SPWOOD: Sparse Partial Weakly-Supervised Oriented Object Detection
Authors: Wei Zhang
Xiang Liu
Ningjing Liu
Mingxin Liu
Wei Liao
Chunyan Xu
Xue Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Oriented Object Detection
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: A consistent trend throughout the research of oriented object detection (OOD) has been the pursuit of maintaining comparable performance with fewer and weaker annotations. This is particularly crucial in the remote sensing domain, where the dense object distribution and a wide variety of categories contribute to prohibitively high costs. Based on the supervision level, existing OOD algorithms
can be broadly grouped into fully supervised, semi-supervised, and weakly supervised methods. Within the scope of this work, we further categorize them to include sparsely supervised and partially weakly-supervised methods. To address the challenges of large-scale labeling, we introduce the first Sparse Partial Weakly-Supervised Oriented Object Detection (SPWOOD) framework, designed to efficiently leverage only a few sparse weakly-labeled data and plenty of unlabeled data. Our framework incorporates three key innovations: (1) We design a Sparse-annotation-Orientation-and-Scale-aware Student (SOS-Student) model to separate unlabeled objects from the background in a sparsely-labeled setting, and learn orientation and scale information from orientation-agnostic or scale-agnostic
weak annotations. (2) We construct a novel Multi-level Pseudo-label Filtering (MPF) strategy that leverages the distribution of model predictions, which is informed by the model’s multi-layer predictions. (3) We propose a unique sparse partitioning approach, ensuring equal treatment for each category. Extensive experiments on the DOTA-v1.0 and v1.5 datasets show that SPWOOD framework
achieves a significant performance gain over traditional OOD methods mentioned above, offering a highly cost-effective solution.

Published as a conference paper at ICLR 2026
SPWOOD: SPARSE PARTIAL WEAKLY-SUPERVISED ORIENTED OBJECT DETECTION
Wei Zhang', Xiang Liu', Ningjing Liu', Mingxin Liu', Wei Liao?, Chunyan Xu², Xue Yang
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University
2 Nanjjng University of Science and Technology
Code: https: / /ttp//gtttttt githhb..thhttggggggggg com/ /tt//visiioonn /LsionnLaab//P000
ABSTRACT
A consistent trend throughout the research of oriented object detection (OOD) has been the pursuit of maintaining comparable performance with fewer and weaker annotations. This is particularly crucial in the remote sensing domain, where the dense object distribution and a wide variety of categories contribute to pro- hibitively high costs. Based on the supervision level, existing OOD algorithms can be broadly grouped into fully supervised, semi-supervised, and weakly su- pervised methods. Within the scope of this work, we further categorize them include sparsely supervised and partially weakkl-supervised methods. To address the challenges of large-scale labeling, we introduce the first Sparse Partial Weakly- Supervised Oriented Object Detection (SPWOOD) framework, designed to effi ciently leverage only few sparse weakly-labeled data and plenty of unlabeled data. Our framework incorporates three key innovations: (I) We design Sparse- annotation-Orientation Orientation-nan Seaentaaioonan-SSall--aaaa
Student (SOS- (SSS-Student model to separate unlabeled objects from the background in sparsely-labele setting, and learn ori- entation and scale information from prientation-agnosti or acale-agnostic weak annotations. (2) We construct a novel Multi-level Pseudo-label Filtering (MPF) strategy that leverages the distribution of model predictions, which is informed by the model's multi-layer predictions. (3) We propose unique sparse partitioning approach, ensuring equal treatment for each category. Extensive experiments on the DOTA-v1 1.03-v1.0MM. 100v and DIOR datasets show that SPWOOD framework achieves a significant performance gain over traditional OOD methods mentioned above, offering a highly cost-effective solution.
INTRODUCTION
In the field of oriented object detection (OOD) task, early research is often supervised by rotated bounding box (RBox) (Ding et al., 2019; Xie et al., 2021; Yang et al., 2019b; 2021), as shown in Figure I(a). However, the dense distribution and diverse nature of objects in the remote sensing domain make extremely difficult to obtain large-scale datasets with such detailed annotations.
To mitigate the reliance on fully annotated data, significant developments have been proposed, such as semi-supervised oriented object detection (SOOD) (Hua et al., 2023; Liu et al., 2021; 2022; Wang et al., 2025) and weakly supervised oriented object detection (WOOD) (Yang et al., 2023; Yu et al., 2025a; Luo et al., 2024) shown in Figure 1(b-c). Semi-supervised methods utilize pseudo-labeling strategies (Li et al., 2022a; Wang al., 2023b) learn angle and scale information from plenty of unlabeled data. Weakly supervised methods use training data with less detailed labels, such as hori- zontal bounding box (HBox) (Yu al., 2023) point (Yu et al., 2024; Ren et al., 2024; Zhang al., 2025). More recently, two notable subfields have emerged that integrate these approaches to further the reduce annotation burdens: Partial weakly-supervised oriented object detection (PWOOD) (Liu et al., 2025a) integrate the unlabeled and weakly annotated datasets. Sparsely supervised oriented object detection (SAOD) (Suri et al., 2023; Rambhatla et al., 2022; Lu al., 2024) leverage the labeled datasets containing annotations for only fraction of the objects in an image. As illustrated in Figure (dd-)), these methods alleviate the annotation dilemma further.
"Corresponding Author

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Published as a conference paper at ICLR 2026
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Figure 1: Current oriented object detection methods are predominantly classified into five categories. Compared to the aforementioned approaches, our proposed Sparse Partial Weakly-supervee Ori- ented Object Detection (SPWOOD) distinguished with minimal annotation requirements.
To further reduce annotation costs, we first propose a novel framework called Sparse Partial Weakly- supervised Oriented Object Detection (SPWOOD). This framework effectively leverages sparsely and weakly annotated data, along with unlabeled data. Inspired by the teacher-studens paradigm (Tarvainen & Valpola, 2017), we leverage small amount of sparsely and weakly annotated data for pre-training. Through this process, the student module learns the scale and angle information from weak annotations and acquires the ability to distinguish unlabeled object from background in the sparse annotation setting. As training progresses, the teacher module's 's capabilities are continuously enhanced through Exponential Moving Average (EMA) mechanism. Upon entering the unsuper- vised learning stage, the teacher module generates pseudo-labels for unlabeled data. These pseudo- labels serve as strong supervisory signal to train the student module, allowing it to learn from both the limited sparsely and weakly annotated data and the abundant unlabeled data. Consequently, pseudo-labels' quality and the strategy used to filter them are crucial for model's performance.
