Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection
Authors: Moussa Kassem Sbeyti
Nadja Klein
Azarm Nowzad
Fikret Sivrikaya
Sahin Albayrak
Venue: Accepted by TMLR ()

Abstract: Semi-supervised object detection (SSOD) based on pseudo-labeling significantly reduces dependence on large labeled datasets by effectively leveraging both labeled and unlabeled data. However, real-world applications of SSOD often face critical challenges, including class imbalance, label noise, and labeling errors. We present an in-depth analysis of SSOD under real-world conditions, uncovering causes of suboptimal pseudo-labeling and key trade-offs between label quality and quantity. Based on our findings, we propose four building blocks that can be seamlessly integrated into an SSOD framework. Rare Class Collage (RCC): a data augmentation method that enhances the representation of rare classes by creating collages of rare objects. Rare Class Focus (RCF): a stratified batch sampling strategy that ensures a more balanced representation of all classes during training. Ground Truth Label Correction (GLC): a label refinement method that identifies and corrects false, missing, and noisy ground truth labels by leveraging the consistency of teacher model predictions. Pseudo-Label Selection (PLS): a selection method for removing low-quality pseudo-labeled images, guided by a novel metric estimating the missing detection rate while accounting for class rarity. We validate our methods through comprehensive experiments on autonomous driving datasets, resulting in up to 6% increase in SSOD performance. Overall, our investigation and novel, data-centric, and broadly applicable building blocks enable robust and effective SSOD in complex, real-world scenarios.  Code is available at https://mos-ks.github.io/publications.

Published in Transactions on Machine Learning Research (03/2025)
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection
Moussa Kassem Sbeytil22.3 Nadja Klein'
Azarm Nowzad
Fikret Sivrikaya' Sahin Albayrak?
"Scientific Computing Center, Karlsruhe Institute of Technology *DAI-Labor, Technische Universität Berlin 3Continental AG
(moussa. badja.klein)0kis
azarm. www.o//accootieeaaallloorraaeaaa
(fikret.eivrikaya, sahin. aahin.albayrak)daae albayyrat)@daillaooo....
Reviewed on OpenReview: htteer//ppaareeeseeeee//////tttt
Abstract
Semi-supervised object detection (SSOD) based on pscudo-labeling significantly reduces dependence on large labeled datasets by effectively leveraging both labeled and unlabeled data. However, real-world applications of SSOD often face critical challenges, including class imbalance, label noise, and labeling errors. We present an in-depth analysis of SSOD under real-world conditions, uncovering causes of suboptimal pseudo-labeling and key trade- offs between label quality and quantity. Based our findings, we propose four building blocks that can be seamlessly integrated into SSOD framework. Rare Class Collage (RCC): data augmentation method that enhances the representation of rare classes by creating collages of rare objects. Rare Class Focus (RCF): a stratified batch sampling strategy that ensures a more balanced representation of all classes during training. Ground Truth Label Correction (GLC): a label refinement method that identifies and corrects false, missing, and noisy ground truth labels by leveraging the consistency of teacher model predictions. Pseudo-Label Selection (PLS): selection method for removing low-quality pseudo-labeled images, guided by novel metric estimating the missing detection rate while accounting for class rarity. We validate our methods through comprehensive experiments on autonomous driving datasets, resulting in to 6% increase in SSOD performance. Overall, our investigation and novel, data-centric, and broadly applicable building blocks enable robust and effective SSOD in complex, real-workd scenarios. Code available at htttss:/moss-e: http:s//moosss.gg..buu..///oope
1 Introduction
Training robust object detectors for real-world applications challenging due to the high cost of obtaining large labeled datasets with accurate labels. Semi-supervised learning (SSL) offers promising solution by leveraging both labeled and unlabeled data. Existing SSL methods often utilize teacher-student framework, where a teacher model trained on labeled data generates pscudo-labels for unlabeled data, which are then
used logether to train a student model (Li el. al. 2020b Sohn el. al. 2020 Yang et al. 2021 Xu el al. 20021 Liu et al.) 2021 Zhang et al. 2022 Liu et al. 2022b Mi et al. 2022 Li et al. 2022c). While effective, the potential of SSL is often hindered by the quality of both labeled and pscudo-labeled data.
Acknowledggment. This work has been funded by the KiKIT (The Pilot Program for Cure-Infornatics the KIT) of the Helmholtz Association. It also supported by the Helmholtz Association Initiative and Networking Fund the HAICOREaKIT partition. Additionally, this work acknowledges finaucial support the Cerman Federal Ministry for Eco- nomic A Affirs and Climate Action (BMWK) and the European Union within the project "just better DATA (JDDATA)" grant 230033...

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We identify three primary challenges that affect label quality and, consequently, the effectiveness of pscudo- labeling-based SSL frameworks. These are: (1) class imbalance in both labeled and pseudo-labeled data, which leads to poor generalization on underrepresented classes; (2) noise in ground truth labels, including incorrect or missing labels, which compromises training; and (3) incomplete or inaccurate pscudo-labels generaled by the teacher, particularly for underrepresented classes, which propagate errors to the student. To address these challenges, we propose the following four novel building blocks aimed at increasing the effectiveness and robustness of SSOD by enhancing label quality, utilization, and class balance.
First, the natural occurrence of objects in the real-world leads to imbalanced class distribution within each image. Re-sampling entire images only reinforces this imbalance, as cach image retains its inherently unbalanced class distribution (Chang et al. 2021). ) Therefore, we introduce Rare Class Collage (RCC), which enhances the representation of underreppesenteed classes by creating a collage of cropped rare object images.
Second, while upsampling rare classes helps mitigate the imbalance, their infrequent occurrence in random training batches prevents the model from retaining the learned features. We propose Rare Class Focus (RCF), which ensures consistent exposure to rare classes by including at least one rare example in each training batch. Our method departs from the standard random sampling strategy, enhancing the ability of the model to handle challenging rare cases.
Third, labeled data is often implicitly assumed to be flawless, although ground truth label errors can range from 10% to 40% (Northcutt et al. 2021 Seedat et al. 2024), significantly hindering performance (Kuan & Mueller 2022). Manual inspection to identify mislabeled data time-consuming, error-prone, and frequently impractical due to the large dataset sizes (Tkachenko et al. 2023). We investigate the effect of noisy, false, and missing ground truth labels on performance by simulating two levels of errors. Our finding motivate the introduction of our Ground Truth Label Correction (GLC) method to refine noisy or incomplete ground truth labels by leveraging anference-time augmentation and teacher model predictions.
Finally, detections are usually filtered based on a score Li ct al. 2022b Xu ct al. 2021), with a trade-off between recall and precision (Zhang el al., 2022). For example, Xu el al. (2021) observe that setting a high threshold, such as 0.9 (Sohn et al.) (0200) results in high precision. However, also results in low rocall, leading to many missed detections. In contrast, setting a lower threshold improves recall but retains many false detections. Although two-stage filtering, dynamic thresholding (Cai et al.
2021 Li et al. 2022c Chen et al. 2023a), or using uncertainty a score (Munir et al. 2021 Cai et al.) this trade-off, the resulting missing detections are still not addressed. Typically, all pseudo-labeled images are used to train the student model after the score-based filtering despite missing or false detections. This introduces noise and hinders effective learning (Xu et al. 2019 Yang el al. 20200. We therefore introduce Pscudo-Label Selection (PLS), a method that estimates the proportion of missing detections an image using a novel metric. The metric also accounts for class distribution, ensuring that images with high missing detections are retained if they contain valuable learning signals from rare classes. This approach enables the filtering of low-quality pseudo-labeled images that could otherwise negatively impact model performance.
2021), can help reduce
Our building blocks are designed to be as model- and framework-agnosttn as possible, ensuring broad applica- bility to many SSOD frameworks and object detection tasks. Additionally, they add minimal computational overhead, ensuring feasibility for large-scale, world applications. Due to high labeling costs and abun- dant unlabeled data, SSL is crucial in real-worid applications. Although some works (Cai et al., 2021 Han et al. 2021 Munir et al. 2021) explore the use of pseudo-labels for domain adaptation in autonomous driv- ing comprehensive analyses of the efficacy of pseudo-labeling remain limited. Our work addresses this gap by analyzing the key limitations of SSOD and proposing improvements that result in more effective and robust worldworld SSOD. Our contributions are summarized as follows:
We investigate the limitations of pseudo-labels in SSOD on real-world datasets, identifying factors hindering its effectiveness: (1) error propagation due to class imbalance, (2) noisy and erroncous ground truth labels, and (3) missing and false pseudo-labels.
Based on the resulting insights, we develop novel and computationally efficient building blocks, cach of which can be independently integrated into an SSOD framework lo:
Balance the class distribution and mitigate error propagation on underrepresented classes.
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Assess the impact performance by varying levels of noisy, false, and missing detections in ground truth labels and correcting them.
Remove low-quality pseudo-labeled images post- post-filtering while considering class distribution.
2
Related Work
Class Amplifiers: Class imbalance often leads to rare classes being filtered out from pscudo-labels due to them receiving low confidence scores. The ellectiveness of re-sampling, iee, adjusting frequency (Yu et al., 2022 Chang et al. 2021), and re-weighting, i.e., adjusting sample weights in the loss function (Tantithamtha-
vorn et al. 2018 Cui et al. 2019 Li ct al.] 2020a] Yu et al. 2022), strongly varies depending detector type (Crasto 2024p. Re-weighting by inverse class frequency often leads to poor performance, particularly in highly imbalanced datasets where noisy rare samples receive disproportionately high weights (Phan & Yamamoto 2020). On the other hand, synthetic re-sampling (Chawla et al. 2002 Chen et al. 2023b) and
class-aware sampling (Shen et al. 2016) increase the frequency of rare classes artificially, reducing perfor-
mance on common classes. Furthermore, entire image collages (Chen et al. 2020 Lyy et al. 2023
Chen et al.,
2023b) downscale images to increase object density, while copy-pasting approaches (Wang ct 2018 Hong
et al., 2019 Yan al., 2022) can compromise spatial and contextual consistency. Ghiasi et al.
[0021) | demon-
strate the effectiveness of copy-pasting for segmentation. However, object detection labels are rectangles that include background along with the object. This contrasts with instance segmentation, where objects can be seamlessly integrated into new images. (Shen et al. 2016)) ensure uniform class representation in the training batches for classification, but such a representation is challenging due to strongly imbalanced real-world data. Other class re- balancinng techniques improve recall but may decrease precision due to overfitting on rare classes (Tantithamthavorn et al., 2018). To address these challenges, our Rare Class Collage (RCC) and Rare Class Focus (RCF) approaches increase the representation of rare classes while preserving spatial context. They ensure consistent and balanced exposure to rare classes during training without sacrificing performance on common classes.
