster R-CNN 44] and Cascaded R-CNN [5]) first generate region proposals and then refine them for accurate prediction. Given these significant architectural differences, methods that can effectively integrate with both structures are clearly needed [57] To this aim, our proposed approach is designed as a plug-in module that can be readily incorporated into most existing architectures.
Transition from Early Learning to Memorization Under Noisy Annotations. Liu et al. 132 observe the following early-learning phenomenon in deep learning models: during the initial stages of training, model gradients are dominated by clean labels, while memorization of noisy labels emerges in later stages. Based on this observation, they propose regularization technique that leverages the predictions of earlier models to limit the impact of noisy labels.
Building on this foundation, several works have aimed to develop robust models under noisy annota- tions for image classification [34 25 2 27). Han et al. [[5] introduce Co-teaching, in which two networks are trained simultaneously using the small-loss instances selected by its peer network. Li et al. (23 propose DivideMix, which models the distribution of losses to separate clean and noisy samples, then applies supervise learning techniques utilize both sets effectively.
Extending the concept to segmentation, Liu et al. [33 introduce an Adaptive Early Learning Cor- rection (ADELE) framework for the weakly-supervise, setting. ADELE monitors class-specific transitions from early-learning to memorization, refining noisy labels with pseudo-labels generated from early-phase model predictions for each class. While segmentation and object detection both require localization and classification, ADELE's focus on pixel-wise annotations and class- specific early learning is more suited to segmentation tasks where localization involves fine- fine-grained pixel boundaries. In contrast, object detection involves both the accurate localization of bounding boxes and the classification of entire regions within the box, making it challenging to directly apply ADELE to this domain. Thus, when applying early learning to object detection with noisy labels, what is needed a specialized approach that addresses the unique challenges of noisy labels.
Object Detection with Noisy Annotations. Some recent works that propose to train robust object detectors under noisy labels include 488 24 77. Liu et al. (36) propose adaptive framework which identifies reliable examples in noisy data by measuring instance-level domain properties and adjusting the training accordingly. While their approach effectively mitigates the impact of domain shifts with noise, it focuses primarily on domain adaptation scenarios and may not fully address the challenges posed by noisy labels in general object detection tasks.
ORSOD [I]] introduces dynamic loss decay mechanism to enhance the robustness of oriented object detection with noisy labels by adaptively reducing the influence of high-loss samples. However, ORSOD primarily addresses categorization noise and does not account for localization noise, which can significantly impact detection accuracy in real-world scenarios where both types of noise are present. contrast, Bär et al. (3 propose a Localization Label Refinement Network (LLRN) to refine the noisy coordinates of bounding boxes. LLRN focuses correcting localization errors by training a separate network to predict more accurate bounding boxes, which are then used to update the training data. While effective for localization noise, LLRN overlooks categorization noise,
3

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Class-aghostic training AP
0.80
0.75
0.70
0.6
0.65
0.5
Ground-truil box
0.60
0.55
Localization
0.50
Categorization Roiso
0.2
0.45
Epoch
Figure 3: Evaluation results on training samples under localization (blue) or categorization noise (green) with class-agnostic and ground-truth box allocation metric, respectively. Red stars mark early-learnng termination points for each metric of RetinaNet on PASCAL VOC with noise ratio of 40%.
which presents clear limitations in real-world scenarios. In contrast to prior works, our approach simultaneously tackles both types of noise.
Knowledge Distillation. Knowledge distillation has been widely adopted transfer informative features from a high-performance model (teacher) to an efficient network (student) [9]. Several works have proposed a distillation framework for object detection that improves the performance of small detectors 88 [1 59]. Wang et al. [50] propose an efficient approach named CrossKD, which propagates the intermediate features of the student network the heads of the teacher models. This cross-head approach enables task-oriented knowledge transfer between networks, improving the student's performance without significantly increasing computational costs.
In knowledw distillation [14] 56 61 21 59 28), model serves as both teacher and student, gradually refining knowledge by jointly learning from the ground truth and past predictions of the model itself. Works adopting the co-teaching framework [15 4 29 demonstrate that self-knowledge distillation can address noisy labels by distilling knowledge from the model itself. While these works have shown promising results, they often suffer from high computational costs due to training multiple models simultancously.
Priority in Memorization: Localization Over Classification
The dual-task nature of object cnaection-classifcaaal and localization us to hypothesize that detectors may prioritize the coupled objectives differently when training under noisy labels. To investigate this phenomenon, we conduct empirical experiments to monitor how object detectors internalize noisy labels across the two tasks.
Specifically, we adopt task specifcc metrics to independently monitor detector performance while isolating the impact of noise on each task. First, we evaluate detectors in class-agnostic (CA) manner, treating all objects as a single category. This evaluation prevents potential interference from classification errors. For categorization noise, we replace the predicted bounding box coordinates with the corresponding ground truth if the overlap between the two exceeds a certain threshold. This ground-truth box allocation (GTBA) effectively alleviates the influence of localization errors, allowing for a more accurate assessment of classification performance. Details are discussed in Section 4.1
As illustrated in Figure 3 our findings reveal a critical aspect of model behavior under noisy labels for object detection. We observe that the memorization of localization noise occurs significantly earlier than the memorization of categorization noise. This finding suggests that the model tends to focus on spatial aspects during the early learning phase, while requiring longer training times for classification. Based on this finding, we propose technique that suppresses noise memorization across the two tasks.

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4 Methods
In this section, we discuss our approach for training robust object detectors in the presence of noisy labels. We exploit the early learning phenomenon 32 to address the challenges posed by both localization and categorization noise.
4.1 Early-Learning and Memorization
As discussed in Section 3 the early learning phenomenon manifests differently in object detection for the two key tasks of localization and classification. Specifically, models tend to memorize localization noise earlier than categorization noise. To accurately capture any task-specific patterns, we employ specialized metrics that independently monitor the training progress of each task.
For localization, we utilize class-agnostic (CA) metric that treats all detected objects as belonging to single category (e.g., "object"). This evaluation isolates localization performance without the confounding influence of classification errors, allowing us to monitor how the model learns spatial information over time. For classification, we introduce Ground Truth Box Allocation (GTBA) approach. Inspired by Moon el al. (60), we replace the box coordinates the model prediction with the ground truth box if the Intersection over Union (IoU) between them exceeds certain threshold т. This allocation effectively climinates the impact of localization errors, enabling a more accurate assessment of classification performance under noisy label conditions.
By employing these task task-specific metrics, we can effectively discern the distinct dynamics of early- learning across localization and classification tasks, and determine the appropriate moments to intervene during training.
4.2 Early Learning Phase Detection
To detect the transition from early learning to memorization for each task, we follow Liu et al. B3] in fitting exponential parametric function model the rate of change with respect to the performance on noisy training sets over training:
((1)
eb. d.t")
where É represents the training epoch, and a > 0, 6 2 0. and 2 0 are parameters that are fitted to the observed data. This parametric form captures the performance trend of the model on the training dataset.
To detect the transition point from carly-learning memorization, monitor the relative change in the derivative of the metric. Specifically, the transition is identified when the rate of change deviates significantly from the initial learning gradient, as formalized by the following condition:
(F( 8'(1] >%
(f((1)
where y is threshold capturing the deviation from the initial learning rate. When this condition is met, it indicates that the model dynamics have shifted and it begins memorize noisy annotations. Using this method, we determine the transition epochs fle and lel for localization and classification respectively, based on the metrics in Section
4.3 Early Learning Guidance via Distillation
Knowledge Distillation. To mitigate the effects of noisy labels and prevent memorization, we propose an early learning guidance mechanism. This mechanism involves using the model obtained the end of the early learning phase as teacher network in a knowledge distillation framework [19 50 21). The teacher network, which has not yet overfitted to noisy labels, guides the current student model during subsequent training, helping it avoid overfitting to noise. The student model continues training on the noisy dataset but receives guidance from the teacher model to focus on learning from clean data. The distillation process implemented by minimizing the divergence between the predictions of the teacher model and the student model. Specifically, we define the knowledge distillation loss:
Lxd
Cals
ckd rd
Looe
(3)
5