Traditional supervupe methods for pseudo-labe selection typically depend on static thresholds (Liu et al., 2021; Wang et al., 2025), which often leads to performance that is sensitive across differ- ent training processes (Chen al., 2022a; Wang et al., 2023b; 2022; Zhong al., 2020). In contrast, PWOOD (Liu et al., 2025a) introduced more robust approach by leveraging Gaussian Mixture Model (GMM) to cluster the teacher model's predictions. While sparse-annotation methods, such as S? Teacher (Lin et al., 2025) and RSST (Liao et al., 2025), employed sophisticated techniques like top-k high-confidence proposal selection or a class-aware label assignment mechanism that lever- ages the distribution of class features. To better utilize the teacher module's predictions in sparsely annotated setting, we designe a Multi-level Pseudo-labels Filtering (MPF) mechanism. Considering the inconsistencies between different layers, we dynamically adjust the filtering threshold for each layer's 's selection. This allows the model to adaptively generate more stable pseudo-labels that are better aligned with the teacher's performance. Our approach improves the model's ability handle diverse and sparse scenarios, ultimately leading to more robust detection performance.
When creating sparse datasets, prior methods (Lu al., 2024; Lin et al., 2025) follows what we term the Single Sparse Method, where annotations are processed on an manage-by-inan basis. A critical limitation of this technique its inherent bias: when an image contains an annotation from rare category, at least on annotation will be preserved simply. This disproportionately retains sparse categories and leads to a significant mismatch between the distribution of the processed data and the original dataset. To overcome this issue, we propose novel approach called the Overall Sparse Method, treating all labeled annotations across the entire dataset as a single, unified group. This allows us to apply consistent sampling ratio to each category, ensuring that every class is treated equally. As a result, our method effectively maintains the overall distribution of the original dataset. The contributions of this work follows:
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To our best knowledge, introduce the first Sparse Partial Weakly-supervise Oriented Object Detection (SPWOOD) framework. This unified training pipeline is designed to robustly support various multi-format annotations (RBox, HBox, Point) or their combination imput under sparse partial annotation setting, thereby alleviating the significant burden of large-scale annotation.
We construct student model designed acquire the crucial ability perceive sparse annotation environments and learn object orientation and scale information within the sparse partial weakly- annotated scenarios. We term this model the
model.
The Multi-level Pseudo-labels Filtering (MPF) mechanism is designed to resolve the inconsistency between traditional selection criteria and the model's 's prediction confidence. By doing so, MPF significantly enhances the robustness of the pscudo-label selection process for unlabeled data.
We propose fundamentally distinet sparse annotation approach for creating sparse-partin partial datasets. Our SPWOOD framework has been rigorously trained and validated on the DOTA- v1.0/v1.5 and DIOR sparse-partial settings. SPWOOD achieves performance highly comparable to state-of-the-art Oriented Object Detection (OOD) algorithms.
2 RELATED WORK
2.1 SEMI-SUPERVISED ORIENTED OBJECT DETECTION
To effectively leverage abundant unlabeled data, the teacher-student framework has been widely adopted in semi-supervised object detection (Li et al., 2022b; Liu et al., 2023; Nie et al., 2023; Sun al., 2021a). This method begins by training student module on limited set of fully laheled data. A teacher module then acquires the ability to generate pseudo-labels for unlabeled data by using an exponential moving average (EMA) of the student's weights. These pseudo-labels subse- quently guide the student's 's training, creating a learning loop. For instance, MCL (Wang et al., 2025) introduced novel approach by introducing Gaussian Center Assignment for labeled data and Scale- aware Label Assignment for unlabeled data. Besides, SOOD++ (Liang et al., 2024) treated remote sensing images as global layouts, explicitly establishing many-to-many relationship between sets of pseudo-labes labels and predictions to enhance detection. However, despite their innovations, these methods still rely on a substantial number of fully annotated RBox for their initial training.
2.2 WEAKLY SUPERVISED ORIENTED OBJECT DETECTION
Weakly supervised object detection algorithms (Bilen et al., 2015; Iqbal al., 2021; Yang et al., 2019a; Zhang et al., 2021; Zhu et al., 2023), as a significant breakthrough, offer more efficient alternative to fully supervised methods by leveraging weak annotation, such as HBox or point an- notations. For HBox-supervised methods (Li et al., 2022c; Sun et al., 2021b; Tian et al., 2021; Wang et al., 2024; Zhu et al., 2023), H2RBox (Yang et al., 2023) introduced a weakly supervised branch and a self-supervised branch to learn both scale and orientation information. Building on this, H2RBox-v2's primary innovation lay in its symmetry-basee -asee self-supervised learning (Yu et al., 2023), which directly derived crucial directional information from object's inherent symmetry. For point-supervised methods, the main challenge is how to accurately learn object's 's scale and angle information. Recent studies have made significant progress in this area (Chen et al., 2021; 2022b; He et al., 2023; Ying et al., 2023). P2RBox (Cao et al., 2023) and PointSAM (Liu et al., 2025b) demonstrated remarkable performance by leveraging the zero-shot capabilities of the Segment Any- thing Model (Kirillov et al., 2023). Additionally, PointOBB (Luo et al., 2024) and its subsequent versions (Ren al., 2024; Zhang al., 2025) pushed the boundaries of puper-sup detection by using instance learning to solve the scale problem and class probability map to acquire angle information. More recently, Point2RBox (Yu et al., 2024) adopted knowledge combination strat- egy by introducing synthetically generated targets, offering prior scale information. Furthermore, Point2RBoxv2 (Yu 2025a) incorporated novel losses based on spatial layout constraints, which ensure that predictions align more accurately with real-world object. Besides, Wholly-WOOD (Yu et al., 2025b), a unified weakly supervised detector, accommodated multiple annotation formats in- cluding PointHHBo//RBos 2025a) merged the advantages of semi-supervised method and weakly supervised mthod to achieve enhanced performance, all while substantially lowering annotation requirements.
their combination as inputs. More specifically, PWOOD (Liu et al.,
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Gaussinn Overay
Original Viea
SOS student
On: View
DE BEER
Preds
Point
liooc
Sporse arnotation learning
Aug. View
Augrevcztion View
Adh
Orientation learning
Scale learning
Aui
(el 50S-Student
EMA
Fhp
Student
Preds
Fresh
Stident
Sparsely weakly Labeled
Teacher
PostivE Dreniuation
Dyrame
Mudi: ledt
Teacher
EM Algorithm
Wesk
Unlabeled
Aug
Negathen Ditribation
labely Dataset
Sparse Partial
Teadhers a SoorE
(b) Multi-level Pscudo-labela Fitterng
Cy Alee
Figure The illustration of the Sparse Partial Weakly-supervised Oriented Object Detection (SPWOOD). The hard negatives and learn the scale and angle information from sparse weak annotation data. The Multi-level Pseudo-labels Filtering (MPF) mechanism acquires ability from student through EMA algorithm and selects high-quality pseudo-labels for student module's 's training.
