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REVIEW 2 major objections 6 minor 100 references

Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning

T0 review · 2 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that detectors miss 10-pixel objects because training priors and sample selection are biased, and that a dynamic coarse-to-fine training scheme removes the bias, backed by a new dataset and eight-dataset experiments.

desk verdict A genuinely useful oriented tiny-object dataset and benchmark wrapped around a DCFL method that was already published at CVPR 2023; the efficiency claim needs a clarifying sentence but the resource is solid. read the letter →

arxiv 2412.11582 v1 pith:FNIEMRTH submitted 2024-12-16 cs.CV

Chang Xu , Ruixiang Zhang , Wen Yang , Haoran Zhu , Fang Xu , Jian Ding , Gui-Song Xia This is my paper · ORCID
classification cs.CV
keywords orientedobjectdetectiontinyaerialimagerylabelassignmentdynamicpriorGaussiansimilaritybenchmarkdatasetlearningbias
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that oriented tiny object detection fails mainly because of a training-time learning bias, not just weak appearance: fixed prior positions rarely land on objects that average 10.6 pixels, and confidence-based sample selection gives those objects too few positive samples, so they are marginalized further. To support this, it introduces AI-TOD-R, an aerial dataset of 752,460 oriented tiny objects, and a benchmark covering fully supervised and label-efficient methods. Its proposed remedy, Dynamic Coarse-to-Fine Learning (DCFL), moves prior positions onto the object's main area with dynamically predicted offsets and splits label assignment into a coarse candidate-selection step followed by a finer per-object quality filter. The paper reports that DCFL improves detection on eight datasets, including the new benchmark, without adding inference-time parameters or computation. A careful reader would care because it suggests the bottleneck is fixable supervision, not receptive fields or backbone capacity.

What carries the argument

The load-bearing machinery is the Gaussian parameterization of priors and ground truths combined with two dynamic modules. Each box is represented as a 2-D Gaussian, and the Generalized Jensen-Shannon Divergence (GJSD) between two Gaussians provides a symmetric, scale-invariant similarity that replaces IoU in the candidate-ranking step. The Prior Capturing Block (PCB) generates deformable-convolution offsets that update each prior location by an average offset scaled by feature stride, so priors track the small object's discriminative area instead of sitting fixed on the feature grid. The finer filter is the Dynamic Gaussian Mixture Model (DGMM), a two-component mixture whose means are the object's geometry center and the semantic center implied by the medium-quality samples; samples below a threshold are re-labeled negative. Together these components convert static prior setting and one-shot assignment into a dynamic coarse-to-fine loop.

What would settle it

A direct test would be to compute, on AI-TOD-R, the top-K GJSD candidates for every ground-truth box under 16 pixels and check how often at least one candidate overlaps the box with rotated IoU above 0.5 after the dynamic prior update; if the majority of tiny objects have no such usable candidate, the coarse-to-fine pipeline cannot be supplying the samples the paper credits it with, and the measured gains must come from another mechanism.

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Extended reading notes

Core claim

The central claim is that across one-stage, two-stage, and query-based detectors, the same bias appears: objects smaller than roughly 16 pixels receive fewer positive training samples than larger objects because prior positions are static and sparse, and they score lower on posterior confidence, which suppresses them under both supervised sample selection and pseudo-label thresholds. The authors argue this bias is a separable cause of poor tiny-object accuracy, and they show it can be measured in the number and quality of assigned samples. Their remedy, DCFL, has two parts: a Prior Capturing Block that predicts per-location offsets and moves each prior toward the object's main area, and a two-stage label assignment in which a coarse positive bag is built using the scale-invariant Generalized Jensen-Shannon Divergence between Gaussian-fitted priors and ground truths, after which a Dynamic Gaussian Mixture Model per instance filters the bag to high-quality samples. The paper reports that this scheme outperforms the compared methods on AI-TOD-R and generalizes to seven further datasets, with the improvement concentrated in tiny classes.