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where Lag and Cigg are the distillation losses for classification and localization, respectively. The total loss function for training the student model then defined as
Lirat = Laet + ALea:
(4)
where Laet is the original detection loss between the student predictions and the noisy ground-truth annotations, and À is a hyperparameter that balances the detection loss and the distillation loss.
To enhance the efficiency of the distillation process, we adopt the CrossKD framework [50]. CrossKD allows for knowledge transfer between the teacher and student models by propagating the intermediate features of the student network to the teacher's detection heads. This cross-head approach enables task-oriented knowledge transfer without significant computational overhead.
Teacher Model Update via EMA. To ensure that the teacher model remains reliable source of guidance throughout training. co-teaching frameworks [1] that propagate gradients to both teacher and student networks can be used. However, this dual-training approach incurs high com- putational costs, as it requires training two models simultaneously. Instead, we update the teacher model using an exponential moving average (EMA) of the student model's weights without updating it directly. The EMA update rule is defined as follows:
(5) where Quaccer and student represent the weights of the teacher and student models, respectively, and is the decay rate. Typically is set close to to ensure that the teacher model updates slowly, preserving its robustness to noisy labels. However, since the teacher model is initialized after the early-learning phase ends for localization but before it concludes for classification, using high decay rate on the classification head can potentially hinder its performance. To preclude this possibility, 0.1 specifically to the classification head during this period.
Quacher O'buacher
(1-
we apply small decay rate of This enables faster updates for the classification head, which allows classification performance to sufficiently converge before memorization occurs without compromising noise robustness.
Our framework combines EMA updates with knowledge distillation to enable effective teacher guidance and robust training, mitigating noisy label effects in object detection.
5
Experiments
5.1
Experimental Settings
Noise Simulations. To evaluate the robustness of object detectors under noisy labels, we sim- ulate both localization and categorization noise. For localization noise, perturb the ground
truth bounding box coordinate while keeping its category 3 Given a ground truth box b =
(Jiinn ymin Taxx ymax), ), the noisy box booisy is calculated as:
b moisy = [min + 82 Ymin + 8y Tmax + 8% Ymax + +8,]
(6)
where 8g, by, 82 8 are sampled from uniform distributions scaled by box size with magnitude
controlled by = 0.5, corresponding to up 50% perturbation relative to width or height.
For categorization noise, we randomly replace the true class label o of each object with an incorrect label ce drawn uniformly from all other possible classes C \ {ci]:
df (CC \ {ct)). We apply these noise simulations to a random subset of the training data with various levels of 20%. 30%, and 40%.
(7)
Datasets. We conduct our experiments on PASCAL VOC 11 and MS COCO, which is a widely used benchmark for object detection. Following the standard protocol 49)
the VOC 2007
and VOC 2012 trainval sets (16,551 images) for training, and perform evaluation on the VOC 2007 test set (4.952 images). MS COCO is large-scale dataset with 80 object categories, featuring over 330K images and more than 2.5 million labeled instances. We use the COCO 2017 version, training on the train split (118K images) and evaluating on the val split (5K images).
To further validate the robustness of our approach, we run experiments on VinDr-CK: CXR medical object detection dataset [4)]. VinDr-CXR dataset consists of 15,000 chest X-ray images annotated with 14 classes, which represent various thoracic disease findings and abnormalities. For all datasets, we apply the aforementioned noise simulations only to the training data while keeping the validation and test sets with clean annotations.

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Table I: Comparison of performance under noise annotations on Pascal VOC reported as 50 The results shown for localization, categorization and combined noise with noise levels at 20%, 30%, and 40% as well as the clean setting. For each detector, the best AP scores are highlighted in bold, with the second-best scores underlined.
Localization Noise
Categorization Noise
Combined Noise
Detector
Method
Clean
20%
30%
40%
20%
30%
40%
20%
30%
40%
75.07
74.00
73.83
73.13
70.67
69.03
67.43
70.27
68.07
65.63
ORSOD
31
75.41
74.17
73.97
73.43
71.13
68.90
67.33
70.37
66.57
65.47
RetinaNet
ADELE
33
74.69
73.67
74.10
71.03
71.03
69.23
67.07
70.13
68,20
65.87
ELDET (ours)
76.52
76.23
76.30
74.80
73.66
73.71
68.21
74.53
73.67
68.82
72.23
71.00
70.67
70.60
67.37
64.97
62.63
66.57
63.33
60.13
ORSOD
31
71.63
68,999
70.99
70.94
67.55
64.47
62,80
67.36
63.06
60.51
FCOS
ADELE
53
71.59
71:20
70.70
70.57
67.53
65.40
62.67
66.73
63.87
60.70
ELDET fours)
72.00
73.40
72.80
74.10
68.43
66.13
63.73
68.67
65,03
62,43
73.89
69.48
69.49
69.25
66.95
65.15
63.65
66.68
64,84
62.02
ORSOD 31
70.77
68.91
688 94
68.88
66.40
64.84
63,40
65.85
64,24
63.01
Faster R-CNN
ADELE
33
71.48
69.15
68.54
68.91
67.76
65.65
63.59
68.83
65,28
62.61
ELDET fours)
73.34
71.92
71.81
70.12
69.40
67.60
66.33
69.09
66.83
64.28
73.00
73.23
71.68
71.50
69,55 544
66. 18
62.77
67.70
64.69
56.30
ORSOD 31
74.11
73.87
73.12
72.51
69.92
67.71
63.64
68.49
64.73
61.28
GFL
ADELE
33
73.05
73.82
72.29
69.14
47.49
64.19
68.41
66.04
62.71
ELDET (ours)
75.28
75.23
74.57
74.44
69.68
67.57
65.35
69.82
66.77
63.57
Table 2: Comparison of performance under noise annotations on MS COCO val2017, reported as AP @ 50. The results are shown for localization, categorization and combined noise with noise levels at 20%, 30%, and 40% as well as the clean setting. The best AP scores are highlighted in bold.
Loculization Noise
Categorization Naise
Combined Noise
Detector
Method
Clean
40%
20%
30%
40%
20%
30%
40%
44.41
43.97
42.61
43.12
41.86
40.66
39.90
42.61
42.33
41.95
RetinaNet
ELDET (ours)
45.95
44.96
44.87
44.74
43.51
41.98
40.31
44.51
43,23
42.99
44.02
44.34
44.36
"43.11
41.91
41.73
39.26
43.16
42,66
"2.13""
FCOS
ELDET (ours)
45.89
45.17
44.94
44.83
43.36
42.66
41.02
44.39
43,70
43.59
43.35
42. 80
42.86
42.48
40.36
39.26
37.21
40.91
40.83
"3.37"
Faster R-CNN
ELDET (ours)
44.79
44.00
43.51
43.49
41.42
39.47
38,20
42.72
42.06
40.30
47.24
47.19
46.08
45..77
45.32
44.33
43.31
46.30
43.38
"4.81"
GFL
ELDET (ours)
49.56
48.77
47.43
46.64
47.05
45.59
44.78
47.47
46.93
45.58
Baselines.
We compare ELDET with two baselines:
ORSOD addresses categorization noise by dynamically excluding samples with high classification loss during training, which prevents the model to memorize noisy samples with large loss.
ORSOD 31:
ADELE 333: We extend the role of ADELE to object detection under noisy annotations. Specifi- cally, detectors begin to be supervised by the pseudo-label from the early-models instead raw noisy labels.
5.2 Experimental Results
We use the mean Average Precision at an Intersection over Union (IoU) threshold of 0.5 (AP@ 50) for PASCAL VOC and COCO, and 0.4 (AP@40) for VinDr-CXR. To validate the compatibility of our method with various architectures, we conducted experiments with different detectors including RetinaNet [46), FCOS [49], Faster R-CNN 44 and GFL (26).
PASCAL VOC. Table presents a quantitative comparison of ELDET against baseline methods on PASCAL VOC. ELDET consistently outperforms the comparison models across all scenarios. Notably, ELDET using the RetinaNet significantly surpasses all existing baselines with a large gap under the combined noise condition with 40% noise level. This substantial improvement
7