-n--o---dd-Saaleaaarr Student (SOS-Student) identifies
2.3 SPARSELY SUPERVISED ORIENTED OBJECT DETECTION
In SAOD research filed, only a fraction objects an image are labeled, while the rest remains un- labeled. Lack of complete annotations presents major challenge, as the detector confuse unlabeled objects with the background, since both share the same "background" label. In the domain of 3D object detection, CoIn (Xia et al., 2023) introduced contrastive learning module to enhance feature discrimination and a feature-level pseudo-label mining framework to guide the training. HINTED (Xia al., 2024) proposed self-hoosting teacher, leveraging instance-level information and up- dates pscudo-labels for labeled scenes to enhance learning effectiveness. When addressing similar challenges within the remote sensing domain, Co-mining (Wang et al., 2021) utilized a Siamese network to enhance multi-view learning, with two branches predicting pseudo-labels for each other a co- oo-generation module. Region-based approaches (Rambhatla et al., 2022) treated SAOD as semi- semi-supervieed problem at the region level, focusing identifying unlabeled regions that likely contain foreground objects. Calibrated Teacher (Wang et al., 2023a) introduced an online calibra- tion mechanism to fit the true precision during training, improving pseudo-labels quality. PECI. (Lu et al., 2024) offered a reinforcement lcarning-based selection strategy specifically tailored for pesudo-labels filtering. More recently, Teeeccher (Lin et al., 2025) proposed a clusterbased pscudo- label generation module to avoid erroneous guidance. RSST (Liao et al., 2025) designed a class- aware pseudo-labeling mechanism for both labeled and unlabeled data by integrating priors from large language model. In contrast to these methods, our approach innovatively integrates various supervised methods under minimal supervision cost.
METHOD
3.1 OVERVIEW
Given the setting consisting of abundant unlabeled data and a small amount of sparsely-weakly anno- tated data, we adopt classic pseudo-labeling semi-supervised object detection (SSOD) framework. As shown in Figure 2. SPWOOD framework operates with two core branches: for supervised learning and another for unsupervised learning. The supervised branch integrates sparse-annotation learning, scale learning, and orientation learning, which together form the COS-Student module. The unsupervised branch leverages a Multi-level Pseudo-labels Filtering (MPF) algorithm to gener- ate reliable supervisory signals used for student module's ' subsequent training.
The training process unfolds in two distinct stages: (1) Burn-in Stage:
The student module begins by training on the few sparse weak annotations from both original and augmented views. Coneur-
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rently, the learned weights of the SOS-Student module mirrored onto the teacher module. (2) Self-training Stage: In this stage, a large volume of dironglyweakkl aaaally-aggmented unlabeled data is fed into the teacher module generate pseudo-labels. The student module is then further optimized using these pseudo-labels. The teacher module is updated from the student's module using an Expo- nential Moving Average (EMA) (Tarvainen & Valpola, 2017) approach, which ensures the teacher's parameters are more stable and reliable, leading to higher-quality pseudo-labels.
3.2
STS-ETTDENT
3.2.1
SPARSE ANNOTATION LEARNING
In the context of sparsely annotated data, a major challenge is effectively distinguishing unlabeled objects from background. Both are treated negatives during training, leading to potential misguid- ance where unannotated objects are incorrectly penalized as background. Inspired by Focal Loss, designed to tackle the class imbalance problem by down-weighting the loss of easy-to-classify ex- amples, we introduce a mechanism to differentiate between different types of negative samples by modulating their loss contribution (Liao et al., 2025). This strategy aims to balance the influence of false supervision and retain beneficial background cues. The loss function formulated as follows:
a1 pr) log(Pt)
for positive objects
Liss =
-(1 (1--au at) log(1 1 Pt)
for negative objects with confidence pe S thr
(1)
-(1 at)p? log(1 Pe)
for negative objects with confidence pt > thr,
Our student classification loss, Lis aims to partition the prediction space into three distinct groups for effective learning under sparse supervision:Labele Objects: Predictions with high confidence (p: > thr) matching the ground truth (GT) labeled category. Background: Predictions with low con- fidence (p: I thr) matching the GT background. Unlabeled Objects (Hard Negatives): Predictions with high confidence (p: thr) that are erroneously matched to the GT background. The base loss formulation incorporates standard components: pt represents the confidence score from the student module; a serves balancing factor between positive and negative samples; and y is the focusing parameter, designed to reduce the contribution of well-classified examples (inheriting the robustness of Focal For the critical third group (Unlabeled Objects), introduce the adaptive factor w. This factor strategically down-weights these potentially misleading false negatives. This novel strategy effectively inherits the robustness of Focal Loss while simultaneously providing targeted solution for sparse annotation scenarios. By mitigating the detrimental effects of misleading false negatives, it leads more accurate and robust model learning.
3.2.2 ORIENTATION LEARNING
To address the lack of orientation information in weakly- data, like HBox annotations, we introduce a symmetry-awart learning approach (Yu et al., 2023) that comprehensively explores the properties object symmetry. As depicted in Figure 2, each input image undergoes a random augmentation (either flipping or rotating) to create an augmented view. This view is then fed into the student module, which generates pair of predicted angles. Since the input views have clear relationship, the output angles are expected to follow the same relationship.
To ensure the SOS-Student effectively learns orientation information from the weakly annotated horizontal bounding boxes, we formulate an angle loss, La
L'nn (0 0, 0) if augmentation is a flip
L'Ans"
La Linng (Orot 0, R) if augmentation is a rotation by R.
The angle loss calculation depends on the specific image augmentation method model used, which can be either a vertical flip or a random rotation by an angle R. The loss function L'nng is a Smooth- L1 loss. Here, O ftpp Orots and represent the predicted angles of the flip-augmented image, the rotated-augmented image, and the original image, respectively.
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Table Comparison with state-of-the-at methods for the OOD task at different sparse-partial ratios. 10% and 10% denote the partial-ratio and sparse-ratio. RSST* represents our SAOD baseline and R:H:P means the annotations count ratio between RBox, HBox and Point.
DOTA-v1.0
Pataset-Sparse-Patee
Algorithm Types
Methods
Annotations
10%4
20%
30%
10%
20%
10%
20%
10%
20%
H2RBox-v2
HBox
30.6
30.8
38.5
42.7
43.9
49.2
Weakly Supervised
Point2RBox-v2
Point
9.4
9.8
14.2
23.1
15.1
27.0
MCL
RBox
31.7
39.0
37.6
44,5
43.5
47.8
Semi-Supervised
PWOOD
RBox
38.0
46.2
46.2
51.9
48.7
55.2
Partial
PWOOD
HBox
33.9
43.8
42.4
47.6
44.8
50.7
Weekky-SSupervin
PWOOD
Point
17.0
24.7
22.4
28.6
23.1
33.8
S Teacher
RBox
36.8
44.0
45.3
50.2
52.3
55.5
Sparsely Supervised
RSST
RBox
43.4
47.2
42.5
52.3
53.0
56.6
RSST
RBox
42.4
45.5
41.7
51.0
53.2
56.5
RBox
48.5
54.0
54.9
57.8
54.9
60.3
Sparse Partial Weakly-Supervised
HBox
45.5
51,9
52.2
54.0
53.1
56.5
SPWOOD (ours)
Point
27.3
36.8
33.3
38.7
35.8
41.8
R.H.P=1:1:1
42.4
48.2
46.1
53.0
50.8
54.8
3.2.3
SCALE LEARNING
Given that weaker annotations, such as point annotations, they lack crucial scale and orientation information about the objects. To address this, we adopt spatial layout learning (Yu al., 2025a) to learn the objects' scale information by determining both the upper and lower bounds.