Label Filters: Confirmation bias, where models overfit, to incorrect labels, remains a critical challenge in (Aazo et al.) 2020 Wang et al. 2021). ). Noisy and incomplete labels hinder effective learning. While
SSOD
several works attempt to mitigate pseudo-labed label noise using dynamic thresholding (Munir et al.] 2021 Chen
let al. 2022: Li et al.) 2023 Chen al. 2023a Kimhi al.]
2024), consistency-
filtering (Li et al.,
2022b;
Yang et al.] 2021 Yan et al.] 2022), uncertainty-weige wet filtering (Cai et al. 2021) iterative
refinement through multi-iteration predictions (Wang ct al.
2018 these approaches often overlook noise in the ground truth labels, assuming perfectly labeled data. Seedal et al. (2024) use aleatoric uncertainly to identify noisy ground truth labels. [hhou et al.] (2023) refine ground truth boxes by aggregating region proposals in two-stage detectors. These mothods generally focus on box refinement and do not address missing or false labels. In contrast, inspired by consistency-based pseudo-label refinement (Li et al.] 2022b) and consistency regularization (Li et al.] 2022a), our Ground Truth Tabel Correction (GT.C) mechanism simultaneously addresses noisy, missing, and false detections by evaluating ground truth labels through teacher predictions under inference-time time augmentation.
As for pseudo-label quality, confidence-based filtering is typically used to retain high quality pseudo-labels (Liu et al. 2021), ; but high-confidence predictions do not always indicate accurate detections (Li et al. 2020b). and low-confidence scores may not always imply incorrect detections. The filtering process often leads to missing detections and noisy pseudo-labels remaining in the dataset, which amplifies errors over successive training iterations (Wang et al.] 2021). The balance between the quality and quantity of pseudo-labels remains an unresolved challenge, as highlighted by conflicting works regarding the benefits of large volumes
of pseudo-labeled data (Wang et al. 2021 Sohn et al. 2020). Wang et al. 2021 and Yang et al. (2021) argue that the volume of unlabeled data needs to be considered carefully, more data does not necessarily lead to better performance. In contrast, |Sohn Sontr et al. (0200) highlight the importance of large-scale unlabeled data in the context of SSL and do not observe a clear correlation between the accuracy of the pseudo-labels and the performance of their SSL approach. Our work investigates the impact of quality vs. quantity on performance. Furthermore, the impact of missing detections remains unclear. [u et al. (2019) show that the performance of detectors declines significantly as the rate of missing detections increascs. In contrast, Wu et al. (2018) and Chadwick & Newman (2019) argue for the robustness of detectors to it We show that the impact of
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missing detections performance depends the class of the missing objects. Building this finding, propose Pseudo-Tabel Selection (PI.S). PI.S proposes metric that estimates the missing detection rate per image while accounting for class distribution, allowing for targeted selection of pseudo-labeled images post-filterigg
3 SSOD Building Blocks
In this section, we begin by outlining the student-teacher pseudo-labeling framework and then roduce our proposed building blocks. An overview of their integration into an SSOD framework illustrated in Fig. using the STAC framework (Sohn et al. 2020 representative example.
RCC
RCF
Butch
Class Amplifiers
Original
Teacher MT
Orignnl Anginentd
PLS
GLC
Student (2
False Label
Missing Label
Noisy Label
6
Unlaheled Data
I abel Filters
RCC: Rare Class Collage
RCF; Rare Class Focus
PLS:: Pseudo- Lahel Selection
GLC: Ground Label Comection
D,66., (1- 5) S)(6)
Prediction Griand Trah
Pscudo-Labeled Data
Figure Our building blocks integrated into an exemplary SSOD framework. The teacher model MT. trained labeled data, generates pseudo-labels for unlabeled data, which are then filtered by confidence threshold 8,. To address class imbalance, Rare Class Collage (RCC) (Section 3.2.1 crops instances of rare classes and combines them into collages, increasing their representation. Rare Class Focus (RCF) (Section b.2.2p ensures each training batch contains common and rare classes, with augmented rare class images to boost their impact. Ground Truth Label Correction (GLC) (Section 3.3.1 corrects false, missing, and noisy labels by utilizing teacher prediction consistency across augmentations. Pseudo-Label Selection (PLS) (Section 3.3.2) removes pscudo-labeled images with many missing detections, estimated using our metric D,(6.. ,3, which incorporates detection confidence and class rarity. Together, our methods enhance the ability of the student model M5,).) to learn effectively from both labeled and pseudo-labeled data, minimizing the propagation of errors from the teacher model.
3.1 Pseudo-Labeling
In SSOD, we are given a labeled dataset Diabeled {1 b,. m)) of images 2,, bounding boxes b, and class labels C₁ for i 1,... m. and larger unlabeled dataset Dunlabeled = {ai)/_>> where I > m. The objective is to train student model Ms using both labeled data and pseudo-labels, with the latter generated by

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teacher model MT for the unlabeled data. Formally, the teacher model MT, trained Dlabeled, generates pseudo-labels for Punlabeled; resulting in a pseudo-labell dataset pseudo = { (z,, by G)) where 6, and & represent the predicted bounding boxes and class labels. Following Sohn (2020), pseudo-labeled images are augmented (A),, and the student model Mc(6.) is trained by minimizing the total loss given by
leotal
1 m Σ
(Zt bi. ci) + A E
I(s (A(x;)
where Laabeldd and épseudo are the default loss terms of the detector calculated Dabeled and D pseudo: respectively, and I is an indicator function that selects pseudo-labels based on their predicted confidence score Formally, I(s) = if the confidence score 2 6, and zero otherwise. The hyperparameter X, controlling the balance between the loss terms, is best set as À E [1,2] (Sohn et al. 2020
3.2
Class Amplifiers
To alleviate class imbalance, introduce two methods that boost the representation of rare classes in Diabeled and ensure their consistent inclusion during training for balanced learning across all object classes.
3.2.1 Rare Class Collage (RCC)
RCC addresses class imbalance by increasing the representation of rare classes through collage generation. Rare objects identified the ground truth labels are cropped and upscaled fit the height of new collage image, while preserving their original aspect ratios. These resized objects arranged side by side blank canvas to create collage primarily composed of rare class objects.
Let C, denote the manually defined set of rare object classes. For each object of a class E C, in an image 24 E Diabeled, we crop an area around its bounding box 6, [6, b%, -,,,,,,,,,, bP], where 6, and by are the top-left coordinates, and 6th and b/t represent the width and height. To determine the area, we sample a random scale factor pr from uniform distribution U (77r,min: 7r,max), resulting in an expanded bounding box 6, = [max(0, by - p,b""), max(0, 6y b(), min(w, br" + (1 + pr ),"",")) min (h, 6% + (1 + Pe p=)) where and h are the width and height of the original image. This ensures that the cropping area based on the resized bounding box remains within the image boundaries.
After cropping, the rare objects are shuffled, resized, and sequentially pasted into a new collage image x/. The newly generated collages are added to Diabeled, thereby increasing the representation of rare classes. Unlike previous works, RCC ensures improved generalization across specifically targeted rare classes without compromising performance on more common classes. It also computationally efficient, as it processes only the images and their corresponding labels in Diabeled-
3.2.2 Rare Class Focus (RCF)
Class imbalance in real-world data leads to non-uniform distribution of classes across training batches, resulting in uneven gradient updates and limiting the ability of the detector to learn from rare class examples effectively. RCC increases the overall representation of rare classes, but it does not guarantee their balanced presence within each training batch. RCF further mitigates class imbalance by ensuring each training batch contains at least one example of rare class. Unlike RCC, which explicitly defines rare classes, RCF stratifies Diabeled into common and rare examples based class frequency, adjusting batch composition to include rare examples consistently. For that, it assigns score F, to each image z; based its class distribution. For an image Z,, containing classes, the score is computed as
Tog(Sx)'
where fü is the frequency of class is in Dlabeled. This inverse-logarithmic formulation ensures that rarer classes receive higher scores, emphasizing their importance in the learning process (Phan & Yamamoto 2020 The
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class scores are then linearly scaled within a predefined range [1, where y is the maximum score that controls the scaling strength.
To construct training batches, the score F, is computed for all images in Diabeled- Images are ranked based on their scores, and the top ho images are classified as rare, while all others are considered as common. The value of & is selected such that each batch contains at least one rare image, satisfying the condition k 2 B where m is the total number of images in Diabeled and B is the batch size. Additionally, the rare set of images is augmented and shuffeed twice to introduce diversity by adding pair of rare samples to each batch. This ensures the batch size remains multiple of two, ensuring efficient GPU utilization and stable batch normalization. Our RCF approach promotes balanced learning, providing adequate focus on rare classes, while preserving the ability of the model to generalize across all classes. Similar to RCC, the steps in RCF incur minimal computational overhead, as they operate solely on Diabeled.
3.3 Label Filters
The quality of labels, both ground truth and pseudo-labels, is crucial to model performance. Leveraging the knowledge of the teacher model before training the student model helps prevent error propagation from label errors.
3.3.1 Ground Truth Label Correction (GLC)
GLC is designed to refine noisy labels, remove false labels, and add missing ground truth (GT) labels by leveraging a trained teacher model and inference-time augmentations on the training set. This approach acknowledges that GT labels are not always reliable and aims to prevent erroneous GT labels from propa- gating through the training process to the student model. Specifically, the teacher model generates bounding box predictions on the original training images and their augmented versions post-training. They are then compared against GT labels to identify and correct errors before training the student.
Typical augmentations such as Gaussian blur, horizontal flipping, and Gaussian noise can be employed. The computational cost of GLC scales lincarly with the number of augmentations, starting at minimum of twice the base inference time. Corrections are deternined based on the Intersection over Union (IoU) bet ween predicted boxes and GT boxes b. The core intuition is that consistent predictions from the teacher model across augmented versions of an image despile the teacher not being trained on these augmentations indicate the reliability of its detections. This consistency serves as an indicator of whether the detections may be used to correct GT errors. We define three error cases:
False GT: A GT label b that does not intersect with any prediction, even at a low confidence threshold 8, = 0.1, likely indicates a false label. In one-stage object detectors, anchors are defined densely across the grid; thus, any valid GT label should intersect with at least one anchor if there a recognizable feature. Similarly, two-stage detectors, the region proposal network generates region proposals around potential object locations, so any valid GT label should intersect with least one proposal region if relevant feature is present.
Missing GT: If a predicted bounding box b appears consistently despite augmentations ((1o0(6_A(6) > 7c) but has no corresponding GT label, a GT label is likely missing for that ob- ject. Here, μ is the mean loU between detections on the original and augmented images, and Yo is predefined consistency threshold.