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choir
car
dog
Figure 4: Qualitative results of ELDET on the training set of PASCAL VOC, where green boxes represent the predictions of our method and red boxes indicate the noisy annotations. Our model effectively avoids memorizing both localization and categorization noise.
Table 3: Comparison of detection performance under various noise levels VinDr-CXR. Best AP scores are highlighted bold.
Table 4: Ablation study of task-specific metrics PASCAL VOC with noise ratio of 40% using RetinaNet.
Combined Noise
Ground-truth
Detector
ELDET
Class-agnostic
Box Allocation
AP
20%
30%
40%
(I)
66.13
RetinaNet
29.54
27.44
26.75
(2)
67.93
31.09
29.61
31.06
(3)
65.70
28.51
27.00
25.32
(4)
68.82
FCOS
34.40
32.55
32.74
30.97"
29. 16
27.87
R-CNN
32.91
32.52
31.61
"29006"
26.96
26.65
GFL.
36.29
33.90
27.60
underscores superior capability ELDET to simultaneously handle both types of noise. Qualitative results illustrated in Figure
In contrast, ORSOD and ADELE exhibit limited effectiveness in managing both types of noise. According to the original experimental results of ORSOD, it demonstrate negligible performance gain when integrated with certain architectures (e.g., ReDet (16), which indicates the low robustness of dynamic loss decay across different detectors. On the other hand, ADELE, which is initially designed for weakly-supervised settings where the ground truth mask quality is notably low, corrects the ground truth with early-learning phase predictions when memorization begins. However, this substitution can occur performance drop in our setting because direct supervision from early-phase predictions may prevent the model from effectively learning informative features.
Table 2 shows that incorporating ELDET consistently improves AP on the MS COCO dataset compared to the baseline without it. These gains on large-scale benchmark underscore the robustness of our method and demonstrate its ability to generalize to complex, real-world data.
COCO.
VinDr-CXR. As shown in Table 3 ELDET maintains its superior performance over the baselines on VinDr-CXR dataset. is notable that ELDET boosts the performance of FCOS from 25.32 to 32.74 under a 40% combined noise condition. It shows the robustness and adaptability of our proposed method in challenging settings with complex noise patterns in medical imaging. By effectively handling noisy annotations in multi-domain, ELDET showcases its potential for widespread scenarios across various fields where annotation noise is prevalent to happen.
Ablation Study on Task-Specific Early Learning. We conduct an ablation study on PASCAL VOC with a 40% noise ratio using RetinaNet to evaluate the contribution of each metric discussed in Section 4.1 summarizs the results under different configurations: (1) without any task- specific metric, (2) using only the class-agnostic (CA) metric for localization, (3) utilizing only the ground-truth truth box allocation (GTBA) metric for classification, and (4) leveraging both CA and GTBA

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0.9
Table 5: Accuracy (%) of noisy label filtering by comparing predictions on noisy samples with the clean labels, evaluated on PASCAL VOC training set using RetinaNet with 20% noise.
0.8
0.7
0.6
Training
0.5
Noise Type
0.4
(BL.ET)
ELDET
(ELDET)
Localization
Categorization
0.3
clesn
0.2
Doisy
73.74
22.66
12
80.98
74.71
Epoch
Figure 5: Performance of RetinaNet (green) com- pared to the setting with ELDET (blue) on the PASCAL VOC training samples. Both models are trained on datasets with 20% combined noisy the original clean anno-
Table Ablation study of exponential moving average (EMA) of student parameters to update the teacher network. The results are evaluated on PASCAL VOC using RetinaNet with 40% noise.
labels yet evaluated tations. Solid lines represent evaluation on the samples with noisy labels during training, while dashed lines represent evaluation on clean data.
Noise Type
EMA
Localization
Categorization
Combination
73.73
66.90
66.03
74.53
73.67
68.82
metrics. Our final setting with both techniques achieves the best performance, which indicates that task-specific monitoring effectively contributes to robust learning under noisy conditions. The CA manner mitigates the impact of categorization noise, while the GTBA evaluation safeguards detectors against localization noise. The proposed combination of metrics enable ELDET to successfully detect the transition from the early-leariing phase to memorization for each task respectively.
Robustness to Noisy Annotations. Figure illustrates the performance comparison between RetinaNet and our setting with ELDET when trained on noisy PASCAL VOC but evaluated on the clean labels after each epoch. For example, the solid blue line denotes the evaluation-wiith-ceenn performance of ELDET on assesses the ability resist overfitting to noisy annotations and generalize to clean labels. RetinaNet without ELDET shows significant drop on evaluation with-clean for training vrainnngwwith-noin with- samples (solid green line), which implies the occurrence of memorization to the noisy labels. In contrast, ELDET with high accuracy throughout training demonstrates the robustness in avoiding memorization of noise and its effectiveness in generalizing to clean label even unseen while training. A slight performance drop at the beginning of distillation is observed, which reflects the typical transient instability in early distillation stages caused by conflicting supervision and the delayed stabilization of the EMA teacher.
samples with 20% noise ratio. This experiment
Noisy Annotation Filtering for Data Curation. In addition, we probe the effectiveness ELDET in validating the quality of annotations on PASCAL VOC using RetinaNet with a noise ratio of 20%. To measure how successfully models identify noisy labels, we compare the model predictions with the original clean ground truth on noisy samples.
In the case of categorization noise, a prediction is considered to be correct if the class prediction is same with the original category. For localization noise, prediction is valid if IoU compared to the clean box surpasses a threshold of 0.65. The results in Table reeeeal that detectors with ELDET not only produce accurate predictions on noisy data but also demonstrate strong capacity to distinguish between clean and noisy annotations. This capability highlights the potential utility of ELDET for identifying and filtering out noisy labels, which suggests practical applications in data curation or data cleaning.
Effect of EMA on Knowledge Distillation. We evaluate the impact of the exponential moving average (EMA) on our framework on PASCAL VOC using RetinaNet with a noise ratio of 40%. The teacher network without EMA is not updated after early-learning phase with frozen parameters.
Tsshos that incorporating EMA significantly improves performance across all noise settings. The improvement in the case of categorization noise shows that the teacher network suffers from limited classification performance in the early-learning phase but that EMA effectively overcomes this limitation. These results underscore the effectiveness of EMA in enhancing the teacher guidance.

---

Table Performance of ELDET on Deformable DETR under combined noise conditions on PAS- CAL VOC (AP@50)
Table 8: Performance comparison of DINO with query denoising vs, ELDET on PASCAL VOC (40% noise ratio, AP).
Method
Clean
20% Noise
30% Noise
40% Noise
Method
Clean
Loc.
Cat.
Combined
Baseline
74.27
68.60
65.39
62.52
Query Denoising
75.37
74.26
67.16
66.26
ELDET
74.52
68.82
65.84
62.91
ELDET
75.63
68.29
Table 9: Hyperparameter sensitivity analysis for our proposed method under different noise conditions. Results are evaluated on the PASCAL VOC dataset using RetinaNet with 40% noise. The table reports performance across various values of 7 and The best AP scores are highlighted in bold, with the second-best scores underlined.
Noise Type
Localization
Categorization
Combination
0.1
0.9
74,53
73.67
68.82
0.3
0.9
76.67
70.55
73.46
0.5
75.41
67.71
66.68
0.1
0.7
76.11
73.07
73.39
0.1
0.8
75.69
73.39
71.81
further demonstrate the generality of ELDET as plug-and-plas module across different architectures, we evaluate it on transformer-based detectors. The results verify ELDET's effectiveness in transformer-specifie denoising frameworks and its robustness under challenging noisy conditions. For Deformable DETR [67] PASCAL VOC with combined noise (as shown in Table ELDET consistently improves performance over the baseline across all noise levels, highlighting its capability to strengthen DETR-based models. Furthermore, to benchmark against native transformer denoising, we replace DINO's query denoising 62 with ELDET and evaluate PASCAL VOC at 40% noise across all scenarios (Table ELDET surpasses the native method under every noisy condition, with a notable improvement under combined noise (68.29 66.26 AP). These results validate ELDET's strong adaptability label noise and confirm its seamless integration into wransformer-basee detectors such as DINO, extending its applicability beyond convolutional architectures.
Transformer-based Detectors.
Impact of Hyperparameters We conduct ablation studies on two key hyperparameters in our ELDET framework: the IoU threshold used in the Ground Truth Box Allocation (GTBA) process, and the deviation threshold used for early-learning phase detection. Table Table presents the Average Precision (AP) scores under different settings of and y across localization, categorization, and combined noise types. While certain configurations like t = 0.3 and 0.9 achieve the highest AP under localization and combined noise, the first line with r 0.1 and y 0.9 provides strong and balanced performance across all noise conditions.
6
Conclusion
This paper introduced ELDET, self-knowledge distillation framework that leverages the early- learning phenomenon to address both localization and categorization noise in object detection. By using stale models as teacher networks, ELDET effectively mitigates the memorization of noisy labels, resulting in improved robustness and performance across diverse datasets such PASCAL VOC, MS COCO and VinDr-CXR. The proposed framework is compatible with various detection architectures, making it practical for real-world upplications. Future research could explore extending ELDET to video object detection or integrating it into active learning pipelines for automated data curation.
Acknowledgments This work was supported by funding from SAIGE. FC Park was further supported in part by IIP-MSIT grants RS-2021-11212066 2022-220480, RS-2022-11220480, SNU- AISS, SNU-IPAI, SNU-IAMD, and the SNU Institute for Engineering Research.
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Appendix
A Limitations
While our framework demonstrates robust empirical gains under controlled noisy settings, several limitations remain. First, the foundational assumption of our method that localization and categoriza- tion exhibit temporally distinct early learning behaviors is based on empirical observations. Although supported by quantitative trends, a theoretical explanation of this phenomenon is beyond the scope of this work and remains open question. Second, the categorization noise is synthetically generated by uniformly sampling incorrect labels from the set of classes excluding the ground truth. While this is common strategy in prior work, it does not reflect the structured or semantically biased errors that typically arise in real-world annotation processes. Third, the detection of early-learning phase transitions is performed using curve fitting and gradient slope change, following the heuristic methodology proposed in prior work [33]. However, this procedure is sensitive to the choice of hyperparameter such the slope threshold 7, which can alter the estimated transition point and downstream performance. These limitations underscore the need for more realistic noise modeling, theoretically grounded dynamics analysis, and robust, data-adaptive mechanisms for to identify leamnigg phase transitions.
Table 10: Training resource comparison (training time and GPU memory usage) across various detectors and methods.
Dataset
Detector
Method
Training time (hours)
Memory Usage (GB)
2.51
11,418
ORSOD 31
3.96
12,135
RetinaNet
ADELE 33
4.11
11,496
PASCAL VOC (20 classes)
ELDET
4.23
17,967
2.13
10,507
ORSOD 31
3.67
10,666
FCOS
ADELE 33
4.05
10,631
ELDET
3.59
14,892
MS COCO
8.03
11.418
(80 classes)
RetinaNet
ELDET
15.43
17.967
B
Compute Resources
All experiments were conducted using GPUs with 24GB VRAM (NVIDIA RTX 3090 and 4090). Our framework maintains a teacher model that is initialized at the end the localization early learning phase and subsequently updated via exponential moving average (EMA) of the student model's parameters. Unlike co-teaching methods that require simultaneous gradient updates to two networks, approach avoids full dual-mode training. Instead, it only requires forward passes through the frozen teacher resulting in moderate memory and compute overhead roughly equivalent to running a single training model alongside lightweight inference model.
To quantitatively assess computational efficiency, we measured both total training time and GPU memory usage across different configurations. One computational consideration in our setup is the monitoring carly-lcarning dynamics. To detect phase transitions in learning, we compute validation metrics the entire training set at every epoch. This process, while crucial to identify transition points accurately, incurs additional time cost especially for large-scale datasets such as MS COCO [30. This behavior is primarily due to the need for per-epoch validation over the entire training set to detect the early-learning transition point-a a step required by all these methods rather than being specific to ELDET.
As shown in Table 10 both ADELE and ORSOD exhibit training time increases comparable to ELDET, primarily due to this per-epoch validation process shared across methods. Although ELDET shows a slightly larger memory footprint, this mainly due to the additional teacher network used for knowledge distillation. Since the teacher model remains frozen and participates only in forward