For the upper bound of the object's scale, we minimize the distance between different predicted oriented bounding boxes. We first model these boxes as two-dimensional Gaussian distributions and then use the Bhattacharyya distance (Yang et al., 2022) to calculate the distance between them. This allows us derive the Gaussian overlap loss, L. as follows:
c. = N Eijj B(N..NG),
(3)
where N donates the number of predicted rotated bounding boxes, Mr and N2 represent different Gaussian distribution, and B is the Bhattacharyya distance between them.
To determine the lower bound of the object's scale, we introduce the Voronoi Watershed Loss. A Voronoi diagram (Aurenhammer, 1991) separates the entire image into individual regions, ensuring each region contains only one point annotation. These regions are then into a watershed algo- rithm (Vincent & Soille, 1991) to obtain pixel-level classification based on pixel similarity. By rotating the output of the watershed algorithm to align with the direction of predicted rotated bound- ing boxes based single point, The Voronoi watershed loss, Lw, is then designed to regress the width and height of the objects:
obtain the regression target of object's 's width and height.
(ww/2
7 h/2
we/2
0
2
Ciy LGWD
(4)
h
where LGWD is Gaussian Wasserstein Distance Loss (Yang et al., 2022).
Finally, we incorporate class loss Li centerness loss Li and box loss Li The total supervised loss, La for the SOS-Student model is given by:
L = Wetefiis + W Ween + Whon Liow W Ang L'ng + wof + ww Li:
(5)
where L Ls and Li represent the sparse annotation aware loss (as described in 3.2.1), the cross entropy loss and the loU loss respectively. The weights Wass Weens Whor are set to 1, while (w Ang. are (0.2, 10, 5) by default.

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Table 2: Comparison of mAP performance across different methods for the OOD task on DOTA-
v1.5 test set under different pparse-partial ratios (RBox supervised).
Algorithm Types
Methods
10-10
10-20
20-10
20-20
30-10
30-20
MCL
25.9
33.9
33.2
40.2
38.2
42.9
Semi-Supervised
PWOOD
33.9
38.1
40.9
45.9
45.1
49.2
S Teacher
33.8
37.2
41.1
45.7
45.7
46.2
Sparsely Supervised
RSST
37.1
40.8
42.3
46.9
45.3
48.0
RSST
36.2
35.8
42.3
46.0
45.7
49.8
Sparse Partial Weakll-Superrised
SPWOOD (ours)
43.2
47.9
49.0
52.1
51.3
53.1
3.3 MULTI-LEVEL PSEUDO-LABELS FILTERING
The selection of pseudo-labels directly influences the performance of subsequent training. In SAOD setting, RSST (Liao al., 2025) leverages classes' diversity and LLM assistance select pseudo- labels, combining fixed number of predictions per category and overall predictions. However, ignoring the confidence variance throughout the training process model makes the detector's per- formance sensitive. Furthermore, Feature Pyramid Networks (FPN) (Lin et al., 2017) is specialized for detecting objects at corresponding scales and the same object exhibits different confidence scores at different levels (i.e. P3, P4, P5, P6, P7), making modeling the entire prediction directly unreliable as Class-Agnostic PseudoLabel Filtering strategy (CPF) (Liu et al., 2025a).
To better utilize the information from the model's prediction and discover reliable pseudo-labels, we focus on the distribution of the teacher's ' predictions by introducing Multi-level Pseudo-labels Filtering. Based Gaussian Mixture Model (GMM) (Wang al., 2023b; Zhao al., 2019), model the prediction confidence of each layer in the teacher module with the following equation:
P'(c') wi,N (o)))) + N (1a (o))))
where Pi(e') means the modeling of i-th level, such as P3, P4, P5, P6 and P7. (N,, 0²) denotes gaussian distribution, while Wp. Myp Fp and Wn, Pn, an represent the weight, mean and variance of positive and negative distributions, respectively. The GMM initialized by setting Hp and μhn to the maximum and minimum of the predicted scores, respectively. The variances (Øp and o,)) and weights (w, and w. are all initially set to 1 and 0.5, respectively, We then use Expectation- Maximization (EM) algorithm solve for the posterior probability, P, with the following equation:
arg Pt (c' , (o)))),
where r' is then used to select pscudo-labels at the corresponding scale level. The selected pseudo- labels from each layer are subsequently used to guide the student module's training.
3.4 OVERALI LOSS
Our proposed SPWOOD framework contains two branches: one for the supervised loss L and another for the unsupervised loss L The combination of these two losses constitutes the overall loss. The former one is detailed in 3.2.3. The latter one is defined as below:
L = L.d (T%, SS + (T S08") + Ci (T gloggit.
where T and S represent the predictions the teacher and student modules, respectively. These prediction include the confidence score (c), centerness (cen) and the margin from the point to the boundaries of the predicted boxes. The loss function Ly and L are binary cross-entropy losses, while Li is Smooth-L1 loss. The overall loss of SPWOOD framework is defined
L L= L- + L"
Through these two complementary branches, the student learns from pparsely-weakee annotated data, enhancing the teacher's 's pseudo-labels filtering ability. In turn, the high-quality pseudo-labels selected by teacher further improve the student's learning, forming positive feedback learning loop.
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Table Comparison of mAP performance across different methods for the OOD task on DIOR test set under different sparse-partial ratios (RBox supervised).
Algorithm Types
Methods
10-20
20-20
30-20
MCL
30.8
33.1
35.8
Semi-Supervised
PWOOD
31.0
36.8
39.1
Teeachher
42.6
45.1
Sparsely Supervised
RSST
40.7
44.8
46.1
RSST
38.8
43,2
45.7
Sparse Partial
SPWOOD (ours)
44.1
45.7
46.3
Weakly-Supervised
Table 4: Detection accuracy under different combinations of weak annotations at the aparse-partial ratio 20-20.
Table 5: mAP results of Sparse Annotation Learning with different weights under sparse-partia 20-20 ratio.
Table 6: Comparison of mAP performance across Sparse Annotation Learning (SAL) module in our framework in DOTA1.0 test set under sparse-pane partia 20-20 ratio.
Roxx:oBBox:Point
mAP
Weight
mAP
1:1:1
53.0
0.4
57.8
1:1:0
56.3
Setting
mAP
1:0:1
52.3
0.3
57.5
0:1:1
47.6
0.2
60.6
with SAL
47.6
0:1:4
41.2
0.1
58.0
without SAL
42.0
4 EXPERIMENT
4.1 DATASET AND SETUP
To evaluate our proposed SPWOOD models, we conducted experiments on DOTA-vI.0/v11.. (Xia et al., 2018) and DIOR (Li et al., 2020). DOTA-v1.0 comprises 2,806 acrial images, consisting of 1,411 training images, 458 validation images, and 937 test images. The training and validation sets contain 188,282 instances across 15 categories. DOTA-v1.5 uses the same images DOTA-v1. but features more annotations, with 403,318 instances across 16 categories, including a higher number of smaller objects. The DIOR dataset comprises 23,463 images across 20 distinet object categories. The dataset is partitioned into 11,725 images for training and 11,738 images for testing.