Noisy GT:
Typically, each GT box is paired with a predicted box b based on the highest IoU, provided the loU exceeds the default threshold of 0.5. However, the IoU between the predicted and GT box falls below an upper threshold (IoU(6, 6) < 7)), but the predicted box shows consistency despite augmentations (ProU16,A(6) > Ti); then the predicted box is considered more accurate and replaces the GT box.
For each of the three categories above, we simulate real-world labeling inaccuracies and evaluate the robust- ness of the model and the effectiveness of our GLC approach under synthetic error conditions as follows.

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For false GT, random bounding boxes are added to simulate false detections. For each image, random boxes are generated, with width and height sampled from uniform distributions, 1 ~ U(T min 7h maxx and T ~ U'T 7hh max)) placed non-overlapping positions relative to the existing GT boxes.
For missing GT, a percentage PMer of GT bounding boxes are randomly removed to simulate missing labels. The removed boxes are selected uniformly across Dabbledd-
Finally, for noisy GT, bounding boxes are randomly perturbed in their dimensions and position. For a GT box b = [b", b,,b"* and A are sampled from { {-c,, c.). The center of the box is also shifted: D" b + Ab and ba B" + where A&" = =
6", 6,,6""]] the width and height are altered by b" = 6" + A6 and 6t = -hh + Ab", where A
Aw and A6" = AA
3.3.2 Pseudo-Labe Selection (PLS)
As outlined in Section ! regardless of the choice or sophistication of the threshold 6, which regulates the precision-recall trade-off in pseudo-labeling, pseudo-labeled images inherently contain missing detections. By estimating the missing detection rate (MDR) for each image, PLS identifies which pseudo-labeled images to remove after initial filtcring with 65.
We hypothesize that the distribution of confidence scores within an image indicator of missing detections. Specifically, the number of detections per image at different thresholds 6, E [0, 1] reflects the reliability of the detector on that image, correlating with the potential of missing detections. Given a confidence score threshold 6, the number of detections al that threshold is denoted (,,))). We define the score metric for an image 24 as
S, (d,)))
(a))
where (a)a) represents the number of detections at a low reference threshold (e.g., a = 0.1), providing a baseline for the total number of detections an image. The value of S increases as the potential of missing detections decreases.
To accommodate class imbalance, recognize that pseudo-labeled images with high MDR may still contain valuable learning signals if they include rare objects. To address this, we introduce regularizing class metric C₁ which considers class rarity. To keep C₁ in the range [0. 1]. it is calculated as
C. C= c-
Σ (
where and n, are the number of classes and predictions in an image I,, respectively, and fa is the frequency of class & in D pseuud- The value of C; increasos the rarity of the predicted objects increases.
The overall metric D, for an image proposed by our PLS method is then given by
(3) where 3 weighting factor balancing the contributions of missing detections and class rarity. The value of D2 increases as the potential of missing detections decreases and class rarity increases. Therefore, images with low D; scores are filtered out to improve the quality of pscudo-labels used during student training. Eq. 3 ofters a computationally efficient solution for pseudo-labeled images selection as it relies solely on the pseudo-labels.
D.(6.. = (1 (6.)) + B C..
4 Experiments
4.1 Implementation Details
To demonstrate the effectiveness of our data-centric mothods, we select EfficientDet-DO (Tan al.] 2020:
Google 2020) pre-trained on MS COCO (Lin et al.] 2014) as our baseline detector. Experiments are con- ducted two autonomous driving datasets: KITTI (Geiger al.] 2012 (7 classes, 20% random split for

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validation) and BDD100K (Yu et al. 2020) (10 classes, 12.5% official split). Each training comprises 200 epochs with 8 batches and an input image resolution of 1024x512 pixels. All other hyperparameters of DfficientDet are set to their default values (Tan et al. 2020
Our methods are also framework-agmosttcc As an exemplary student-teacher framework, this work adopts STAC (Sohn et al. (020)) with A = Eq. ① due to simplicity. For data augmentations (A), we use RandAugment (Cubuk et al. 2020). Using AutoAugment (Cubuk et al. 2019) instead yields identical performance. We continue to denote the teacher model MT and the student model Mg(6.), which depends on the score threshold 8z. We observe minimal inter-run variance and therefore average over total of three training runs. We evaluate cach proposed method individually to isolate its specific impact on SSOD performance.
4.2 SSOD Framework
Impact of 8g and Amount of Labeled Data: We begin experiments by examining key fac- tors that influence the effectiveness of SSOD frameworks. These include the choice of confidence score threshold (6.) and the ratio of labeled to unlabeled data. Fig. 2 shows a strong correlation between the choice of 6, and the amount of labeled data used during training. As the quantity of labeled data increases, the pseudo-labels generated by the teacher model become more reliable, enabling the use of a higher 6.. While a lower threshold (6, = 0.4) optimal for setups with 15% labeled data KITTI and 10% in BDD, higher threshold (ō. 0.7) outperforms the latter 25% labeled data.
However, misconfiguring 82, such as selecting 0.9 for 10% of BDD labeled, results in a high MDR since many pscudo-labels get filtered out. This leads to significant performance drop of 3%, suggesting that
10%
15%
25%
1% X 10%
25%
59.89
22.43
57.55
20.61
both the amount of labeled data and the choice
55.22
18.78
of 8g must be carefully calibrated (also relative to
52.88
16.96
each other) to achieve optimal performance. While
50.54
15.13
48.21
13.30
SSL improves the mean Average Precision (mAP) both datasets by up to 2%, ineffective threshold- ing can lead to its degradation for Mg by up to 3% compared to MT- The latter is most pronounced when labeled data significantly limited, and both labeled and geeudo-labeled data are noisy and un- balanced, as demonstrated by the consistent under- performance of Ms at 1% of BDD labeled. This can be attributed to poor-quality pseudo-labels gener- ated in such sparse setlings, as shown in Table
45.87
11.48
Mr
Ms(0.1)
Ms(0.4)
Ms(0.7)
Ms(0.9) .)
MT
Ms(0.1)
Ms(0.4)
Ms(0.7)
Ms(0.9) )
Figure 2: KITTI (left), BDD (right). Impact of the re- lationship between the confidence score threshold (6.) and the proportion of labeled data on performance. A higher proportion of labeled data allows an effec- tive increase in 6g. A misconfigured 6, relative to the available labeled data results in student (Mg) that underperforms its teacher (Mp).
To avoid misconfiguring 6,, we generally recommend based on our experiments to calculate the average score across matched detections with GT labels in the validation set to effectively select the minimum 8, for pseudo-labels. We select 8, = 0.4 as the default value since it aligns with the default setting of EfficientDet (Tan et al. 2020) and matches the average score (0.36 + 0.1) across correctly matched delections on the validation sets of both KITTI and BDD.
Impact of Pseudo-Label Quality: We contimee our experiments the SSOD framework by investi- gating the quality of pseudo-labels and its impact performance. We identify three primary factors that affect it: noisy (Nd), false (FD), and missing detections (Mp). As shown in Table [ MT achieves an mAP of 47.16% on KITTI and 14.43% on BDD. The default Mg(0.4) shows an increase of 1.29% on KITTI but a decrease of 0.97% on BDD. This poor performance on BDD is a result of poorer pseudo-label quality, as demonstrated by metrics such as classification accuracy (mACC), mcan Intersoction over Union (mIoU), MDR, and uncategorized detection rate (UDR). MDR represents instances that are not detected, while UDR refers to detections that may be correct but lack a corresponding GT label or entirely false.
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Table 1: KITTI (top, 10% labeled), BDD (bottom, 1% labeled). Impact of pseudo-label quality on perfor- mance, with NDs, FDS, and Mps removed (x) from the pscudo-labels. dash (-) indicates non-applicability
ND
FD
MD
mAP
MDR
UDR
mACC
mloU (%)
m +1
(%)
(96
(%6
(%)
MT
47.16
3069
598
Mg(0.4)
48.45
23.26
7.10
97.24
85.92
23453
5866
Mg(0.4)
X
52.21
29.54
6.66
99.30
95.97
23453
5866
Mg(0.4)
x
48.95
23.26
0.00
97.24
85.92
21789
5861
Mg(0.4)
x
47.47
00.00
4.92
97.83
87.40
6540
2668
MT
14.43
12365
(g(0.4)
13.46
64.03
14.63
96.36
81.15
528818
69021
Mg(0.4)
x
13.59
64.03
14.63
98.83
90.30
528818
69021
(0.4.
x
13.87
64.03
00.00
96.36
81.23
450202
69021
The quality of psoudo-labels has a more significant impact student model performance than their quantity. Despite the number of detections per image (n,) being approximately 23 times higher in BDD compared to KITTI, the quality of pseudo-labels remains crucial. The 12 times larger number of unlabeled images (1) in BDD at a comparable number of labeled images (m, 598 for KITTI vs. 698 for BDD) could not compensate for lower pseudo-label quality.
Further analysis shows that the influence of ND, FD, and MD in pseudo-labeled data is closely tied to the quality of the GT labels. On KITTI, which features higher-quality images and labels, removing Nps leads to approximate 4% increase in mAP. However, excluding images with Mps lowers performance by reducing the total number of images l in Dunlabeled and detections n,, This reduction compromises the reliability of (pseudo and disrupts the balance between flabeled and ppeedo. Since both losses are weighted equally in the total loss (Eq. [], the negative impact of the low quality of the pseudo-labels is further amplified by their limited quantity. Pseudo-label quality generally has a more significant impact than quantity, but quantity becomes crucial when GT label quality high. Furthermore, some pscudo-labeled images remain valuable even if they contain Mps, particularly when they contain rare objects as demonstrated in Section 4.4 Mcan- while, removing FDS from BDD boosts performance more than removing Nps, underscoring the challenges of noisy datasets, In BDD, no images exist without Mps, thereby preventing an analysis of the impact of their complete absence.
These observations emphasize the need to maintain a careful balance between noise, precision, and recall in pseudo-label. based on the characteristics of Diabeled- We also test advanced pseudo-label selection strategies, such as uncertainty-basee thresholding with class-specific weighting, but observe no direct gains in mAP over using 8g for filtering. This is primarily due to the rarity of some classes in the labeled set, which adversely impacts the performance of MT on them, hence propagating errors to Ms- As result, for SSL to be effective in object detection, class-specific approach is necessary, motivating the development of our Rare Class Collage (RCC) and Rare Class Focus (RCF) methods.
Summary: our experiments reveal that optimal SSOD performance depends on coreful calibration of the confidence threshold 8, with the amount of labeled data. Furthermore, pseudo-label qualily is generally more influential than quantäly, unless Diabeled is of high quality and MT well-trained. KITTI benefits from noise reduction in pseudo-labels, while BDD sees improvement with reduced false detections, underscoring the importance of datasct-specifie adjustments.