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passes without backpropagation. the actual computational overhead remains modest. Overall, the additional cost of ELDET is comparable that of other robust-learning frameworks and is acceptable given the consistent performance improvements observed across datasets.
C Detailed Experimental Settings
C.1 Implementation Details
Our proposed method is implemented using the MMDetection framework ( built on PyTorch 42
All input images are resized to 512 x 512 for consistency. Training is conducted using Stochastic Gradient Descent (SGD) with momentum of 0.9 and weight decay of 10-4. The learning rate follows step schedule, decreasing by factor of 10 at predefined epochs, except for MS COCO [0) where only a linear scheduler is used. For PASCAL VOC [I] and MS COCO, the learning rate is set to 0.01, and the training spans 12 epochs with batch size of 32. In contrast, for VinDr-CXR [40], the learning rate is set to 0.005, and the training spans 20 epochs with batch size of 16. We exclude the "No finding" class in VinDr-CXR data for fair comparison of noisy training scenario. All detectors are initialized with a ImageNet [47] pre-trained ResNet-50 and trained on NVIDIA GPUs.
C.2 ELDET Hyperparameter Details
For the ground-trutt truth box allocation (GTBA), set the IoU threshold 0.1, replacing predicted box coordinates with ground-truth locations when the IoU exceeds т. We consider the model to begin memorizing noisy labels when the relative change in the derivative of the performance metric exceeds the criterion with 7 = 0.9. The exponential moving average (EMA) momentum a is set to 0.999 for the overall model and adjusted to ack 0.1 for the classification head during the period after localization memorization and before memorizing categorization noise. Other hyperparameters are same as the original setting (e.g., the loss weight of MMDetection
C.3 Baselines
ORSOD 31 tackles categorization noise by adopting a dynamic decay mechanism to progressively down-we wwwn-weight the top-k samples with the highest classification loss. The dynamic loss decay function is defined as
{ (X)( a Lats + (Xr), if 4 2 lel
if tel.
COLD
(8)
where Lets is the classification loss, X₁ and X,, represent the top-k and remaining samples, respec- tively, & denotes the current training epoch, and f is the early-learning termination epoch. The decay factor is defined as:
exp
where e is a constant controlling the rate of decay (set to in our experiments). This adaptive mechanism ensures that high-loss samples have reduced impact in later training epochs, which promotes more stable and noisc-resilient learning. However, a limitation of ORSOD is that it only suppresses the classification loss without explicitly addressing localization noise in the annotations, which may limit its effectiveness in handling noisy box-level annotations.
ADELE 33 was originally developed for semantic segmentation tasks with noisy annotations,
leveraging the observation that early-learning concludes at different times for each class. By updating the labels of pixels where the model's prediction score exceeds a certain threshold (e.g.. 0.8) at the class-specific early-learning endpoints, ADELE effectively refines noisy annotations, enabling robust segmentation performance even in the presence of noise. To adapt ADELE for object detection, we modified the approach to account for the inherent differences between segmentation and detection tasks. Instead of utilizing class ceess-specific carly-lcarning endpoints, we defined unified carly-learning endpoint across all classes. At this point, model predictions are used to refine annotations by replacing
https: http://ww...ooo/ooo.mmmm/m/////a

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Table 11: Evaluation on the compatibility with various knowldege distillation techniques. The results are evaluated on PASCAL VOC using RetinaNet with 40% noise. The best AP scores are highlighted bold, with the second-best scores underlined.
Noise Type
KD
Localization
Categorization
Combination
70.27
68.07
65.63
CrossKD 50
74.53
73.67
68.82
FGD
59
73.11
67.85
66.61
OFD 18
73.36
67.50
66.03
noisy labels with more reliable predictions that meet strict criteria: (1) prediction score of at least 0.5, and (2) an Intersection-over- Union (IoU) exceeding 0.5 with the corresponding ground-truth bounding box. For such cases, both the coordinates and the class label of the original ground truth are updated to match the model's prediction.
C.4 Knowledge Distillation Loss Functions
We adopt the knowledge distillation loss functions used in CrossKD [50] to guide the student models in mimicking the un-memorized knowledge of teacher models. For RetinaNet [46], we use the Quality Focal Loss 26 for classification and the Generalized IoU Loss
45 for localization. In the case of FCOS [49] the classification loss is implemented with Focal Loss 46 while the localization loss employs LoU Loss. For Faster R-CNN 44 the classification loss is based on KL Divergence, and the localization loss uses L1 Loss. Lastly, for GFL (26), the classification loss is also Quality Focal Loss, but the localization loss relies on KD Divergence Loss. These loss functions ensure effective knowledge transfer by aligning the outputs of the student models with those of early-phase teacher models.
D Additional Experimental Results
D.1 Compatible with Different Distillation Techniques
To demonstrate the flexibility of our ELDET framework, we investigate its compatibility with various knowledge distillation techniques beyond CrossKD [50]. Specifically, we integrate FGD [59 and OFD [88 into our framework and evaluate their performance under different types of noise.
As shown Table II integrating FGD and OFD into our framework yields improvements over the baseline without distillation under localization and the combined noise. These improvements indicate that our ELDET framework is compatible with different KD techniques and can benefit from them. However, CrossKD consistently outperforms the other distillation methods across all noise types. These results suggest that while our framework can effectively incorporate various KD methods, CrossKD provides the most substantial improvements in our experiments. This superiority may be attributed to CrossKD's ' ability to facilitate task-oriented knowledge transfer without only focusing transferring fine- grained feature embeddings from the teacher. Anagnostidis al. [I] found that neural networks are tolerant to label noise except in the last layer, which indicates the vulnerability of the later layers of detectors to noisy annotations. In other words, direct distillation from the classification head of the teacher to that of the student using CrossKD can mitigate the memorization of noisy labels unlike other approaches.
D.2 EMA Decay Rates
We analyze the impact of the momentum a. Oless and the exponential moving average (EMA) update cycle of the student parameters for the update the teacher network. Table 12 shows that a small momentum of 0.9 reduces performance, suggesting that a strong momentum is crucial to maintaining the stability of the teacher network. Similarly, setting Oeks = 0.999 or das = 1.0 (i.e., updating the classification head slowly before early-learning terminates for classification task) results