Taking the DOTA-VI.O dataset as representative example, to create sparse-partial weak supervision dataset, we select 10%, 20%, and 30% of the images as initial labeled data from the DOTA-v1.0 train-val set, while the remaining images are treated as unlabeled. From the labeled subset, we generate datasets using two distinct sparse methods. The first is the Single Sparse Method (Lu et al., 2024; Lin et al., 2025), which applies specific sparse ratio (i.e. 10% 20%, 30%) each category within each image. keeping at least one annotation for any category present in the image. In contrast, our proposed Overall Sparse Method treats all labeled annotations as unified group and samples annotations for each category the desired ratio (i.e. 10%, 20%. 30%). For a fair comparison with prior studies, we conduct main experiments on the datasets generated by the Single Sparse Method, unless otherwise noted. Then create the weakly annottted data for training by simply omitting the orientation and scale information from the annotations. In our naming convention, we combine the partial-ratio and sparse-ratio (i.e. 30-10), where 30% indicates the partial-ratio and 10% indicates the sparse-ratio. Besides, all reported detection results of models are obtained by testing on test set.
Our proposed SPWOOD model implemented using the MMRotate (Zhou al., 2022) frame- works. We employ an FCOS detector (Tian et al., 2019) with a ResNet50 backbone (He et al., 2016) and FPN (Lin al., 2017) neck. The AdamW optimizer (Loshchilov Hutter, 2017) is used for optimization. The entire training schedule consists of 180,000 iterations, which includes an initial burn-in stage 12,800 iterations to stabilize model convergence in the early stage.
4.2 MAIN RESULT
DOTA-v1.0: We employ simplified version of RSST, comprising both supervised and unsupervised branches, as our SAOD baseline. which we term RSST". As summarized Table 1, we present the detection results of different methods on DOTA-v1.0 dataset and SPWOOD exhibits substantial
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Table Detection accuracy at sparse- partial ratio 20-50 (RBox supervised).
Table 8: The performance comparisons of our proposed
PWOOD framework based on different Pseudo-labels Filtering Methods different parse-partie ratios under
Algurilhm Types
Methods
20-50
DOTA-v1.0 Dataset-Sparss-Partin setting.
MCL
53.2
Semi Supervised
PWOOD
59.8
Module
Methods
10-20
20-10
20-20
Teecchr
56.5
Sparsely Supervised
RSST
56.1
SPWOOD
w/ CPF
44.4
53.0
51.9
57.1
RSST
55.1
w/ MPF
49,5
54,0
54,9
57.8
Sparse Partial Weukky-Supervised
SPWOOD (ours)
63.0
Table 9: Performance of the PWOOD Framework with Overall and Single Sparse Methods.
Algorithm paradigms
Methods
10-10
10-20
20-10
20-20
30-10
30-20
Overall Sparse Method
41.7
49.1
47.9
57.2
52.3
57.5
SPWOOD
Single Sparse Method
49.5
54.0
54.9
57.8
54,9
60.3
and consistent improvement over other methods across all sparse- parutel ratios. Notably, SPWOOD achieves superior performance using the less-informative HBox annotations, even outperforning the RSST* model that utilizes corresponding proportions of RBox annotations with gains of 3. % (10-10), 6.4% (10-20), 10.5% (20-10) and 3.0% (20-20). It means that SPWOOD delivers excellent performance at a lower cost. The effectiveness of SPWOOD under weak supervision is further high- lighted by comparisons with WOOD methods. Compared to H2RBox-v2, SPWOOD achieves large margins of improvement, yielding gains of 14.9% (10-10), 21.1% 1 (10-20), 13.7% (20-10), 11.3% (20-20), 9.2% (30-10), and 7.3% (30-20) under the corresponding HBox annotations. Similarly, under point annotations, SPWOOD improves mAP by 17.9%, 27.0%, 19.1%, 15.6%, 20.7%, and 14.8% respectively, compared to Poin22BBox-v2. These results strongly underscore SPWOOD's excellent capability in the pparse-partial weakly-supervin oriented object detection task.
Concurrently, SPWOOD's ability support multiple annotation formats within unified framework offers a highly effective paradigm for reducing the data acquisition burden. As detailed in Table (i.e. R:H.P=1:1:1), we conducted experiments to evaluate the framework's performance under di- verse labeling scenarios, using a combination of Point, HBox, and RBox annotations as input. The detection performance significantly surpasses WOOD and SOOD methods. Crucially, SPWOOD achieves highly competitive performance compared to the current State-of-the-Art SAOD methods across different sparse-partial ratios. In Table 4, we present comprehensive results from experiments conducted under different mixed weak annotation settings. This analysis empirically confirms the robust capability of our framework to accommodate and leverage different types of weak supervi- sion concurrently. Furthermore, as detailed in Table 7, we present the results at the ratio 20-50, highlighting SPWOOD's s effectiveness in utilizing limited annotation resources.
More Results: As shown in Table 2, to provide additional validation for our proposed framework's efficacy, we performed thorough comparative analysis under RBox annotations on DOTA-v1.5 dataset. Our proposed SPWOOD outperforms RSST with gains of 7% (10-10), 12.1% 1% (10-20), 6.7% (20- 10), 6.1 (20-20). 5.6% (30-10) and 3.3% (30-20), respectively. Compared to the gain on DOTA-v1.0 (6.1%. %. 8.5%, 13.2%, 6.8%. 1.7%, and 3.8%. respectively), SPWOOD's performance on DOTA-v highlights its robustness and adaptability in complex scenes with smaller objects. To validate the capability of our model across different datasets, conducted additional experiments on the DIOR dataset. As demonstrated in Table 3, our model consistently outperforms all competing methods across all the ratios.
4.3 ABLATION STUDIES
The selection of pseudo-labels directly impacts the model's subsequent training, making the filtering strategy crucial. As detailed in Table 8, we conducted a rigorous systematic evaluation of our pro- posed Multi-level Pscudo-label Filtering (MPF) algorithm against the Class-Agnostic Pseudo-label Filtering (CPF) approach (Liu et al., 2025a). Our MPF method consistently outperforms CPF across all tested sparse-parte ratios the DOTA-v1.0 dataset. Specifically, our MPF method achieves notable mAP improvements of 5.1% % (10-10). 1.0% (10-20). 3.0% (20-10), and 0.7% (20-20), re- spectively. The most significant gains are observed at lower annotation sparsity levels (e.g.. 10-10),

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Table 10: Annotation Statistics and Performance Analysis for Overall and Single Sparse Methods at sparse-partial ratio 10-10. 20-10 and 30-10. and mean annotations numbers under Single and Overall Sparse Method. presents relative difference among corresponding numbers.