4.3 Class Amplifiers
Due to limited labeled data in SSL, class imbalance significantly impacts model performance. In addition to our proposed RCC and RCF methods, we evaluate two baselines that address this: classical image-level re-sampling and re-weighting, as detailed in Section 2
Imbalance Strength: Class imbalance is evident in Fig. showing the frequency fir of classes in KITTI and BDD. The imbalance spans 260-fold difference between the most and least frequent classes. In RCC,
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2239
6769
2426
1807
20
38 Tram
79 Truck
134
210
349
26 33 66 133
289
815
Person Sitting
Cyclist
Van
Pedestrian
Car
Motorcycle
Rider
Bicycle
Bus
Truck
Pedestrian
Traffic Light
Traffic Sign
Car
Figure 3: KITTI (left, 10% labeled), BDD (right, 1% labeled). Class frequency Sk for each class in Diabelod-
we larget the rare classes "person sitting" and "tram" for KITTI, and "rider", "motorcycle", and "bicycle" for BDD to boost their representation. An example collage is visualized in Fig. | for each dataset. Unlike RCC, RCF does not explicitly target specific classes but instead stratifies Diabelod by using a class score that accounts for the frequency all classes, as defined in Eq.
Figure 4: KITTI (left), BDD (right). Example collages with scaling factors Tr 0.25 and Trmaax 0.75.
Figure KITTI (top), BDD (bottom). Batch structuring via RCF with images categorized common (left) predominantly containing cars and images categorized rare (right) including the rare classes "tram". "truck", "pedestrian" and "person sitting".
Overall Performance of RCC and RCF: Table 2 highlights the importance of a per-class per- spective when analyzing SSOD performance. While both RCC and RCF improve MT perfor- mance, only RCF consistently benefits Mg. RCC targets specific classes, resulting in a limited number of collage images (18 for KITTI and 43 for BDD) based on our choice of classes. The impact of the additional images becomes negligible during the training of Mg due to the thou- sands of additional pseudo-labeled images. Furthermore, the class-specific improvements from RCC have a less significant impact on mAP, as mAP averages performance across all classes by default.
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Nevertheless, the impact of class balancing through our methods is clear: using RCC for targeted collag- ing and RCF for structured batch training, the mAP of Mp is improved by up to 1% from each approach with no additional manual labeling or changes to the core training process. An example of the batch structuring via RCF is visualized in Fig. 5 Fig. G demonstrates that RCC effectively increases the fre-
Table 2: KITTI (left, 10% labeled), BDD (right, 1% labelod). Impact of RCF and RCC on mAP.
RCF RCC
mAP
MT MT
47.16 + 0.43
14.43 + 0.18
v
48.11 + 0.15
14.65 1 0.16
MT
47.49 + 0.29
15.00 + 0.06
Mg(0.4)
48.45 + 0.18
13.76 + 0.04
queney of targeted rare classes by approximately 300% while minimally affecting the distribution of common classes (up to 3% increase). In contrast, re-sampling introduces a greater bias toward com- mon classes (up to 10% increase) while achicving a comparatively smaller boost in the representation of rarc classes (only 200%).
Mg(0.4)
/
49.56 + 0.13
14.27 + 0.05
(0(0.44
48.41 + 0.11
14.26 + 0.15
Per-Class Performance of RCC and RCF: We
investigate both methods from a per-class perspec- compares the performance of RCC and RCF against re- re-weighting (RW) and re-sampling en- tire images (RS). For RW, we leverage F; in Eq. (2) to weight images in Diabeled as described in Sec-
Original
2239 2312
tive. Table 3
Re-sumple
RCC
349385360 385360
349
798779 798779134146 1341461.344 2102192100
204060 387696
tion 2 We use the mean weight per image since al- ternative approaches, such as per-detection weight- ing and using sum or max values per image, were less effective. The higher mAP of RCF observed in Ta- ble is further supported by consistent performance gains across individual classes. However, while RCF requires changes to the data loader, RCC remains more straightforward, data-centric approach that consistently outperforms RS and RW on targeted rare classes.
Person Sitting
Tram
Truck
Cyclist
Van
Pedestrian
Car
Figure 6: KITTI. Class frequency fk after applying
RCC vs. re-sampling of entire images. RCC increases fix for targeted classes ("person sitting" and "tram") without over-representing common classes.
The impact on other classes varies across datasets. On KITTI, RCC reduces the AP on common classes by up to 1%, while on BDD it consistently improves it by up to 1% across all classes (see Table This variation can be attributed to the strong model performance on KITTI, where additional collago- based augmentation may introduce unnecessary variability. This is also evident. the total sum (Σ) across all classes, where RCC achieves an additional 1% improvement total AP on BDD compared to KITTI. On the other hand, RCF enhances performance across all classes as maintains the original object resolution. RW performs best KITTI (total increase of 10.53% in AP across all classes) but performs worse BDD (decrease of 4.49%), particularly struggling with common classes. This is due to its disregard for label noise when weighting samples, and BDD containing higher noise levels compared to KITTI. Challenging samples are assigned high weights, hindering learning on easier and more common samples. Meanwhile, RS marginally improves the AP on rare classes by up to 1% at the cost of degrading it on common classes by up to 1%. This decline occurs bocause RS introduces challenging images that hinder the effective optimization of Eq. D.. It is worth mentioning that we test re-sampling both with and without augmenting the images using RandAugment iCubuk et al. 2020). Given the consistent underperformance of RS compared to other methods in Table we exclude it from the analysis in Table 4 Furthermore, the "train" class is excluded from the analysis on BDD because there is only one instance of a train in the labeled set at 1% and it is occluded, resulting in an AP of 0% for that class irrespective of the blass-balancing approach used.
Both tables (Table 3 for MT and Table 4 for Mg(0.4)) show similar trends with RCF yielding the highest improvements for rare classes by up to 5% AP without significantly reducing performance on common classes (less than 1% AP). This indicates that addressing class imbalance in Diabeled is beneficial, regardless of whether the training includes psseudo- However, the impact is diminished for Ms(0.4) (9.15% vs 3.16% total increase in AP across all classes), as the contribution of llabeled to the overall training is reduced. The limited improvement with RCC on the AP of the student (up to 2% vs. 5% for the teacher) can be
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Table 3: KITTT (top, 10% labeled); BDD (bottom, 1% labeled). Impact of RS, RW, RCF (yf = 20), and RCC (Tramin = 0.25, Tr.max = 0.75) on per-class AP of MT and the total sum across all classes (E). Green arrows (+) indicate performance increase, while red arrows (+) denote a decrease compared the mean AP of the original MT across the multiple runs (shown in the first row of each dataset). The values adjacent to the arrows are the absolute differences to the mean APs. Colored columns highlight the classes targeted by RS and RCC. Classes are ordered left to right from rarest to most common.
AP
RS
RW
RCF
RCC
Person Sitting
Pedest-
Tram
Truck
Cyclist
Van
Car
Σ
rian
22.89
43.64
60.14
45.03
51.70
36.80
65.99
11.30
0.95
0.88
+0.42
4000
0.55
00.0
10.33
2.72
2.66
1.08
0.22
0.776
10.32
0.33
10.53
3.18
4.32
1.003
10.17
0.25
0.81
0.11
9.15
4.92
1.59
v1055
00444
0.99
0.22
0.30
3.50
Motor- cycle
Pedest-
Traffic
Traffic
Rider
Bicycle
Bus
Truck
Car
rian
Light
Sign
4.59
6.66
9.59
24.72
23.02
17.07
7.83
14.71
35.62
0.99
22002
0.81
00.0
10.41
+0.0
+0.00
00.2
0.21
2
0.78
0099
1.27
2.118
0..5
0.888
10.06
0.28
0.09
4.49
1.00
1166
0.05
0.44
0.52
0.26
0.19
0.229
0.20
3.16
1.90
1.88
0.29
00.71
00.24
40.01
40.09
0.00
0.00
4.554
attributed to the small number (18 for KITTI) of generated collage images relative to the large pool of added pseudo-labeled images (5866 for KITTI). Nonetheless, RCC still improves performance on targeted rare classes. Meanwhile, RW significantly reduces performance, particularly for rare classes, with drops of up to 3% in AP. Our findings support our hypothesis regarding error propagation: Mg(0.4) underperforıs MT on certain inadequately--earned rare classes such as "motorcycle" in BDD, yet improves performance well-learned classes such as "car" and "traffic light". Both RCF and RCC effectively mitigate this issuc, consistently improving performance rare classes across both datasets while minimizing the impact on common classes.
Table 4: KITTI (top, 10% labeled), BDD (bottom, 1% labeled). Impact of RW, RCF (1. = 20), and RCC (Tr.min = 0.25, Tr,max 0.75) on per-class AP of Mg.0.4).
AP
RW
RCF
RCC
Person Sitting
Pedest- rian
Tram
Truck
Cycllst
Van
Car
Σ
25.05
47.60
61.21
47.36
51.60
37.45
67.60
2.50
2.55
4066
1.21
0.55
0.93
1.45
3.72
3.26
5.03
2.13
0..4
40.01
1.31
0.09
11.29
1.05
0.05
1.21
0.42
0.62
0.37
0.34
1.20
Motor- cycle
Pedest-
Traffic
Traffic
Rider
Bieycle
Bus
Truck
Car
rian
Light
Sign
4.13
6.62
7.48
23.89
22.78
13.63
9.14
15.37
36,55
0.77
00.32
1.64
2.40
1.90
0.88
0.22
+0.20
0.56
8.88
1.48
00.48
0..73
00.02
0..87
22.17
0.62
00.02
0.24
4..82
0.91
1.24
0.40
0.25
40.36
00.71
0.10
40.25
00.07
3.559
Summary: class imbalance strongly impacts SSOD performance, particularly with limited labeled data, and is effectively addressed by our RCC and RCF methods. RCC largels specific rare classes in KITTI and BDD, while RCF achieves balanced representation across all classes during training. Therefore, if performance on common classes less critical, RCC more beneficial. However, if maintaining performance on common classes a priority, RCF is the better choice. Both methods consistently outperform traditional re-sampling and re- te-weighting, underscoring the critical role of per-class analysis when evaluating SSOD.
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4.4 Label Filters
First, investigate the impact of the quality of labeled data performance and the effectiveness of our GLC method. Second, we evaluate our proposed metric D; and the advantages of selecting pseudo-labeled images with our PLS method post-filtering via 6,.