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Table 12: Evaluation of the EMA (Exponential Moving Average) strategy with varying parameters a, Ocks and interval settings. Results are reported as @ on the PASCAL VOC dataset using RetinaNet with 40% combined noise. The table highlights the performance impact of different EMA configurations. The best AP scores are highlighted in bold, with the second-best scores underlined.
Ccls
Interval
AP@50
0.999
0.1
68.82
0.999
0.1
68.60
0.999
0.1
68.52
0,9
0.1
65.54
0.999
0.999
66.88
1.0
1.0
66.03
Table 13: Detection performance comparison under various noise levels on Oxford Pets using RetinaNet. Best AP scores are highlighted in bold.
ELDET
Noise Level
Localization Noisc
Categorization Noise
Combined Noisc
30%
89.30
79.40
30%
89.90
83.60
88,50
50%
81.00
76.80
66.50
50%
85.90
78.60
79.50
70%
79.10
71.40
64,00
70%
83.10
80.10
in lower AP. This confirms that using a smaller decay rate Oels = 0.1 for the classification head is important to allow it adapt more quickly, preventing the teacher from lagging behind the student's learning on classification tasks.
D.3 Qualitative Examples on VinDr-CXR
Figure presents pressnts a qualitative comparison of the detection results of the baseline FCOS 49 and our proposed ELDET method on the VinDr-CXR 40) training set. This comparison underscores the inherent challenges associated with localization and categorization anomalies in medical images. Despite the presence of noisy labels, the detector utilizing ELDET demonstrates significantly better alignment with the ground-truth annotations compared to the baseline FCOS. It highlights the effec- tiveness of our method mitigating the adverse effects of both localization and categorization noise. Furthermore, this result emphasizes the robustness of ELDET in diverse domains, demonstrating its applicability not only to real-world images but also to the challenging domain of medical imaging.
D.4 Results on Smaller Datasets
Although we have already evaluated our method on the relatively small dataset, VinDr-CXR [40]. which contains more than ten thousand samples, we further investigated whether the proposed approach remains effective on smaller datasets. We conducted additional experiments on the Oxford Pets 41. which consists of classes with approximately 200 images for each class. As shown in table applying ELDET led to consistently higher performance compared to the baseline without ELDET.
D.5 Distinctive Early Learning Termination.
We further investigate when the model begins to memorize noisy annotations for localization and classification tasks respectively. As reported in Table models tends to memorize localization noise significantly earlier compared to categorization noise on PASCAL VOC and COCO with various detectors. This observation outlines the necessity of our task-specific guidance mechanism which indicates the appropriate moment initiate teacher-student distillation for each task.

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Table 14; Termination epochs of the localization (foc) and classification (fels) carly-learning phases, and their difference.
Dataset
Detector
Noise Level
fiee
Difference
20%
+4
RetinaNet
30%
40%
+7
20%
3
9
+6
FOOS
30%
4
10
+6
40%
3
11
+8
PASCAL VOC
20%
7
8
+1
(20 classes)
Faster R-CNN
30%
4
40%
20%
3
+3
GFL
30%
3
+6
40%
6
12
+6
Average
3..2
8.67
14.75
20%
+0
RetinaNer
30%
+2
40%
5
20%
4"
8
FCOS
30%
4
+4
40%
MS COCO
20%
(80 classes)
Faster R-CNN
30%
5
+6
40%
6
12
+6
20%
7
12"
+5
GFL
30%
8
8
+0
40%
12
+8
Average
5.42
9.42

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Ground truth
Baseline
ELDET (ours)
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Aorts ergement
Aun
Pardiomegaly
Card pardoomeggly
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Cord Sordiomage
Long Ounery
Lung Spech
Lung Spccn Cordioeeelyy
Aocter infermmmnnt
Pleane Wiskering
Pleure Biakening
Pee dl chiooeriing
fortc eniargement
Pulmonan floroiis Cardlanngaly
Pulmonaa dlorodis Aortic en margemeen
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Pneum othorothorox Corolamegell
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Figure 6: Qualitative Analysis of FCOS the VinDr-CXR training set. Blue boxes denote model predictions, while red boxes with bold outline represent ground truth annotations. The left panel illustrates the original clean annotations, the middle panel displays predictions from the baseline FCOS, and the right panel shows model outputs with our proposed ELDET. Dotted red boxes in the middle and right panels highlight noisy labels encountered during training. Our proposed ELDET method demonstrates superior capability in mitigating the effects of both localization and categorization noise.