Category
PL
BD
BR
GTF
SV
LV
SH
TC
BC
ST
SBF
RA
HA
SP
HC
762*
88
211
242
2083
1342
2344
238
81
376
137
156
514
225
40
Annotation at 30-10 10
785
37
114
39
2163
1319
2316
189
44
389
34
40
567
124
25
Relative Difference
-2.9%
+137.8
+85.1
+520.5
+3.7
+1.7
+1.2
+25.9
+84.1
-3.3
+302.9
+290.0
-9.3
+81.5
+60.0
551*
57
117
151
1256
863
1868
180
59
306
90
99
352
157
40
Annotation at 20-10 10
585
19
68
24
1270
873
1824
121
35
275
22
27
366
111
30
Relative Difference
-5.8%%
+200
+72.1
+529.2
-1.1
-1.1
+2.4
+48.8
+68.6
+11.3
+309.1
+266.7
+3.8
+41 14
+33.3
383*
37
77
79
767
479
846
100
25
224
48
46
134
110
32
Annotation at 10-10
369
14
29
13
779
491
870
78
20
232
12
11
145
95
24
Relative Difference +3.8%
+164.3
+165.5
+507.7
-1.5
-2.4
-2.8
+28.2
+25.0
-3.4
+300
+318.2
-7.6
+15.8
+33.3
71.6*
46.8
17.3
53.6
55.0
52.3
73.2
86.1
50.3
62.5
27.6
48.1
19.8
53.9
23.0
AP at 10-10
73.4
23.6
17.6
25.6
55,9
56,4
72.6
88.8
37.1
67.5
0.40
24.8
18.5
40.8
18.0
Relative Difference -2.5%
+988.6
-1.7
+109.4
-1.6
-7.2
+0.8
-3.1
+35.5
-7.5
+583.0
+94.2
+6.9
+32.1
+27.6
demonstrating MPF's superior capability in extremely sparse settings. This superior performance stems from MPF's ability to effectively capture intricate relationships within the model's multi-level predictions, thereby achieving superior detection accuracy even with limited supervision.
Sparse Processing Method Analysis: To rigorously evaluate the impact of data generation model performance, we conducted ablation study focusing on datasets created by two sparse methods. As shown in Table 9, the SPWOOD model demonstrates consistently superior performance on the dataset derived from Single Sparse Method compared to Overall Sparse Method, with gains of 7.8%, 4.9%, 7.0%, 0.6%, 2.6%, and 2.8% across the corresponding ratios. As detailed in Table 10, the Sin- gle Sparse Method tends to retain more annotations for categories with initially scarce labels, such as baseball diamond, ground track field, soccer-ball field, and roundabout. We further analyzed the direct relationship between annotation count and detection accuracy (Table 10). For instance, considering the -isemod category under ratio 10- 10, the Single Sparse Method yielded a 164.3% relative increase in annotation count compared to the Overall Sparse Method. This sub- stantial data advantage directly translated into a near 100% (98.6%) relative difference on AP per- formance. These findings decisively reveal that relatively higher annotation count leads to superior detection accuracy, which is particularly pronounced for categories with few initial annotations.
More results: We conducted ablation study the parameter within our Sparse Annotation Learning module, as shown in Table 5. In our framework, the primary function w is to suppress the influence of hard negatives, ensuring robust training despite sparse munotations.Furrhhmmmor Furthermore, we performed a ablation study about Sparse Annotation Learning module in the SOS- Stuuentt compo- nent, with results presented in Table 6. The substantial performance degradation observed after removing this module clearly underscores its critical contribution in the sparse setting. These results underscore the Sparse Annotation Learning module's efficacy in semi-sparse setting.
5 CONCLUSION
In this work, we propose a Sparse Partial Weakly-Superviae Oriented Object Detection (SPWOOD) framework, which leverages the strengths of mainstream oriented object detection methods to sig- nificantly reduce the need for annotation in remote sensing. Given the sparse weak annotation data, we introduce three core components sparse annotation lcarning, orientation learning, and scale learning- to form -otSentt model. To effectively utilize abundant unlabeled data, we employ a Multi-level Pseudo-labels Filtering mechanism to select reliable supervised signals. Extensive ex- periments on benchmark datasets demonstrate that SPWOOD outperforms existing OOD methods, all with a minimal annotation cost. Furthermore, we introduce an novel sparse processing method, guaranteeing that the sparse dataset maintains the same distribution as the original data. One cur- rent limitation of our approach is that it exclusively utilizes a single visual modality. Incorporating additional data modalities (c.g.. spectral or textual information) is posited as an interesting future research direction, particularly for further improving performance within sparse annotation settings.
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ACKNOWLEDGMENT
This work was supported by the National Natural Science Foundation of China (62506229), Natural Science Foundation of Shanghai under 142268, and Shanghai Yuan Innovation Foundation.
ETHICS STATEMENT
This work is conducted in accordance with all ethical standards. We have appropriately cited all compared and referenced methods. The datasets used are publicly available and have been appropri- ately cited. We have ensured that our data collection and processing methods, well the release of our code, do not violate privacy rights or intellectual property laws.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we have provided comprehensive details of our experi- mental setup, methodology. and results. This includes clear description of the algorithms,, model architectures, hyperparameters, and training procedures used. We will make our source code, in- cluding all necessary scripts and configurations, publicly available upon publication. Besides, will provide detailed instructions on how to prepare sparse partial weak supervision dataset.
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Yue Zhou, Xue Yang. Gefan Zhang. Jiabao Wang, Yanyi Liu, Liping Hou, Xue Jiang, Xingzhao Liu, Junchi Yan, Chengqi Lyu, et al. Mmrotate: A rotated object detection benchmark using pytorch. In Proceedings of the 30th ACM International Conference on Multimedia, pp. 7331-7334, 2022.
Tianyu Zhu, Bryce Ferenczi, Pulak Purkait, Tom Drummond, Hamid Rezatofighi, and Anton Van Den Hengel. Knowledge combination to learn rotated detection without rotated annotation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15518-15527, 2023.
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Published as a conference paper at ICLR 2026
Table 11:
Comparison of computational costs across different methods for the OOD task on DIOR
test set under different sparse- sparse-partial partial ratios (RBox supervised).
Algorithm Types
Methods
memory usage
running time
Semi- Supervvie
MCL
5598MB
16hours
Partial Weakly-Supervisee
PWOOD
9021MB
23hours
Sparsely Supervised
RSST
13672MB
34hours
Sparse Partial
SPWOOD (ours)
22785MB
40hours
-upeer--de
Figure 3: Qualitative results showing the qualities of the detection performance.
A APPENDIX
A.1 THE USE OF LARGE LANGUAGE MODELS (LLMS)
We affirm that this paper prepared and written entirely by us. We did not use any large language models (LLMs) to generate the abstract, content, or any part of the text. The ideas, analysis, and conclusions presented are the sole product of the authors' original thought and research. We did, however, utilize standard tools like grammar checkers for minor stylistic improvements.
A.2 COMPUTATIONAL COST COMPARISON
As shown Table 11, the operational cost (time and computational resource consumption) of our spwood method is observed to be higher compared to other models. This increase is primarily attributed to two factors: The necessary overhead associated with processing multiple weak anno- tations within the model. like combination of HBox and point. The substantial computational load required for effectively handling the highly sparse annotation settings, where extensive calculations performed compensate for the lack of dense supervision.