GLC. We first evaluate the impact of missing (Mer), false (Fer) and noisy (NGr) GT labels on the performance of MT- Table 5 presents the num- ber of discovered errors in KITTI and BDD using GLC. Given their limited number in both datasets (up to 3% of all detections), GLC only slightly im- proves the mAP by around 0.5%. Examples of de- tected GT errors and their correction are visualized in Fig. 7 for both datasets.
Table 5: KITTI (left, 10% and 15% labeled), BDD
(right, 1% and 10% labeled). Total number of detec- tions
> ny in Diabeled and identified GT errors.
%
1
Fer
Mer
%
>
Fer
Mer
10
3069
14
20
1
4661
20
20
12365
380
123
126726
2772
1608
Por
GT
Mevr
Figure KITTI (left), BDD (right). Examples of GT label errors identified and corrected via our GLC approach. Both human annotators and model-assisted labeling tools often fail to label distant or occluded objects. Moreover, they frequently make errors, such drawing tiny, incorrect bounding boxes scattered across the images or misplacing boxes near the image edges.
Impact of GT Quality: To further understand the impact of GT quality on model performance, we introduce two levels of synthetic GT errors as depicted in Fig. g This allows for a controlled analysis of label errors, highlighting the sensitivity the model to the quality of the labeled data and emphasizing the advantages of our proposed GLC method. The two levels include:
Man
Original GT Modified
Naas
Cyclnt
Level
Podestran
Pedestrae
Cis
Pedestrian
Pedestrian
Van
Level 2
yyli
Pedesbiian
Redestran
Polleeaa
NGT
MGT
F&T
Figure 8: KITTI. Visualization of two levels of synthetic GT errors: Level (+) and Level 2 (++).
Level 1 (+): PMeT = 20% dropped GT boxes, added one mistake per image (15- min = 10 pixels and T8 maxx 100 pixcls), and noise applicd 20% of images with a perturbation factor 6, 0.1.
Level (++): PMer 50% dropped GT boxes, added five mistakes per image (% mii 10 pixels and 78" 100 pixels), and noise applied to 20% of images with perturbation factor < 0.2.
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Table 6: KITTI (top; left 10,, right 15% labeled), BDD (bottom, left 1,, right 10% labeled). Impact of synthetic errors on mAP of MT without (x) and with GLC (V). The errors include noisy (NeT), false (FeT), and missing (MoT) GT labels, evaluated at two intensily levels: Level (+) and Level 2 (++).
mAP
NeT
Fer
MGr
47 16 + 0.43
51.61
+ 0.21
39.38 + 0.21
45.65 + 0.32
45.86 + 0.13
49.81 + 0.20
34.99 + 0.73
43.08 + 0.37
37.62 + 0.40
46.77 + 0.89
41.64 + 0.44
16.76 + 0.58
47.28 + 0.96
51.54 + 0.56
+
33.32 + 0.77
16.56 + 0.33
37.51 + 0.48
51.21 + 0.26
41.91 + 1.02
43.88
0.02
46.19 + 0.42
48.63
0.15
29.43 + 0.18
39.88 + 0.83
32.75 + 0.93
42.89 + 0.39
14.43 + 0.18
18.73
+ 0.17
11.78 1 0.61
13.57
0.01
15.85 1 0.02
17.05 0.83
11.32 + 0.18
13.54 1: 0.02
15.06 1 0.34
16.99 1 0.71
14.17 + 0.07
14.12 + 0.24
18.25 + 0.01
18.88 + 0.04
13.02 + 0.08
14.17 + 0.02
17.25 + 0.13
18.81 + 0.17
12.68 + 0.22
13.39 0.21
16.65 + 0.20
17.21 0.11
09.50 + 0.23
12.02 + 0.03
13.93 + 0.35
15.54 + 0.02
The performance impact of the selected error levels on MT is presented in Table All error types substan- tially reduce the mAP, with reduction of up to 18%. McTs have the most detrimental effect across all tested data regimes and datasets. As for FGTS, even introducing a single error per image results in up to a 6% drop in mAP, demonstrating the high sensitivity of the model to GT errors. This model vulnerability remains consistent irrespective of the data regime or dataset characteristics. For instance, despite KITTI having four times fewer detections (3069 in 598 images) compared to BDD (12365 in 698 images, see Table D the performance reduction comparable at both levels. The mAP drops relatively to its original value by 17% and 38% on KITTI and 18% and 34% on BDD at Levels and 2, respectively.
Effectiveness of GLC: GLC mitigates the impact of GT label errors by correcting them before training Ms or for retraining Mp. Correcting FGTS is found to be more straightforward than recovering Mers due to overlapping GT labels, as GLC assumes for the latter no overlap between consistent detections and GT labels. As for NGTS, GLC restores significant performance, raising mIoU from 83.98% to 90.97% and mAP from 39.38% to 45.65% on KITTI (10% labeled) after correction at Level 1.
PLS. Given the importance of addressing MGTs and the abundance of Mps in D pseudo of real-world datasets such BDD, introduce PLS. The mloU of MT trained on 10% of KITTI is around 88% for
True Positive Rate
1.0
C, 0.50
S,, 0.90
common classes such as "car", but drops to 76% for
0.5
D((0100,, 0.89
n, 0.66
rare classes such as "person sitting". Similarly, the
D((0255), 0.79
Ms. 0.74
mACC 100% for common classes vs. 96% for rare
D.(0.50), 0.68
Random
0.0
ones. This disparity motivates the considcration of the class distribution and the estimated MDR when selecting aseudo-labeled images with PLS.
0.0
0.5
1.0
False Positive Rate
Figure 9: KITTL ROC curve for identifying images with more than 50% MDS (6, 0.9). Our metrics St and D, outperform baseline metrics such as the aver- age score per image (H,) and the number of detections per image (n:) (see AUC values).
Evaluating D,: Fig. g demonstrates the effective- ness of our proposed metric S; in identifying im- ages with high Mps, achieving an AUC of 90% on KITTI and 91% on BDD, significantly outperform- ing the average score per image (Hs) at 74%, and the number of detections per image (n.) at 66%.
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As expected, class distribution does not correlate with the MDR (50% AUC). Still, it is beneficial when incorporated into our metric Di, as it allows for the inclusion of images containing rare classes without com- promising effectiveness in recognizing Mps. This is evidenced by 10.0.0 and (0.2.)) still outperforming Hs and n; despite the increase of B. The results are consistent across both KITTI and BDD, underscoring the robustness of our approach.
Effectiveness of PLS: Fig. 10 qualitatively compares the lowest and highest scoring images based on S. The contrast between these examples provides insights into the effectiveness of PLS in discriminating between high-quality and low-quality pseudo-labeless images, reinforcing its reliability in pseudo-label selection.
MD
Figure 10: KITTI (left), BDD (right). For cach datasot, the left image represents the highest S,, while the right image represents the lowest St (poor detection quality). Predictions (in green) are filtered at 6, 0.9.
Fig. summarizes the PLS results. At 1% of BDD labeled and 6, 0.4, compare mAP, MDR, and CDR across different configurations (3 = 0.1 and 0.2). We demonstrate that PLS consistently outper- forms random selection in mAP and MDR/UDR, confirming that PLS effectively identifies high-quality pscudo-labeled images. PLS improves the performance of Mg(0.4) to match or even surpass the perfor- mance of MT (see Table T by up to 0.5% mAP. Our method effectively filters out images with high MDR and UDR (around 80% and 16% in the removed set, compared to 60% and 14.5% the remain- ing pseudo-labeled images), indicating correlation between our metric and both the MDR and UDR.
Moreover, the model trained on the remaining 50% of papeddo post-selection using our D; metric outper- forms the original student trained on the full D Ppeeudo and the students trained on a random 50% subset or the removed 50%. Incrcasing 8 removes fewer rare classes, further underscoring the importance of class- balanced perspective in SSL.
Y
Original
Remaining
Random
Removed
14.0
16
13.5
MDR (%)
70
UDR (9))
13.0
60
0.1
12.5
0.2
Summary: GT and pseudo-label quality significantly impacts model performance. Synthelic errors re- veal high model sensitivity to GT label quality, with even minor errors causing notable performance de- clines. Our GLC method effectively identifies and corrects GT label errors, while. PLS filters low- quality pseudo-labeled images. By preventing error propagation and leveraging the learned knowledge of MT. GLC and PLS improve the effectiveness of SSOD and therefore Ms performance.
20
30
40 50 60
20 30 40 50 60
20 30 40 50 60
Data (%)
Duta (%)
Data (%)
Figure 11: BDD (1% labeled). PLS results for 6, = 0.4 and B 0.1 and 0.2. Original student model vs. a student trained on: the remaining data post-selection using D,, randomly selected data of equal size as the remaining data, and the removed data. Data (%) rep- resents the percentage of removed data.
4.5 Combined Building Blocks
We evaluate the offectiveness of our proposed building blocks in addressing the key challenges of SSOD: class imbalance, erroneous GT labels, and low-quality pseudo-labeled images. To this end, we analyze their combined impact on both the teacher model Mr and the student model Mg (0.4). The per-class performance results are summarized in Table 7 GLC significantly improves the AP of Mt trained with NGTS at Level by up to 12% in total, demonstrating its effectiveness in correcting noisy GT labels. Balancing the 1% labeled data of BDD via RCF or RCC further increases the AP by up to 8% in total, highlighting the
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importance of addressing class imbalance. However, directly combining RCF and RCC leads to suboptimal class balancing, as all collages are designated as rare. As a result, they appear in every training batch, and their augmented nature results in unintended biases. To address this, collages should not influence RCF- based batch structuring and should instead be categorized as common, allowing them to be shuffled with the majority of the dataset.
We select RCC as the representative claan-ng method and evaluate its interaction with GLC and PLS. Removing 20% of pseudo-labeled images via D, with PLS leads to a total AP increase of up to 7%. Increasing the removal percentage to 60% results in only 0.2% drop in mAP compared to removing only 20%, suggesting that the retained high-quality pscudo-labeled images provide the key supervisory signal for learning. The combination of PLS with RCC/RCF and GLC achieves the best results, leading to a total AP boost of up to 21%. This effectively mitigates the confirmation bias and overfitting issues observed in Mg(0..4, where it otherwise underperforms MT on rare classes (2.34% drop for "motorcycle") while outperforming it on common classes (2.08% increase for "car"). By integrating our proposed building blocks, Ms(0.4) transitions from underperforming MT by a total of 4.97% in AP to surpassing it with total AP increase of 15.98%.
Table 7: BDD (1% labeled) with NGTS Level 1. Impact of GLC, RCF (75 20), RCC (7rmmn 0.25, Yr,max 0.75), and PLS (3- 0.1, 20% removed) on per-class AP of MT (top) and Mg (0.4) (bottom).