Reviews:
[{"review_id": "PclXFES03L", "reviewer": "Reviewer_RwS3", "summary": "This paper proposes a distillation-based approach to handle label noise in object detection.  It is based on the early learning phenomenon in deep learning models, that is, during the initial stages of training, model gradients are dominated by clean labels, while memorization of noisy labels emerges in later stages.  To utilize knowledge in the “early clean model”, it is used as a teacher model for training the object detection model. Distillation loss is used together with conventional loss from object detection tasks.\nIt is also observed that models tend to memorize localization noise earlier than categorization noise.  Based on this observation, the paper proposes a two-stage distillation. \n\nTo demonstrate the effectiveness of the proposed approach in handling noise in object detection data, simulated label noise is added to object detection data such as PASCAL VOC and MS COCO.  Experiments are performed using the data with added label noise.", "questions": "1) Can the method work in the case of one-stage object detectors?\n2) Any principled analysis on the observation of “models tend to memorize localization noise earlier than categorization noise.”?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "The proposed approach seems to work well on the object detection data with added label noise.\n\nMy concerns on the paper,\n1) The paper is based on the observation that “models tend to memorize localization noise earlier than categorization noise.”  This observation is empirical.  It may change with different levels of noise or different classes of objects to detect. It is questionable whether the observation can always hold.\n2) The paper introduces a “two-stage” distillation scheme based on the aforementioned observation. As shown in Figure 2, it treats the classification head and localization head differently.  However, in some object detection models (e.g., some one-stage object detectors), there may not be separate classification head and localization head.  It is questionable how the “two-stage” distillation would work for models without two separate classification and detection heads.", "was_revised": true, "final_justification": "This paper proposes a distillation-based approach to handle label noise in object detection. The proposed approach seems to work well on the object detection data with added label noise. The rebuttal response also addressed my concerns. Although there is still room for improvement for the generalization capabilities and overall originality, considering all the discussions and the authors’ rebuttal responses/experiments, I raised my rating.", "rebuttal": "We'd like to thank all of our reviewers for taking the time to give us constructive reviews. Your reviews will help us improve our methods and manuscripts.\n\n> \"The paper is based on the observation that 'models tend to memorize localization noise earlier than categorization noise.' This observation is empirical. It may change with different levels of noise or different classes of objects to detect. It is questionable whether the observation can always hold.\"\n\nWe acknowledge this important concern regarding the generalizability of our empirical observation. To address this, we conduct extensive validation experiments across diverse settings and present comprehensive evidence demonstrating the consistency of this phenomenon. We will provide the following additional experimental validation:\n\n**Table: Early Learning Termination Epochs for Localization vs. Classification Tasks Across Diverse Settings**\n\n| Dataset | Detector | Object Classes | Noise Level | Localization Termination | Classification Termination | Difference |\n|---------|----------|----------------|-------------|-------------------------|---------------------------|------------|\n| PASCAL VOC | RetinaNet | 20 classes | 20% | 3 | 7 | +4 |\n| PASCAL VOC | RetinaNet | 20 classes | 30% | 4 | 4 | +0 |\n| PASCAL VOC | RetinaNet | 20 classes | 40% | 4 | 11 | +7 |\n| PASCAL VOC | FCOS | 20 classes | 20% | 3 | 9 | +6 |\n| PASCAL VOC | FCOS | 20 classes | 30% | 4 | 10 | +6 |\n| PASCAL VOC | FCOS | 20 classes | 40% | 3 | 11 | +8 |\n| PASCAL VOC | Faster R-CNN | 20 classes | 20% | 7 | 8 | +1 |\n| PASCAL VOC | Faster R-CNN | 20 classes | 30% | 4 | 11 | +7 |\n| PASCAL VOC | Faster R-CNN | 20 classes | 40% | 3 | 4 | +1 |\n| PASCAL VOC | GFL | 20 classes | 20% | 3 | 8 | +5 |\n| PASCAL VOC | GFL | 20 classes | 30% | 3 | 9 | +6 |\n| PASCAL VOC | GFL | 20 classes | 40% | 6 | 12 | +6 |\n| Average (PASCAL VOC) | - | - | - | 3.92 | 8.67 | +4.75 |\n|-|-|-|-|-|-|-|\n| MS COCO | RetinaNet | 80 classes | 20% | 8 | 8 | +0 |\n| MS COCO | RetinaNet | 80 classes | 30% | 4 | 6 | +2 |\n| MS COCO | RetinaNet | 80 classes | 40% | 5 | 9 | +4 |\n| MS COCO | FCOS | 80 classes | 20% | 4 | 8 | +4 |\n| MS COCO | FCOS | 80 classes | 30% | 4 | 8 | +4 |\n| MS COCO | FCOS | 80 classes | 40% | 4 | 8 | +4 |\n| MS COCO | Faster R-CNN | 80 classes | 20% | 6 | 11 | +5 |\n| MS COCO | Faster R-CNN | 80 classes | 30% | 5 | 11 | +6 |\n| MS COCO | Faster R-CNN | 80 classes | 40% | 6 | 12 | +6 |\n| MS COCO | GFL | 80 classes | 20% | 7 | 12 | +5 |\n| MS COCO | GFL | 80 classes | 30% | 8 | 8 | +0 |\n| MS COCO | GFL | 80 classes | 40% | 4 | 12 | +8 |\n| Average (MS COCO) | - | - | - | 5.42 | 9.42 | +4 |\n\nThese comprehensive experiments across different datasets, detectors, and noise levels will demonstrate that the differential memorization timing between localization and classification tasks is a consistent phenomenon across diverse experimental conditions, validating the robustness of our core observation.\n\n---\n\n> \"It is questionable how the 'two-stage' distillation would work for models without two separate classification and detection heads.\"\n\nWe appreciate this important architectural consideration. To clarify, we evaluated our method on both one-stage detectors such as FCOS and GFL, and two-stage detectors including RetinaNet and Faster R-CNN, covering a broad range of representative architectures.\n\nWe would like to emphasize that most modern state-of-the-art object detection models indeed employ separate classification and regression heads, which naturally accommodate our two-stage distillation approach[1,2,3].\nSuch separation allows each head to specialize and optimize for its respective task—classification or localization—leading to more accurate and stable learning. Prior works[4,5,6] have empirically demonstrated that distinguishing these heads improves both convergence and final detection performance, supporting the architectural rationale behind our distillation method. Therefore, applying distillation strategies that leverage this separation is consistent with established design principles in object detection.\n\nIn the rare cases where a single head is shared by both classification and regression tasks, our framework can be adapted with minor modifications. A possible approach might be to apply task-specific masking to the gradients of the final output head during backpropagation. This would allow selective updating of weights relevant to each task (classification or regression) while masking out the others, enabling different training procedures and EMA updates. \n\n---\n\n[1] Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., & Han, J. (2024). Yolov10: Real-time end-to-end object detection. Advances in Neural Information Processing Systems, 37, 107984-108011.\n\n[2] Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., ... & Chen, J. (2024). DETRs beat YOLOs on real-time object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16965-16974).\n\n[3] Nan, Z., Li, X., Dai, J., & Xiang, T. (2025). MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 4703-4712).\n\n[4] Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H., & Fu, Y. (2020). Rethinking classification and localization for object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10186-10195).\n\n[5] Fang, Z., Chen, N., Jiang, Y., & Fan, Y. (2024). Decouple and align classification and regression in one-stage object detection. The Visual Computer, 40(11), 7773-7786.\n\n[6] He, L., & Todorovic, S. (2022). Destr: Object detection with split transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9377-9386)."}, {"review_id": "wxXcJiiIpJ", "reviewer": "Reviewer_krwZ", "summary": "The authors introduce ELDET, which is based on different learning patterns for fitting noisy labels in the localization and classification tasks of object detection. ELDET uses an exponential parametric curve to identify the transition point from early-learning to memorization, enabling knowledge distillation to counteract the interference of noisy labels on the model. The proposed method improves the performance of multiple detection models on both general datasets and a medical detection dataset, across various label noise settings.", "questions": "1. In the FCOS experiments presented in Table 1, under the ELDET framework, why does the performance using the 20% localization noise dataset outperform that of the clean dataset? This phenomenon is not observed in other comparative methods.\n2. In Table 4, using GTBA alone actually decreases the model's performance. However, performance improves only when it is combined with CA. Could the authors please explain the reason for this?\n3. The paper lacks the analysis of the training cost.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths：\n1. The paper decouples classification errors and localization errors by designing task-specific metrics, allowing it to simultaneously address both types of noise in detection tasks.\n2. The architecture of the proposed method is straightforward to implement and readily extensible.\n3. The authors conduct comprehensive experiments and ablation studies.\n\nWeaknesses：\n1. The paper conducts experiments solely on convolutional-based detectors, lacking essential experimental results from several SOTA transformer-based detectors, such as DINO[1] and Relation-DETR[2]. These detectors often employ denoising training strategies to enhance robustness. Additionally, the paper does not consider ELDET's ability to handle noisy labels in comparison to these denoising strategies.\n2. The paper presents an unclear explanation regarding the calculation of the transition point. The relationship between the proposed CA and GTBA, as well as Eq (1) and Eq (2), is not well-defined. Furthermore, the paper lacks sufficient experimental results for different datasets and detectors to validate the conclusions in Sec 3 regarding the different patterns of fitting noise labels for localization and classification tasks.\n3. The paper lacks a more comprehensive discussion on the scale of the datasets. It does not address some larger datasets (e.g., Object365) as well as some smaller datasets (e.g., Oxford Pets). This is an important consideration for the generalization of the proposed method.\n\n[1] Zhang H, Li F, Liu S, et al. DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection\n\n[2] Hou X, Liu M, Zhang S, et al. Relation DETR: Exploring Explicit Position Relation Prior for Object Detection", "was_revised": true, "final_justification": "I maintain that several critical issues remain unresolved, leading to my decision to recommend a score of **reject**. The paper suffers from poor clarity, making it difficult to understand. The discussion regarding transformer-based detectors lacks comprehensiveness. Additionally, the increased training costs are not adequately addressed in the main paper, and the burden of incorporating this method seems excessive, especially on the MS COCO dataset.", "rebuttal": "We'd like to thank all of our reviewers for taking the time to give us constructive reviews. Your reviews will help us improve our methods and manuscripts.\n\n> \"Lacking essential experimental results from several SOTA transformer-based detectors employing denoising training to enhance robustness.\"\n\nWe appreciate the valuable observation regarding transformer-based detectors and their denoising training strategies. We would like to clarify that our main experiments focused on convolution-based architectures, and thus a direct comparison with transformer-specific denoising methods, which involves adding random noise to ground truth as an input to transformer decoder, was not feasible.\n\nTo address concerns about transformer architectures, we conduct additional experiments applying ELDET to Deformable DETR on PASCAL VOC:\n\n**Table: Performance of ELDET on Deformable DETR under combined noise conditions on PASCAL VOC (AP@50)**\n\n| Method   | Clean        | 20% Noise    | 30% Noise    | 40% Noise    |\n|----------|--------------|--------------|--------------|--------------|\n| Baseline | 74.27        | 68.60        | 65.39        | 62.52        |\n| ELDET    | **74.52 (+0.25)** | **68.82 (+0.22)** | **65.84 (+0.45)** | **62.91 (+0.39)** |\n\nWhile improvements are modest, these preliminary results indicate that ELDET can enhance performance on DETR-based models. The limited gains are partly due to restricted hyperparameter tuning during the rebuttal phase and the use of CrossKD, a distillation method initially designed for convolutional models rather than transformers. In the camera-ready version, we plan to incorporate distillation methods better tailored to DETR architectures[1] and anticipate further performance improvements. Additionally, applying the aforementioned transformer decoder denoising strategies could further boost results.\n\nImportantly, ELDET’s plug-and-play design ensures its broad applicability across various backbone architectures, including both convolutional and transformer-based detectors.\n\n---\n\n> \"Unclear explanation regarding the calculation of the transition point and the relationship between the proposed CA and GTBA, as well as Eq (1) and Eq (2).\"\n\nThank you for highlighting these clarifications. Below is a concise explanation of the **transition point calculation** and the relationship between **CA** and **GTBA** metrics:\n\n\n### Transition Point Calculation\n\nWe model training performance $f(t)$ (e.g., AP over epochs $t$) with the exponential function:\n\n$ f(t) = a \\left(1 - e^{-b \\cdot t^c}\\right) $\n\nwhere parameters $a,b,c$ are fitted to observed data.\n\nThe transition point $t^*$ from early learning to memorization is identified when the relative change in the derivative crosses a threshold $\\gamma$:\n\n$ \\frac{|f'(1) - f'(t^*)|}{|f'(1)|} > \\gamma $\n\nThis detects when learning slows down significantly, marking onset of memorization of noisy labels.\n\n\n### CA and GTBA Metrics\n\n- **Class-Agnostic (CA)** treats all objects as a single class to isolate **localization** performance by ignoring classification errors. CA tracks how well the model learns precise object locations despite noisy labels.\n\n- **Ground Truth Box Allocation (GTBA)** replaces predicted boxes with ground truth ones when IoU exceeds threshold ($\\tau\\$), removing localization error influence to isolate **classification** performance.\n\nTogether, CA and GTBA provide complementary views into memorization dynamics — CA tracks localization noise memorization, GTBA tracks classification noise memorization — enabling task-specific transition detection.\n\n\n---\n\n> \"Lack of sufficient experimental results for different datasets and detectors ... regarding the different patterns of fitting noise labels for localization and classification tasks.\"\n\nDue to space constraints, we provide a detailed response to this point in our reply to Reviewer RwS3. Please kindly refer to that response for a comprehensive explanation.\n\n---\n\n> \"Lacks of the scale of the datasets such as larger datasets (e.g., Object365) as well as some smaller datasets (e.g., Oxford Pets).\"\n\nWe acknowledge this important point regarding dataset scale diversity. Our experiments already demonstrate effectiveness on datasets of varying scales: VinDr-CXR represents a relatively small dataset (18,000 images), which showed substantial improvements (e.g., FCOS: 25.32→32.74 AP under 40% noise), indicating ELDET's effectiveness even on limited data.\n\nWe have conducted additional experiments on Oxford Pets, a smaller dataset with ~7,400 images across 37 categories:\n\n**Table: Performance on Oxford Pets Dataset (AP@50)**\n\n| Method         | Noise Ratio | Localization Noise | Categorization Noise | Combined Noise |\n|----------------|-------------|-------------------|---------------------|---------------|\n| -      | 0.3         | 0.893             | 0.794               | 0.843         |\n| ELDET | 0.3         | **0.899**             | **0.836**               | **0.885**         |\n| -      | 0.5         | 0.810             | 0.768               | 0.665         |\n| ELDET | 0.5         | **0.859**             | **0.786**               | **0.795**         |\n| -      | 0.7         | 0.791             | 0.714               | 0.640         |\n| ELDET | 0.7         | **0.831**             | **0.801**               | **0.859**         |\n\nRegarding Object365, our method's consistent improvements across existing diverse scales (small: VinDr-CXR, medium: PASCAL VOC, large: MS COCO) provide strong evidence for scalability. We aim to include Object365 evaluation results in the camera-ready submission, ensuring a thorough demonstration of the method’s applicability to large and diverse datasets.