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Published as a conference paper at ICLR 2026
A.3 FAILURE ANALYSIS
As depicted in Figure 3, we use different colors to illustrate the different bounding boxes under sparse conditions: the blue boxes represent the sparse annotations used for training; the red boxes indicate objects that were omitted from the labels due to the sparse setting; and the green boxes represent the final objects detected by our SPWOOD framework. The figure highlights the following scenarios and challenges:
Column (Effectiveness in Sparse Setting): This column demonstrates our model's effectiveness in sparse scenarios, showing its ability to learn class features from partially labeled data and success- fully detect other unlabeled instances of the same category in image.
Column (Intra-class Variance Challenge): This column highlights cases where the model fails to detect certain objects. This failure typically due to significant intra-class variance (differences within the same category) present in the sparsely annotated data.
Column 3 (Complex Spatial Arrangement Challenge): This section illustrates the performance degradation observed when dealing with complex spatial arrangements, such such large objects containing smaller ones or closely packed, overlapping instances.
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Reviews:
[{"review_id": "rdyYJ1QmiK", "reviewer": "Reviewer_gKiC", "summary": "This paper presents a novel and promising setting (Sparse Partial Weakly-Supervised ) that makes significant strides in addressing the high annotation costs associated with oriented object detection in remote sensing imagery. Its innovations, methodology, and experimental results demonstrate considerable potential.", "questions": "1. I would like to know whether the symmetry assumption in directional learning limits the generalisation ability of the method.\n\n2. The paper demonstrates SPWOOD's capability with a combination of annotation types (R:H:P=1:1:1 in Table 1). This is a realistic and strong setting. However, how does the framework's performance vary with different proportions of weak labels? \n\n3. The ablation study on sparse data creation methods (Table 5 & 6) presents a very interesting, albeit counter-intuitive, finding: the \"Single Sparse Method,\" which introduces sampling bias by over-representing rare categories, outperforms the more distributionally faithful \"Overall Sparse Method.\" The paper attributes this to the raw annotation count. Could this also indicate that the model is simply overfitting to these rare classes, and its performance on common classes might be suffering? Furthermore, what are the practical implications of this finding?\n\n4.  Is the performance gain of SPWOOD significant enough to justify its potential increase in computational cost, particularly for resource-constrained research environments?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces a novel Sparse Partial Weakly-supervised Oriented Object Detection (SPWOOD) framework designed to significantly reduce annotation costs for oriented object detection in remote sensing. This task setting is more challenging and can further reduce annotation costs.\n\n2. SPWOOD effectively leverages sparsely annotated data, weakly annotated data, and a large volume of unlabeled data, supporting various annotation formats (RBox, HBox, Point) or their combinations. This flexibility is a significant advantage, allowing it to adapt to diverse annotation resources.\n\n3. The author employs a “simplify and divide” approach to transform a seemingly intractable, contradictory learning task into a series of subproblems that can be tackled through technical means.\n\n4. The paper is well-structured.", "weaknesses": "1. While attempting to classify different methods and their cost-effectiveness, it is overly dense. \n\n2. The paper claims the Overall Sparse Method addresses the bias of the Single Sparse Method. However, Table 6 on page 9 shows that the Single Sparse Method sometimes yields higher annotation counts and AP performance for several categories, which appears contradictory to the paper's claims. While the paper attempts to explain this by stating the Single Sparse Method \"tends to retain more annotations for categories with initially scarce labels,\" the explanation is not deep enough.\n\n3. The description of the Multi-level Pseudo-label Filtering (MPF) implementation details is not sufficiently thorough. For example, specific details on the initialization and update mechanisms for GMM parameters (mean, variance, weights) are missing.\n\n4. While the ablation study demonstrates MPF's superiority over CPF, a more detailed analysis of the individual contributions of different components within SPWOOD (e.g., orientation learning, scale learning, sparse annotation learning within SOS-Student) would be beneficial.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "S5WcJGHMEj", "reviewer": "Reviewer_2UVa", "summary": "This paper introduces SPWOOD, a framework to tackle the novel and challenging task of Sparse Partial Weakly-Supervised Oriented Object Detection. This problem setting is highly relevant, especially for domains like remote sensing where annotation costs are prohibitive.", "questions": "None", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The primary contributions are: (1) defining this new, complex problem setting; (2) proposing a unified teacher-student framework that leverages sparse, weak (HBox/Point), and unlabeled data simultaneously; (3) introducing several components, including a Sparse-annotation-Orientation-and-Scale-aware Student (SOS-Student) and a Multi-level Pseudo-labels Filtering (MPF) mechanism. The experiments conducted on DOTA datasets demonstrate that the proposed framework achieves state-of-the-art performance, significantly outperforming methods from related sub-fields.", "weaknesses": "Despite the impressive results and the important problem formulation, I have several major concerns regarding the methodology's novelty and the paper's internal consistency.\n1. Questionable Novelty of the Core Supervised Model (SOS-Student)\nThe paper presents the SOS-Student as a key innovation, but its core components for learning orientation and scale appear to be directly adopted from prior work.\n•\tOrientation Learning (Sec 3.2.2): This is explicitly stated to be a symmetry-aware approach from Yu et al. (2023).\n•\tScale Learning (Sec 3.2.3): This adopts the spatial layout learning method from Yu et al. (2025a), using Gaussian overlap and Voronoi watershed losses.\nWhile combining these techniques in a new framework is an engineering contribution, the paper presents them as foundational parts of its proposed model without clearly delineating its own novel algorithmic contribution. The main new component, Sparse Annotation Learning (Sec 3.2.1), is itself insufficiently described.\n2. Lack of Crucial Implementation Details and Unverified Assumptions\nThe paper omits critical details for key methods, making them unreproducible and their effectiveness difficult to assess.\n•\tSparse Annotation Learning: The proposed modification to Focal Loss hinges on an \"adaptive factor $w$\" to down-weight potential unlabeled objects. The paper provides no information on how $w$ is defined or computed. Is it a fixed hyperparameter, a function of the prediction confidence $p_t$, or something else? This is a crucial detail that is entirely missing.\n•\tMulti-level Pseudo-labels Filtering (MPF): This method is based on the strong assumption that the teacher's prediction scores at each FPN level can be effectively modeled by a Gaussian Mixture Model (GMM). The paper provides no empirical validation (e.g., visualizations of score distributions) to support this claim. If the distributions are heavily skewed or non-Gaussian, the GMM-based filtering may not be robust.\n3. Significant Contradiction in the Experimental Design and Claimed Contributions\nThis is my most serious concern. The authors propose a new \"Overall Sparse Method\" for dataset creation as a contribution to overcome the bias of the existing \"Single Sparse Method\" (Lines 100-104, 375-377).\nHowever, the paper's own ablation study in Table 5 clearly shows that their proposed \"Overall Sparse Method\" is consistently and significantly inferior to the \"Single Sparse Method\" across all settings. More problematically, the authors then abandon their own proposed method and use the superior-performing \"Single Sparse Method\" for all main comparison experiments (Table 1, 2), citing \"a fair comparison with prior studies\" (Line 394).