AP
GLC
RCF
ROC
PLS
Motor- eycle
Rider
Bleycle
Bus
Truck
Podest-
Traffic Light
Traffic
Car
Σ
rian
Sign
2.02
6.16
8.12
21.67
18.85
15.01
7.07
12.49
31.23
2..55
2/00
1.19
22.13
43.00
40.99
40.25
11.1
43.08
12222
4.338
2200
1..07
42.65
43.79
11.35
0.223
1.337
43.16
200.04
4-.13
4-2.5
11.74
22.60
42.63
+1.04
+0.02
11.29
43.33
118.99
v
v
42885
1.78
1.00
1.91
11.0
10.73
00.27
0..33
42.74
48.81
2.82
3.82
6.43
20.86
17.78
11.48
7.76
13.39
33.31
10.02
1.87
0.551
0.55
55.00
1..0
10.19
1.33
2.229
111.2
1.85
4..70
00.98
1.559
3.55
1.62
00,29
+0.13
2,007
13.17
22249
2-240
1.88
3..04
4445
22.23
00.55
1.44
42,47
200.55
Summary:
The combination of GLC, RCC/RCF, and PLS results in cumulative AP boost of up to 21%
in total across all classes, highlighting the necessity of a structured and dataset-aware combination of class balancing, label correction, and pseudo-label filtering in SSOD.
4.6 Ablation Studies
The following ablation studies present empirical validations for our parameter selection and the robustness of our methods, alongside a comparison between the selected SSOD framework STAC and other SSL and active learning (AL) frameworks.
SSOD Framework. We compare the basic student--eacher SSL framework to consistency- SSL, active learning (AL), and uncertainty-based pseudo-label filtering methods. For consistency-based SSL, we re-implement CSD (Jeong et al. 2019) for DfficientDet. For uncertainty-bassed methods, we employ Loss Attenuation [Kendall & Gal 2017 Kassem Sbeyti et al.] 2023) and 2D spatial Monte Carlo (MC) dropout (Tompson et al. 2015) with a dropout rate of 0.05 selected based on best performance and 10 MC samples.
AL inherently addresses class inbalance by selecting uncertain, often underrepresented samples. For instance, it increases "person sitting" from 51 to 164 samples in Djabeled at the first iteration (5% start and increase to 10%) compared to random 10% sampling. The increased representation results in an mAP boost of 5% for MT and 6% for M0.0.4) over random sampling on KITTI, despite similar pseudo-label quality ( 80% MDR, 4% UDR). This underscores the potential of combining AL with SSL and the importance of class-aware analyses and methods.
We observe consistent increase in mAP by 2% via SSL regardless of the filtering threshold and uncertainty metric used, including entropy (Roy et al. 2018). combined uncertainty (Kassem Sheyti et al. 2024 and
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epistemic uncertainty. This validates our focus on psendo-label quality post-filtering and class-balancing over uncertainty-based filtering alone.
Additionally, CSD [Jeong et al. 2019 shows no improvement over MT at 10% on KITTI or BDD, suggesting that augmentations and consistency strategies that work on MS COCO (Lin et al. 2014) and PASCAL VOC [Everingham et al. 2010) do not necessarily generalize to real-world datasets with different challenges and characteristics.
Detector Choice. To evaluate the impact of detector choice on our methods, employ the TensorFlow implementation of YOLOv3 (Farhadi & Redmon 2018 Zhang 2019 as an alternative to Efficient We fine-tune a YOLOv3 model pretrained on MS COCO using 10% KITTI for 50 epochs with an input resolution of 1024x 1024, a batch size of 8, a learning rate of 0.0001, and anchors optimized via k-means clustering. All other hyperparameters remain at their defanlt values. As shown in Table D RCC improves the performance of MT the most, increasing mAP by up 3% and resulting in a total increase in AP by up to 19% across all classes. RCF improves the AP by up to 11% in total with batch structuring only (no augmentation or RW), consistent with its contribution to the performance of ElficienDet-based MT (see Table Since our methods are data-centric, they generalize across different detectors. Unlike Efficient YOLOv3 struggles on KITTI (achieving an mAP of only 6%). However, class balancing through RCC and RCF enhances performance across all classes, demonstrating the broad applicability of our methods.
Table & KITTI (10% labeled). Impact of RCF (75 =20,, and RCC (7r,min 0.25, Tr,aax 0.75) on per-class AP of YOLOV3- based MT.
AP
RCF
RCC
Person Sitting
Pedest- rian
Σ
Tram
Truck
Cyclist
Van
Car
1.09
2.47
3.15
2.05
2.76
6.98
27.51
2.44
1.89
00.02
40.18
1.55
10.49
44.44
111.00
0..9
43.03
10.03
11.83
1.21
22.67
10.10
18.87
RCC. Our ablation studies on RCC focus on the configuration of the collage (horizontal allocation sctup vs. 4x4 grid setup), scale variation (SV), augmentation using RandAugment (Cubuk al., 2020), and the cropping parameters Tr.min and maxx A horizontal allocation visualized in Fig, outterrorrs grid setup as illustrated in Fig. 12 (left). The grid setup only improves the AP by 1.5% vs. 1.9% for "motorcycle", though it decreases it by 1.3% for "person sitting" and 0.2% for "rider" and "bicycle". Furthermore, incorporating different scales via SV (see Fig. 12 (right)) confuses the model, as the upscaling from cropping already introduces sufficient variation. SV leads to a smaller increase in AP for rare classes, e.g., "person sitting" (only 1.6% vs. 4.9%) and "motorcycle" (0.9% vs. 1.9%). Adding augmentations to the collages via RandAugment Cubuk et al. 2020) also results in smaller increase in AP, e.g., only 2% vs. 4.9% for "person sitting".
Figure 12: KITTI (left., 4x4 grid setup with Tr.min 0.5 and Tr,max 1.0), BDD (right, scale variation with Tramin 0.25 and Tr.max 0.75). Example collages.
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We demonstrate in Table the robustness of our RCC method under different cropping parameters. RCC remains effective across different configurations of mr,min and Tr,max; with the combination of Tr,min = 0.25
and 7r,maxx = 0.75 yielding the highest performance on rare classes in both KITTI and BDD (up to 5%). Importantly, RCC consistently increases the performance of targeted classes without significantly impacting other classes (total increase of up to 7% across all classes (E)). While Tr.max = 1.0 results the highest total improvements on KITTI due to a smaller drop in the AP of common classes, this comes at the cost of smaller gain in AP for rare classes. A higher 7r,max avoids bruncating surrounding objects, maintaining performance the common classes of the cleaner KITTI dataset, in contrst to BDD. A limitation of RCC is visible on the class "bicycle", as it does not increase the performance due to labeling inconsistencies in BDD. As shown in Table B and discussed in Section 4.4 the labeling of "bicycle" often includes the rider, which limits the improvement. potential of the model despite the use of RCC due to inter-class confusion. This underscores the limitations of post-labeling balancing strategies.
Table 9: KITTI (top, 10% labeled), BDD (bottom, 1% labeled). Impact of Tramin and Tr.max on AP of MT.
Teamin
7 max
Person Sitting
Tham
Truck
Cyelist
Van
Pedest-
Car
Σ
rian
22.89
43.64
60.14
45.03
51.70
36.80
65.99
0.1
0.5
4.099
42.61
2.07
+0.42
10.12
0.19
00,07
L
0.25
0.75
44.92
41.59
1.00
+0.44
+0.9
0.22
0.30
43.50
0.5
1.0
4.3.4
4-205
1.11
+0.02
00.13
40.40
40.00
46.83
1.0
1.5
43.46
33.63
00.6
00.42
00.7
00.0
00.1
5.41
Motor- eycle
Pedest-
Traffic
Traffic
Rider
Blcycle
Bus
Truck
Car
Light
Sign
4.59
6.66
9.59
24.72
23.02
17.07
7.83
14.71
35.62
0.25
0.75
1..90
11.88
+0.29
00.71
00.2
00.01
0..09
0.02
0.02
44.54
0.5
1.0
1.33
42.13
0.00
40.17
40.15
0.06
0.08
0.27
0.33
43.01
Table 10: KITTI (Lop. 10% labeled), BDD (bottom, 1% labeled). Impact of ys and A on AP of MT.
AP
RW
RCF
7
A
Person Sitting
Tram
Truck
Cyclist
Van
Podest-
Car
Σ
22.89
43.64
60.14
45.03
51.70
30.80
65.99
10
2,90
1.61
0.81
00001
1.33
00.45
00.48
00.14 5.93
20
22
2.66
1.00
00.26
0.76
40.2
0.31
10
22.81
1.29
2.35
0.22
00.1
0.29
0..5
1.41
20
1.33
4..14
+0.38
0..66
0.155
0.227
00.14
3.15
10
0.94
1.58
0.44
0.63
0.30
10.40
+0.02
2.41
20
22.56
00.3
0.88
+ 1.32
0.73
0.33
0.04
0.31
10
v
44.47
2.55
0.79
+ 1.14
10661
0.37
0.36
5.00
V
20
V
3,18
1.32
1..03
0.17
0.25
00881
0..1
9.15
30
<
6..14
1.27
00.3
1.02
+0.63
1.18
00.1
8.288
V
10
L.58
44.66
1-40
0.65
0.41
0,22
0.00
8.84
20
2.63
3.14
+0007
1.18
0.94
0.25
4..3
7.77
Motor-
Podest-
Traffic Light
Traffic
Rider
Bicycle
Dus
Truck
Car
cycle
Sign
4.59
6.66
9.59
24.72
23.02
17.07
7.83
14.71
35.62
10
1.13
0.73
1.44
1.68
0.441
0.52
0.04
0.23
0.00
4.31
01.78
0.91
1.27
2.18
0..54
10.88
0.06
0.28
+0.09
4.49
10
1.87
10.16
00.3
0.59
0.32
00.31
0.00
0.23
00.1
3.55
20
0..7
1.24
1.18
+ L.59
0.69
00.6
0.02
0.42
+0.02
3.26
10
0.95
1.20
+0.11
00.31
0.33
0.28
0.12
0.009
10.19
0.38
20
2.40
40.44
+0.67
0.38
0.28
10.12
00.09
00.07
+0.11
2.56
10
V
0..5
2.21
1.28
0.00
0..71
1..19
+0.01
00.31
+0.25
1.26
20
/
11.00
11.60
00.05
0..45
40.52
0,26
10.19
0.22
$0.20
3.166
r
10
<
1.00
1.40
0.85
0.72
00.1
0..4
40.09
0.32
+0.2
0.38
Y
20
V
0.77
0.88
1.82
1.77
0..4
1.19
0025
0..49
+/.23
1..88
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RFC. Our ablation studies on RFC examine the effect of different scaling strengths via Y for re-weighting (RW) and RCF, both with and without augmentation A, well as their combined use. Depending on the selected Yf. RW improves the AP by up to 2%, but also decreases it by up to 3% on certain classes. In contrast, RCF less dependent on Y as shown in Tables 10 and 11 with a consistent increase in performance almost. across all classes, and particularly on rare classes (up to 6% AP). Evaluating "y 5, 10, 20, 100 reveals that Y = yiclds the highest improvement on both datasets for MT- Y = is however sufficient for Mg(0.4),, as Ppscudo compensates for weaker scaling by inherently enhancing the representation of rare classes. Values of Y greater than 20 only increase the mAP by up to 0.5% (Yy 100 on KITTI with 15% labeled). Moreover, using logarithmic smoothing in Eq. (2) further stabilizes the improvements, with an additional 1% increase in mAP compared to linear weighting at 7y 10. The combined use of RW and ROF proves beneficial for Ms(0.4) but not Mr. as the additional loss term fpeedo in Eq. (1) reduces sensilivily to weight fluctuations and improves overall training stability. RandAugment (Cubuk et al., 2020) in RCF improves the AP by up to 3% due increasing the insufficient variability the datasct for rare classes.