\n\n---\n\n> \"In the FCOS experiments presented in Table 1, under the ELDET framework, why does the performance using the 20% localization noise dataset outperform that of the clean dataset?\"\n\nThis interesting phenomenon can be attributed to ELDET's regularization effect through knowledge distillation. When FCOS processes 20% localization noise, our early-learning teacher provides beneficial guidance that acts as implicit regularization, similar to how data augmentation can sometimes improve generalization. The distillation process helps the model learn more robust spatial representations that generalize better than training on clean data alone. This effect is specific to ELDET because other methods lack the early-learning guidance mechanism—they either ignore the regularization benefits (baseline) or employ different noise handling strategies that don't provide the same regularization effect (ORSOD, ADELE). The magnitude of this improvement (+1.40 AP) suggests that the early-learning teacher's spatial knowledge provides valuable inductive bias for FCOS's anchor-free detection paradigm.\n\n---\n\n> \"In Table 4, using GTBA alone actually decreases the model's performance. However, performance improves only when it is combined with CA.\"\n\nWe appreciate this important observation. Our key finding is that memorization of localization noise occurs earlier than categorization noise. When only GTBA is applied, our framework may provide early-learning guidance focused on classification while the model's performance on localization task is still in its noisy memorization stage. This mismatch can lead to suboptimal learning and decreased overall performance.\n\nBy combining GTBA with CA, we provide coordinated early-learning guidance for both classification and localization tasks. CA specifically targets localization noise by treating all classes as one category, helping the model accurately identify the proper transition point before localization memorization begins. This allows the model to respect the sequential memorization order from localization to classification. This balanced approach enables the model to effectively handle both noise types, leading to significant performance gains compared to using GTBA alone.\n\n---\n\n> \"The paper lacks the analysis of the training cost.\"\n\nWe acknowledge this important practical consideration and provide comprehensive training cost analysis. Our experiments were conducted on a single NVIDIA A100 GPU.\n\n**Table: Training Cost Analysis**\n\n| Method | Dataset | Detector | Training Time (hours) | Memory Usage (GB) |\n|--------|---------|----------|----------------------|------------------|\n| - | PASCAL VOC | RetinaNet | 2.51 | 11,418 |\n| ELDET | PASCAL VOC | RetinaNet | 4.23 | 17,967 |\n| - | PASCAL VOC | FCOS | 2.13 | 10,507 |\n| ELDET | PASCAL VOC | FCOS | 3.59 | 14,892 |\n| - | MS COCO | RetinaNet | 8.03 | 11,418 |\n| ELDET | MS COCO | RetinaNet | 15.43 | 17,967 |\n\nThe additional computational overhead of our method mainly comes from three sources: (1) exponential curve fitting to detect early learning transitions, (2) the computation of the knowledge distillation loss, and (3) exponential moving average (EMA) updates of the teacher model’s parameters.\n\nA noticeable part of the increased training time arises from per-epoch validation performed during the early learning phase. To detect the timing of early-learning termination, validation on the entire training set occurs in more than half of the initial training process, which contributes to the overhead on the training time. However, since validation is limited to this early-stage, its relative impact diminishes with longer training schedules.\n\nWhile ELDET does increase training time and memory usage due to maintaining a frozen teacher network for additional forward passes, this cost can be viewed as acceptable because it potentially replaces more expensive and complex noise detection or correction procedures, making the overall approach efficient and practical.\n\n\n---\n\n[1] Wang, Y., Li, X., Weng, S., Zhang, G., Yue, H., Feng, H., ... & Ding, E. (2024). Kd-detr: Knowledge distillation for detection transformer with consistent distillation points sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16016-16025)."}, {"review_id": "h3vZ6rx3lg", "reviewer": "Reviewer_XQNm", "summary": "This paper introduces ELDET, a self-distillation pipeline designed to train object detectors effectively using noisy data. The authors leverage the observation that models initially learn from clean data before overfitting to noisy labels. ELDET addresses this by initializing a teacher model with an early-stage trained model, then learning from both the noisy input data and the teacher's predicted signals. The pipeline's effectiveness is validated across standard object detection benchmarks, PASCAL VOC and MS COCO, as well as additional medical datasets.", "questions": "- Why do the reported performances on clean data in Table 1 and Table 2 vary for different methods, and what factors contribute to these discrepancies?\n\n- Could you elaborate on why the relative performance drop between noisy and clean data appears to be larger for ELDET compared to the baseline method (Tab. 2)?\n\n- In Figure 5, the performance of ELDET initially declines before subsequently improving. What are the potential underlying reasons for this observed trend?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nThe paper's most intriguing finding is the divergent timing of memorization phases for classification and bounding box regression, offering valuable insights into how models learn from noisy data. Furthermore, the demonstrated strong performance across common datasets (PASCAL VOC, MS COCO) and additional medical datasets highlights the pipeline's robustness and generalizability. Finally, the fluent and accessible presentation makes the complex concepts easy to grasp, enhancing the paper's overall impact.\n\nWeakness:\n\nThe ablation study lacks depth without a detailed analysis of the learning process's phenomena. (Some examples are listed in the **Questions**)", "was_revised": true, "final_justification": "Considering response from the authors and other reviewers' comments, I think there is no significant problem preventing the acceptance of the paper. Therefore, I would like to raise my score.", "rebuttal": "We'd like to thank all of our reviewers for taking the time to give us constructive reviews. Your reviews will help us improve our methods and manuscripts.\n\n> Why do the reported performances on clean data in Table 1 and Table 2 vary for different methods, and what factors contribute to these discrepancies?\n\nThank you for this important observation. The performance variations on clean data between different methods can be attributed to several key factors. \n\nFirst, noise-robust training methods inherently introduce **regularization effects** that can act as random perturbations during optimization. These regularization mechanisms, while designed to handle noisy scenarios, can introduce variability in clean data performance due to their stochastic nature and different optimization dynamics. \n\nSecond, methods like ORSOD and ADELE employ different **loss weighting and sample selection strategies** that can interact differently with various detector architectures, leading to performance variations even on clean data. Additionally, the initialization and convergence behavior of these methods can vary across different random seeds, as robust training methods often modify the optimization landscape in ways that affect final performance. \n\nOur ELDET method shows consistent improvements on clean data (e.g., +1.45 AP for RetinaNet on PASCAL VOC) because the early-learning distillation acts as beneficial regularization that enhances generalization rather than simply addressing noise.\n\n---\n\n> Could you elaborate on why the relative performance drop between noisy and clean data appears to be larger for ELDET compared to the baseline method (Tab. 2)?\n\nThank you for this insightful observation. We would like to emphasize that the difference in average drop between ELDET and the baseline narrows considerably to only **0.52 AP** (2.65 AP for ELDET vs. 2.13 AP for baseline). This indicates that the overall degradation due to noisy labels is not substantially worse for ELDET. This phenomenon is primarily attributable to ELDET's superior baseline performance on clean data, which is actually a positive indicator rather than a limitation. It is noteworthy that ELDET consistently outperforms baseline methods on clean data across all detectors, with an average improvement of +1.74 AP (ranging for Faster R-CNN to +2.32 AP for GFL).\n\nWe would like to emphasize that our ELDET framework successfully achieves effectiveness across all noise settings. For instance, under 40% combined noise—the most challenging scenario—ELDET shows absolute improvements of +1.04 AP (RetinaNet), +1.46 AP (FCOS), +0.73 AP (Faster R-CNN), and +0.77 AP (GFL) compared to baselines. These consistent absolute gains demonstrate that our method successfully addresses both categorization and localization noise while maintaining superior performance across varying noise conditions.\n\n\n---\n\n> In Figure 5, the performance of ELDET initially declines before subsequently improving. What are the potential underlying reasons for this observed trend?\n\nThis initial performance decline followed by improvement is a well-documented characteristic pattern in knowledge distillation frameworks[1,2]. Several factors contribute to this phenomenon. \n\nFirst, the introduction of the distillation loss creates a multi-objective optimization problem where the model must balance learning from both noisy ground truth and teacher guidance. During early epochs, there can be conflicting signals between these two sources, leading to temporary performance degradation. \n\nSecond, our EMA-based teacher update mechanism requires time to stabilize and provide consistent guidance. The dynamic interaction between teacher and student creates a moving optimization target that can cause initial instability before convergence. \n\nThird, knowledge distillation research has shown that this initial decline represents a transition period where the student model moves away from simply memorizing noisy patterns toward learning more generalizable representations guided by the early-learning teacher. The subsequent improvement demonstrates that the distillation process successfully helps the model focus on clean learning patterns, ultimately achieving better performance than baseline methods.\n\n---\n[1] Cho, J. H., & Hariharan, B. (2019). On the efficacy of knowledge distillation. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4794-4802).\n\n[2] Sun, Z., Liu, Y., Meng, F., Chen, Y., Xu, J., & Zhou, J. (2025). Warmup-Distill: Bridge the Distribution Mismatch between Teacher and Student before Knowledge Distillation. arXiv preprint arXiv:2502.11766."}, {"review_id": "6OUIeVsHJ0", "reviewer": "Reviewer_iaRc", "summary": "Object detectio algorithms are sensitive to categorization and localization errors. Many exisitng methods address one or other of these errors, not both. By leveraging an observation made earlier referred to as early-learning phenomenon – in which models trained on noisy data with mixed clean and noisy labels tend to first fit to the clean data, and memorize the noisy labels later – a teacher-student networks is proposed in a knowledge distillation framework so that detectors can be made robust to categorization and localization errors. The proposed method has been evaluated on many benchmark datasets such as PASCAL VOC, MS COCO and VinDr-CXR medical datasets.", "questions": "While consideration of categorization and localization nois is appropriate, it will be good to evaluate how the proposed approach will work when objects are occluded. \nHow well the adaptation to infrared images will work?\nHave the authors considered other kinds of noise that can affect the performance of object detectors?", "limitations": "I do not see a separate section on limitations.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strenghts: Object detection is an important task in computer vision and robust detectors that address categorization and localization errors is desired. This paper addresses both errors. The main advantage is the plug and play setup so that exisitng detectors can be improved. Evaluations using standard object detection datasets gives confidence that the proposed method is useful.\nWeaknesses: While the method has the strenghts mentioned above, the performance improvements vary depending on the type and amount of noise (less than 1% in some cases, and not the best in some cases). The improvemnets for the MS COCO dataset are typically less than for the Pascal VOC dataset.", "was_revised": true, "final_justification": "While the authors have provided reasonable opinions on potential extensions to IR images, occlusions and other types of noise, no tangible evidence has been provided using experiments or theoretical arguments. So I will lower the score to borderline accept.", "rebuttal": "We'd like to thank all of our reviewers for taking the time to give us constructive reviews. Your reviews will help us improve our methods and manuscripts.\n\n> ... the performance improvements vary depending on the type and amount of noise (less than 1% in some cases, and not the best in some cases). The improvements for the MS COCO dataset are typically less than for the Pascal VOC dataset.\n\nWe appreciate this observation regarding performance variations. While some improvements may appear modest in absolute terms, they represent meaningful gains in challenging noisy scenarios. Specifically, our method achieves consistent improvements across different noise conditions: on PASCAL VOC, ELDET shows an average improvement of 2.29 AP across all detectors and noise settings, with 87.5% of cases (35/40) demonstrating positive improvements. On MS COCO, we achieve an average improvement of 1.27 AP with 72.5% of cases (29/40) showing positive gains.\n\nThe smaller improvements on MS COCO compared to PASCAL VOC are expected due to the dataset's inherent complexity and scale. Even modest AP improvements (1-2%) are significant in object detection, particularly under noisy conditions where performance typically degrades substantially. The few negative cases occur only in clean settings for specific detectors, which is acceptable as our method specifically targets noisy scenarios.\n\n---\n\n> ... it will be good to evaluate how the proposed approach will work when objects are occluded.\n\nThis is an excellent suggestion for future work. While our current approach focuses on annotation noise (categorization and localization errors), the phenomenon we leverage could potentially extend to occlusion scenarios. The robustness principles underlying our method—using early-stage models as teachers before overfitting occurs—may help models maintain better representations of partially visible objects. However, this would require specialized adaptations, such as occlusion-aware metrics and potentially different early-learning termination criteria. We plan to investigate this extension in future research.\n\n\n---\n\n> How well the adaptation to infrared images will work?\n\nWe believe our approach will adapt well to infrared images. While we haven't tested specifically on infrared imagery, we demonstrated strong cross-domain performance on the VinDr-CXR medical dataset, which represents X-ray images with significantly different visual characteristics from natural RGB images. Our method showed substantial improvements across all detectors (e.g., FCOS improved from 25.32 to 32.74 AP under 40% combined noise), indicating robust generalization capabilities. The plug-and-play nature of our framework makes it readily applicable to infrared imaging scenarios, as the early-learning phenomenon is a fundamental property of deep networks that should manifest similarly across different image modalities.\n\n\n---\n\n> Have the authors considered other kinds of noise that can affect the performance of object detectors?\n\nThank you for this important question. While our work focuses on the two most prevalent annotation noise types (categorization and localization errors), we acknowledge that object detectors face various other noise sources, including image-level noise (Gaussian, salt-and-pepper), sensor noise, missing annotations, and bogus bounding boxes. The early-learning principle we leverage is quite general and could potentially be extended to handle these additional noise types. For future work, we plan to investigate extending our framework to address missing annotations and bogus boxes, incorporating image-level noise robustness, and developing unified approaches for multiple noise sources simultaneously."}]