\nThis creates a major logical flaw: a method introduced as a contribution is empirically proven to be worse and is subsequently discarded in the main experiments. A contribution that fails its own evaluation cannot be claimed as a strength of the paper and undermines the work's scientific rigor.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uNYWeN63aS", "reviewer": "Reviewer_JpkM", "summary": "This paper proposes SPWOOD, a Sparse Partial Weakly-supervised Oriented Object Detection framework that reduces annotation costs by jointly leveraging sparse, weakly annotated, and unlabeled data. The method introduces three main components: a Sparse-annotation-Orientation-and-Scale-aware Student (SOS-Student) to learn orientation and scale from weak annotations, a Multi-level Pseudo-label Filtering (MPF) strategy to dynamically select reliable pseudo-labels, and an Overall Sparse Method ensuring balanced category sampling. Overall, the paper is clearly written and easy to follow. However, the related work discussion and experimental scope could be more comprehensive, and the novelty relative to closely related sparse or partial weak supervision literature needs clearer differentiation.", "questions": "1.How does SPWOOD perform on other datasets beyond DOTA to verify its generalization capability?\n\n2.How do existing sparse partial supervision methods perform or adapt in oriented object detection, and how does SPWOOD differ from them technically?\n\n3.Would including visual qualitative examples or failure cases help demonstrate how SPWOOD handles sparse or ambiguous annotations?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.The paper investigates a new and practical Sparse Partial Weakly-supervised Oriented Object Detection problem that reduces annotation effort in remote sensing.\n\n2.The proposed SOS-Student effectively integrates orientation and scale learning under sparse and weak supervision. \n\n3.The writing and structure are clear, logical, and easy to follow, with well-presented figures and tables. \n\n4.The framework’s generality across multiple annotation types (RBox, HBox, Point) increases its potential practical significance.", "weaknesses": "1.The related work lacks discussion of similar sparse partial supervision paradigms explored in other domains, such as 3D detection (e.g., HINTED), which share comparable annotation reduction strategies and technical challenges.\n\n2.The experimental validation is confined to DOTA datasets, limiting the evidence of generality; evaluation on other datasets or domains would strengthen the claims.\n\n3.The baseline selection is relatively weak, with no comparison to stronger or more recent sparse partial supervision approaches in general object detection.\n\n4.The paper lacks qualitative failure analysis, which would help identify common error patterns and highlight the limitations of SPWOOD under difficult or ambiguous scenes.\n\n5.The claim of being the “first” to propose this setting seems overstated, as the method mainly adapts existing ideas from related supervision paradigms; broader validation, stronger baselines, and deeper technical discussion would make the contribution more convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ErgJNsaNLy", "reviewer": "Reviewer_Xk5r", "summary": "The paper proposes the SPWOOD (Sparse Partial Weakly-Supervised Oriented Object Detection) framework, which is the first to support multi kinds of annotations, such as RBox, HBox and point under a sparse partial weakly-labeled setting, forming a unified training pipeline for oriented object detection (OOD) task.\nThe SPWOOD framework is built upon the classic Teacher-Student paradigm, aiming to efficiently utilize a minimal amount of sparsely and weakly labeled data alongside a large volume of unlabeled data. The core innovations include three aspects:\n\n1.\tSOS-Student (Sparse-annotation-Orientation-and-Scale-aware Student) Model: This model is specifically designed for sparse annotation scenarios, effectively distinguishing between unlabeled objects and background. It also employs self-supervised learning to extract crucial orientation and scale information from weak annotations that initially lack these details.\n\n2.\tMulti-level Pseudo-label Filtering (MPF) Mechanism: To overcome the limitations of conventional pseudo-label filtering strategies (e.g., static thresholds or single-distribution modeling), MPF utilizes the Gaussian Mixture Model (GMM) to model the confidence distribution of predictions at every layer of the Feature Pyramid Network (FPN). This allows for dynamic adjustment of filtering thresholds, leading to the generation of higher quality and more stable pseudo-labels.\n\n3.\tOverall Sparse Method: An innovative sparse dataset creation technique. It treats all annotations across the entire dataset as a whole, applying a consistent sampling ratio to each category. This ensures that all classes are treated equally and effectively maintains the original dataset's class distribution.\n\nThe paper conducts extensive experiments on the DOTA-v1.0 and v1.5 datasets under a sparse-partial annotation setting. The results demonstrate that the SPWOOD framework achieves significant performance improvements compared to traditional semi-supervised and weakly supervised methods and reaches highly competitive performance with current state-of-the-art OOD algorithms.", "questions": "1. Regarding Parameter Definitions in Formulas (4): In the bounding box loss (Formula 4), please explicitly define the symbols w and h?. Are these variables referring to the dimensions of the predicted box, the anchor box?\n\n2. Could the authors provide a flowchart or a detailed textual description clarifying the precise order of execution between the sparse processing and the semi processing during the SPWOOD dataset generation?\n\n3. Given that the point annotation scenario provides the most minimal supervision, have the authors considered integrating the knowledge or capabilities of large vision foundation models (e.g., SAM) in future work?\n\n4. To enhance the paper's academic completeness and facilitate future research, the authors are requested to incorporate a dedicated discussion about inherent constraints of the SPWOOD framework and promising avenues for future development.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1.\tA Unified Framework: SPWOOD is the first OOD framework to support multi kinds of annotations, such as RBox, HBox and point under a sparse partial weakly-labeled setting, significantly pushing the boundaries of OOD research. It provides a theoretical and practical foundation for remote sensing applications where labeled data is extremely scarce.\n\n2.\tStrong Robustness to Sparsity: The SOS-Student model effectively addresses the core challenge in sparse annotation—the misclassification of unlabeled objects as background (Hard Negatives)—by introducing an adaptive loss modulation mechanism. This improves the model's learning accuracy in sparsely annotated scenarios.\n\n3.\tHigh-Quality Pseudo-label Generation: The MPF mechanism accounts for the prediction score discrepancies across different FPN levels by independently modeling the distribution for each layer. This allows for dynamic and targeted filtering of pseudo-labels, which significantly enhances the quality and stability of the guidance signal during the semi-supervised learning phase.\n\n4.\tFair Data Sampling Mechanism: The proposed Overall Sparse Method ensures a fair sampling process by applying a consistent sampling ratio to all classes across the entire dataset. This avoids the disproportionate preservation of rare categories often seen in traditional Single-Image Sparse Methods, ensuring the class distribution of the sparse data remains consistent with the original dataset.\n\n5.\tSuperior Performance: The experimental results demonstrate that SPWOOD achieves highly competitive performance on the DOTA datasets, validating the effectiveness of the proposed methodology.", "weaknesses": "1. Missing Bounding Box Parameter Definition: In the context of the bounding box loss (Formula 4), the crucial dimensional parameters w and h are not explicitly defined. It is unclear whether these variables refer to the predicted box, anchor box.\n\n2. The sparse processing and the semi processing, it is unclear whether the data is first made sparse and then split, or vice versa. This opacity in the data pre-processing pipeline severely hampers the reader's ability to accurately replicate the structure of the final SPWOOD dataset.\n\n3. Authors should incorporate a dedicated discussion about the inherent constraints of the current research and promising avenues for future work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)