Table 11: KITTI (top, 10% labeled), BDD (bottom, 1% labeled). Impact of ys and A on AP of Mg(0.4).
AP
RW
RCF
A
Person Sicting
Pedest- rian
Tram
Truck
Cyclist
Van
Car
Σ
25.05
47.60
61.21
47.36
51.60
37.45
67.6
10
0.66
2233
40.74
1.13
1..19
0.79
0.96
2.03
20
2.50
22.54
+0069
1.221
10.32
0.93
+ 1.45
3.72
10
1..5
43.71
+0.73
0..65
+0.26
00.63
0..13
3.98
20
0.55
43.35
+0.03
+1.03
22.57
00.49
00.1
6.93
1.98
43.24
00.71
0.06
40.36
1..77
00.20
3.99
11339
42.47
+0.09
0.33
1..4
11,00
0.13
66.25
5.15
6.12
3.33
0.02
1.70
1.34
0..0
17.58
43.26
55.0
22.13
0.31
+0001
1.31
0.00
11.29
30
3.00
2,96
10.17
0.81
1.00
1.69
0.00
8
10
1.56
44.16
41.79
0.66
22.42
11.06
0..32
10.04
20
0.02
44.53
40.75
0.33
22.30
00.12
0.21
7..79
Motor cycle 1.13
Pedest
Traflic Light
Traffie
Rider
Bicycle
Bus
Truck
Car
rian
Sign
5.62
7.48
23.89
22.78
13.03
9.14
15.37
36.55
00.61
1.01
0..3
0.880
1-.19
0.42
0.19
0.22
0.33
V1.79
0.75
00.32
1.66
1.90
0..8
0.22
0.20
0.56
88888
10
0.31
0.63
40.31
0.57
10.11
00.74
0-006
0.33
0.00
22.19
20
0.10
1..9
1.53
00.
1..87
00.1
00.10
0.11
0.21
4.36
10
0.79
1.56
11.13
00.31
1-.22
1..0
0..38
00.0
0..9
5.72
20
1.34
1.14
4-075
00551
4-063
11.42
+0.46
00.32
10.06
33.03
10
V
0.73
1.86
40.20
0.50
10.51
1.89
00.11
0.11
0.05
4420
20
1.43
40.48
40.73
0.00
00.87
22.17
0.66
0.02
0.24
4.82
1.77
41.7
1.04
1.01
40.25
1.77
0.02
0.14
0..9
55.51
0.77
22.3
+0.38
0..80
40,20
1.03
0.06
0.05
0.29
22.90
Original
Horizontal Flipping (HF)
Gaussian Blur (GB)
Gaussian Noise (GN)
Figure 13: Augmentations applied during inference on the training set of KITTI via GLC.
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GLC. Our ablation studies on GLC explore the impact of IoU threshold To for GT and prediction overlap,
Nv for consistency across augmented predictions, and choice of augmentation A (see Fig. 13 Gaussian blur is applied using a 9 x 9 kernel size, and Gaussian noise is drawn with a mean of 0 and variance of 0.5.
Table 12 presents the impact of each parameter on the effectiveness of our GLC method correcting syntheti- cally introduced NGTS at Level 1 to KITTI (10% labeled). The results indicate robustness to different choices of A and IoU thresholds, with consistent mAP recovery from added NGrs by up to 7%. This robustness can be at- tributed to the use of Gaussian Noise (GN), Horizontal Flipping (HF), or Gaussian Blur (GB) solely during infer- ence, introducing sufficient variability to evaluate model consistency effectively. GLC achioves strong performance (47.16 + 0.43 on original Diabeled; 39.38 E 0.21 post- corruption, and up to 46.47 = 0.56 (ost-GLC) even with single augmentation, thereby avoiding the additional computational cost incurred by multiple augmentations.
Table 12: Impact of A Gaussian Noise (GN), horizontal flipping (HF), Gaussian Blur (GB) and IoU thresholds To and To on the efficacy of GLC in correcting added NoTs at Level 1 to KITTI (10% labeled).
A
GN
HF
GB
%
%
mAP
90
90
46.47 0.56
90
90
45.99 = 0.72
90
90
45.11 = 0.27
90
90
45.65 = 0.32
90
45.41 0.53
A similar analysis for correcting Mors demonstrates consistent robustness across different %o values, yielding mAPs of 43.58 + 0.01 for Te 70, 44.08 + 0.06 for Ye = 80, and 43.88 + 0.02 for Te 90 (refer to Table 6
70
90
45.40 0.04
90
80
45.85 + 0.03
90
70
44.97 = 0.21
Furthermore, we also evaluate the importance of evaluat- ing model consistency, compared to simply using all detections with a confidence score above the default 6, = 0.4. Relying solely on ó₂ results in a reduction of up to 4% in mAP, achieving only an mAP of 41.32 + 0.17 post-correction of NGrs at Level 1 on 10% of KITTI.
Our experiments on NGTs focus on box perturbations, as we assume that noisy ground truth labels primarily arise from inaccuracies in box locations rather than misclassification of objects. Manual labeling often strug- gles with precise box placement but rarely misidentifies object categories. For instance, confusing pedestrian is less likely than an imprecise bounding box placement. This assumption is supported by the high mACC and lower mloU of the detector on both datasets (see Table I as well as by previous work demon-
with
strating that spatial inaccuracies in labeling can significantly degrade model performance (Grad et al. 2024 On KITTI, the detector encounters difficulties in crowded scenes. While the overlapping nature of objects in such scenes complicates boundary iden- tification, the primary issue arises from low-quality box labels, visualized in Fig. 14
Nevertheless, to investigate the effect of class label noise, we randomly mislabel 20% of the objects in KITTI (10% labeled), corresponding to level 1 NGT- The 20% noise result in a reduced label accuracy of 86.83%. Applying our consistency-base method, where the class labels are corrected using predictions that remain consistent across multiple inferences under data augmentations, increases la- bel accuracy from 86.83% to 90%. This results in an mAP improvement from 35.91 + 0.22 under noise to 42.55 + 0.14. Despite this correction, the approximately 4% remaining loss in class label accuracy demonstrates the sensitivity of the model to it, as the original labels yield a substantially higher mAP of 47.16% (see Table G)
correction
Figure 14: Examples of noisy box labels in KITTI. Hu- man annotators often fail to accurately label occluded and truncated objects, especially in crowded scenes.
Label noise, whether in class or box labels, appears to have a comparable impact on model performance. In the absence of significant label errors (see Section 4.4 and Table 5) the primary bottleneck in SSOD
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lies in class distribution and object availability. This highlights the critical importance of addressing class imbalance to achieve substantial improvements in object detectors.
PLS. Our first ablation study on PLS explores the correlation between MDS and our St metric at varying 6.. Increasing 6, results in higher proportion of MAS, with St showing proportional increase (see Fig. 15
thus validating the effectiveness of our metric. Next, we assess the impact of 3 in Eq. (3) on the class
distribution of removed pseudo-labeled images at 30% of KITTI labeled. Fig. 16 illustrates the shift in class
100. A higher (AD,CR) Sags
distribution for different values via the relative class count calculated as
Jts,
3 retains more rare classes, with the trade-off of a weaker correlation with Mps. Compared to B 0.1, 3 = 0.25 retains 95% more of the rare class "person sitting", while removing 70% more of the most common class "car". Despite these adjustments, the AUC only decreases by 10%, as shown in Fig. g highlighting the robustness of Eq. 3 Identical behavior is observed on BDD.
0.8
0.6
Class Count (%)
0
0.7
100
0.25
5
0.4
Mo
Relative
50
8 =
0.6
0
0.2
50
0.5
100
0.0
Car
Van
Truck
Pedestrian
Person Sitiig
Cyclist
Tram
0.7
0.9
5
Figure 15: Correlation between our metric S, and the proportion of miss- ing detections MDS per image for vary- ing values of 8, on KITTI.
Figure 16: Impact of 3 on the relative class count in the removed pseudo-labeled images at 30% of KITTI labeled with 6, = 0.4.
5
Conclusion
We conduct extensive analyses on label quality and its impact on SSOD performance. Our findings indicate that effective pseudo-labels-basee SSOD requires a fundamental understanding of the challenging factors affecting label quality, such as class distribution, noise, and the precision- trade-off. By addressing dataset imbalances and implementing data-centric quality measures, we demonstrate that even basic SSOD frameworks can perform well under real-world conditions. We propose four novel, computationally efficient, and broadly applicable building blocks to improve the effectiveness SSOD. Our building blocks consist of two methods for balancing training data to increase rare class representation (RCC, RCF), method to enhance labeled data quality (GI.C), and method to refine the selection of pscudo-labeled images (PLS). Through comprehensive experiments across different data configurations on the KITTI and BDD100K autonomous driving datasets, we show that our methods significantly improve the effectiveness of student-teacher SSOD frameworks when integrated individually. Our results highlight the significant. potential of our methods and pave the way for future rescarch in SSL.
6 Discussion and Future Work
We evaluate each proposed method individually to isolate its specific impact SSOD, joint evaluation risks conflating their contributions. Therefore, we focus on a detailed analysis within controlled sctting, providing a clear foundation for future work to build upon and explore these methods in combination and across other application domains. For instance, in the context of filtering in the SSL framework, combining more advancod filtering mothods, such as those proposed by (Chen et al.] 2022 Li et al. 2023 Kimhi et al., 2024), and training strategies, including those by (Li et al.] [202aa] 2023), with our building blocks holds potential for more significant improvements. Our experiments demonstrate that combining different
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uncertainties (epistemic and alcatoric) achieves 41% MDR at 1% UDR, compared to 46% MDR at the same UDR with dz-
Understanding the influence of training parameters on detector performance and robustness, particularly in the presence of noise, is a promising direction for future work. Additionally, examining how robustness evolves across different stages of training could offer valuable insights into optimizing SSL frameworks. Given the additional hyperparameters introduced by each building block, reducing sensitivity to hyperparameter selection would enhance the practical usability of our approach. Automating batch balancing in RCF, optimizing the removal ratio in PLS, and refining consistency thresholds in GLC through hyperparameter tuning techniques or automated optimization methods could help identify more generalizable configurations.