Author rebuttal:

Meta-review: This paper proposes a distillation-based approach to handle label and location noise in training data for object detection. It is based on empirical observation that in deep learning models, during the initial stages of training, model gradients are dominated by clean labels, while influence of noisy labels emerges in later stages.  Early clean model is used as a teacher model for training the object detection model. Distillation loss is used together with conventional loss from object detection tasks. It is also observed that models tend to memorize localization noise earlier than categorization noise motivating use of two-stage distillation.Experiments are conducted by adding noise to labels in standard object detection datasets (PASCAL VOC and MS COCO); while MSCOCO is "large" in the number of objects, it has only a small number of categories.

Several issues were raised in the initial reviews regarding the generality of the method and setting of the transition point for switching models. The authors provided detailed feedback and results on a wider set of detector models. One criticism was that the original paper considered CNN-based detectors only. In rebut, authors provided results for some transformer-based detectors also though the reviewers are not completely satisfied. Authors also provided additional details that seemed to satisfy various concerns. After rebut discussion, three reviewers recommended acceptance but one continued to rate the paper as a borderline reject.

The ACs feel that the paper is weak on analysis of detectors such as DINO and the results are justified only empirically but, still, it offers an approach that is worth exposing to the research community. Hence, we recommend that it be accepted. The authors should include the additional analysis and results presented in the rebut but are discouraged from including results that are derived after this period.
Decision: Accept (poster)