Our building blocks are designed to be as model- and Haanework-agnoslin as possible, avoiding reliance on domain-specific assumptions. They utilize available labels and predictions without requiring modifications to the underlying detector architecture or SSOD framework. RCC generates collages based on ground truth labels. GLC refines ground truth labels using teacher predictions and augmentation consistency, assuming the availability of trained teacher detector. RCF only requires changes to the dataloader for batch structuring, PLS assumes trained teacher detector that outputs confidence scores and incorporates filtering as part of its post-processing, a common feature in most object detectors but not universal. This highlights the advantages of evaluating with additional detectors, such as Transformer-bassed models, as part of future work. Our results demonstrate that RCC, RCF, and GLC improve teacher performance, with benefits extending to the student, particularly through PLS. However, since our empirical validation involves a single detector type (EfficientDet-DD and YOLOv3) and a single SSOD framework (STAC), additional evaluation across diverse detectors and SSOD frameworks would strengthen the potential for widespread adoption of our methods. KITTI and BDD provide diverse and challenging conditions reflective of real-world datasets, including class imbalance, label noise, and varying dataset sizes. Extending the evaluation to other domains, such as medical imaging, satellite imagery, and industrial quality control, would further demonstrate the broad applicability and robustness of our methods.
PL. effectively identifies low-quality pseudo-labeled images post-filtering. However, these are currently discarded. Integrating AL with SSL could enhance Ms performance by utilizing those images. Low-quality pscudo-labeled images could be directed either to an oracle or included in a consistency- loss, enabling further feature extraction in self-supervised manner. Moreover, adding depth estimation to our metric D; could improve its correlation with missing detections and reduce reliance on teacher model score quality, as distant objects often have higher miss rates than nearby ones.
unsupervised
Curriculum learning approaches could also be explored to complement SSL and AL. Specifically, we propose defining a curriculum for Mg based on the learning progression of MT to mitigate error propagation. For example, adopting ramp-up strategy by starting with labeled data before gradually incorporating pseudo- labeled data might outperform using both from the start. Moreover, alternative weighting strategies for ranking images in RCF, considering various forms of prior knowledge beyond class frequency, could be further investigated.
Constructing collages of rare pseudo-labeled objects could also further increase performance via RCC, espe- cially under low data regimes. However, collages introduce a trade-off between addressing class imbalance and potential distribution shifts, highlighting the possible limitation of this approach. Using the same train- ing dataset to generate the collages ensures consistency with the original data distribution but also limits the diversity of objects and contexts available for training. Gathering collages from other datasets containing similar classes is promising despite significant challenges such as differences in labeling conventions and the semantic understanding of class definitions across datasets, as also highlighted by (Liu et al. 2022a). For example, distinctions between classes such as "van", "truck", and "car" often vary based on annotator in- terpretation, potentially introducing additional label noise. Moreover, matching rare classes across datasets poscs significant challenges. For instance, BDD contains rare classes such as "rider", which are absent in KITTI, making cross-dataset collage generation infeasible in specific scenarios. Future work could explore methods enable more effective collage generation in RCC by integrating domain adaptation techniques and utilizing large-scale datasets with harmonized class definitions.
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For GLC, our work could be extended to analyze class-based errors. Given the excellent performance of the classification head across datasets, optimizing the combination of class and box losses could further improve the quality of pseudo-labeled samples.
In summary, basic SSOD frameworks become more nuanced when confronted with real-world data. We hope this work encourages deeper investigation into SSOD, moving beyond incremental gains toward a more comprehensive understanding of the core challenges and mechanisms underlying SSL.
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26


Reviews:
[{"review_id": "afvtjrxkwi", "reviewer": "Reviewer_h5gt", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n1) Well-written and well-presented\n2) Extensive experiments to show the effectiveness of the proposed components\n3) Useful discussion on the limitations of the work\n\n\nWeaknesses\n1) The collective performance gain from all four components seems to be missing (or maybe I missed it)\n2) A single model (EfficientDet-D0) has been employed in the evaluations\n3) The performance depends on carefully choosing many hyper-parameters/thresholds (e.g. threshold for rare/common images, confidence score threshold, and etc)", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper first provides an in-depth analysis of pseudo-labeling-based Semi-supervised Object Detection (SSOD) to identify the potential causes of suboptmial performance in SSOD. Next, it proposes four building blocks that can be incorporated into SSOD frameworks: Rare Class Collage (RCC), Rare Class Focus (RCF), Ground Truth Label Correction (GLC), and Pseudo-Label Selection (PLS). Finally, they experimentally demonstrate that using the proposed building block can enhance the performance in the SSOD task.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Major changes:\n1) The authors should provide an experiment to show the performance gain from incorporting all proposed building blocks together (or mention where the corresponding results can be found if I missed it)\n\n2) Additional model should be used in the evaluations. Due to the volume of the experiments, only a small subset of the experiments can be conducted with the new model. \n\nMinor change: \n1) Considering Pseudo-Label Selection (PLS) as metric is confusing. I think the paper should refer to PLS as a method instead of a metric.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "t92uYZqoVb", "reviewer": "Reviewer_2jgj", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "I particularly liked that the authors make an effort to use real time predictions statistics as a quality assesment for both the model and the data. While existing methods typically relay on more treditional robust assesments, it is usually a computational burden (spesifically for object detection, assesing the quality of the regression is extremly hard task to preform efficiently, and most methods use all data statistics or 100 pertubated boxes to asses quality).\nThe paper is well sectioned, clear figures and description of each module, as well as experiments are well documented.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper suggest two components for SSOD paradigme, that can be sub-categorized into four, focusing on class rareity and label refinement. each module categorize the action by the definition of problem, as defined by the paper, varying from real labels to pseudo labels, missing to noisy and so on.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "My main concerns fall into two categories: hypothesis validation and comparable methods-validated results.\n\n# 1. Hypothesis Validation\n\n* The authors make several statements, for example, claiming that noisy labels in object detection are not addressed, while citing claims about the effects of label noise from classification tasks. This is indeed an issue, for example in [1]; however, even in instance mask prediction, most of the effects on the model derive from misclassification, as presented in [2]. They argue for spatial label noise, yet the ablation shows that most of the performance hindrance comes from class noise.\n\n* Other statements, such as the certainty approximation based on the model's confidence for a specific image, need to be validated by collecting statistics on the number of instances per image, model calibration, and different stages of training (e.g., an untrained model is more likely to be uncalibrated).\n\n* Consider an alternative for $𝑆_i(\\delta_i)$, such as the margin between the first and second class scores of the classification head [7], as it is robust to the number of instances in an image.\n\n* You claim that the blocks are model-agnostic. I disagree, as the method builds exclusively on EfficientDet. Modern detectors, such as DETR, operate differently, and some of the assumptions may not hold when using object queries instead of anchors. Additionally, your method relies on a teacher-student paradigm that leverages teacher-student consistency. If you aim to be framework-agnostic, you would need to modify this appropriately.\n\n* A final note on this aspect: you utilize \"copy-paste augmentation\" [3] to mitigate imbalanced predictions. However, this approach completely disrupts the natural image distribution. I wonder about its effects on object detection \"in the wild.\" By creating a collage, you may address the class imbalance issue but simultaneously introduce out-of-distribution images that are unlikely to occur in real-world scenarios. It would be beneficial to see examples from a different dataset or simply outside the validation set and analyze the cost/effects of your method.\n\n\n# 2. Comparable Methods\n\n* The literature includes a wealth of SSOD approaches [4,5], including those addressing class imbalance [6]. Since your paper does not address any aspect unique to the data presented, it would be more compelling to compare your results with these existing methods. I believe it would strengthen your argument significantly if you applied your method to LVIS, where class imbalance is prominent (and where various existing papers have conducted similar experiments). While not mandatory, I think testing your imbalance correction on COCO would also be appropriate, given its larger set of classes.\n\n* Please add \"cascaded two-stage thresholding\" [8] to the related work section.\n\n\n### References:\n\n[1] Towards Robust Adaptive Object Detection under Noisy Annotations\n\n[2] Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters\n\n[3] Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation\n\n[4] Label Matching Semi-Supervised Object Detection\n\n[5] Semi-Supervised Object Detection via Multi-instance Alignment with Global Class Prototypes\n\n[6] Gradient-based Sampling for Class Imbalanced Semi-Supervised Object Detection\n\n[7] Semi-Supervised Semantic Segmentation via Marginal Contextual Information\n\n[8] Robot Instance Segmentation with Few Annotations for Grasping", "broader_impact_concerns": "No concerns raised about the broader impact", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "UgLBwKalIY", "reviewer": "Reviewer_Xcrj", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The writing and presentation of this work is one of its strengths. Sections are ordered sensibly, figures and tables are presented well, and the text is very readable. The authors' choice of interventions to study are simple but also practical and useful. The experiments are reasonably comprehensive as well.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors contribute a novel empirical study in the field of semi-supervised object detection (SSOD) for self-driving datasets. They enumerate three causes of suboptimal pseudo-labeling (class imbalance, noise in ground truth labels, incomplete or inaccurate pseudo-labels) and propose remedial methods for each of the shortcomings.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "W1. The paper's claim that the methods they propose are model-agnostic and framework-agnostic is not sufficiently supported by the experiments, which consist of a single model and a single framework. It is not clear from these experiments alone that the conclusions drawn by the authors would hold true for other models and frameworks. I would request either that the authors change the claim that their methods are model-agnostic and framework-agnostic, or add another model and another framework to the experiments in the paper.", "broader_impact_concerns": "None.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper presents a novel empirical study addressing challenges in semi-supervised object detection (SSOD) for self-driving datasets, focusing on mitigating class imbalance, ground-truth label noise, and pseudo-label inaccuracies. The reviewers unanimously recommend acceptance, commending the work's clarity, practical interventions, and comprehensive experiments. Key strengths include the logical organization of content, effective visual aids, and actionable insights for the community. While initial concerns were raised about the scope of claims and the generality of data/model-agnosticism, the authors addressed these through revisions, as noted in the reviews.

Therefore, I recommend accepting this paper.
Decision: Accept (poster)