Load-bearing premise

The scheme assumes that the mathematical similarity used to compare a tiny object with candidate detection boxes keeps ranking the right boxes correctly even when the object spans only about ten pixels, so that the coarse candidate bag and the fine per-object filter both select useful samples; if that similarity stops discriminating at this scale, the reported gains would degrade.

Editorial extensions

If this is right

  • On AI-TOD-R, DCFL raises the AP0.5 of RetinaNet-O from 23.9 to 36.7 at the 1x schedule and of S2A-Net to 49.6 at 40 epochs, without extra inference parameters or FLOPs.
  • The bias diagnosis extends to label-efficient training: with 30% labeled images, the best semi-supervised method reaches a level comparable to a fully supervised one-stage baseline, and HBB-only supervision approximates OBB supervision, implying annotation cost for oriented tiny objects can be cut sharply.
  • Applying the same learning scheme to horizontal detectors improves small-object detection on VisDrone, MS COCO, and DOTA-v2 HBB by 2-3 AP0.5 points, so the correction is not specific to rotated boxes.
  • On multi-scale datasets, per-class gains concentrate on tiny classes, with around 10-18 point improvements on DIOR-R's vehicle, bridge, and wind-mill classes, consistent with the claim that the bias, not the class, was the obstacle.
  • Ablations on DOTA-v2 val attribute the gain to all three components—dynamic prior, coarse candidate bag, and DGMM filtering—with the full combination reaching 59.15 mAP versus the 51.70 baseline.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference (not the paper's claim): if the bias diagnosis is right, then any score-thresholding pseudo-labeling pipeline in semi-supervised detection should under-select tiny objects; replacing the global confidence threshold with a size-calibrated threshold would likely narrow the remaining gap between SSOD and fully supervised training.
  • Inference: the paper's ablations compare GJSD against KLD and GWD only; a natural next test is to swap in a learned matching network inside the same coarse-to-fine structure, which would reveal how much of the gain is the Gaussian metric versus the dynamic candidate-bag design.
  • Inference: since the Prior Capturing Block is model-agnostic, the same offset-update idea could be applied to query-based detectors whose priors are already learned, potentially making the dynamic prior component unnecessary there; the paper does not test this configuration.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper targets oriented tiny object detection and makes three contributions: a new dataset AI-TOD-R (28,036 images, 752,460 oriented instances, mean object size 10.6 px), a benchmark covering fully-supervised and label-efficient detection paradigms, and a training scheme called Dynamic Coarse-to-Fine Learning (DCFL). DCFL consists of a Prior Capturing Block (PCB) that updates anchor/prior positions and a two-stage sample assignment procedure (CPS, MPS, DGMM) intended to balance the quantity and quality of positive samples across object scales and orientations. Experiments are reported on eight datasets, including AI-TOD-R, SODA-A, DOTA-v1/v1.5/v2, DIOR-R, VisDrone, and MS COCO, with claims of state-of-the-art accuracy, high efficiency, and versatility. The dataset, benchmark, and code are promised to be publicly available.

Significance. If the main claims hold, AI-TOD-R is a valuable resource: it is smaller in mean object size than existing oriented object detection datasets, and the benchmark across fully-supervised, semi-supervised, sparsely-annotated, and weakly-supervised settings is informative for the community. The DCFL accuracy improvements are substantial and are tested against standard MMRotate baselines on multiple datasets; the coarse-to-fine assignment idea is a reasonable training-side mechanism for tiny objects. The paper also lists honest limitations (closed-set setting, aerial optical scenario) in the discussion. However, the central 'costless improvement' claim is not yet demonstrated, and the relationship between the baseline and DCFL prior configurations is not fully specified; these points affect how the accuracy numbers should be interpreted. The release of code and dataset is a concrete strength that supports reproducibility.

major comments (2)
  1. [Section I, V-B, VI-B; Equations (2), (8); Tables II, IV] The headline 'costless improvement' claim is not substantiated by the paper's own architectural description. Section I states that DCFL improves detection 'without adding any parameter or computational overhead during inference,' and Section VI-B repeats that the accuracy improvement incurs no extra parameter or computational cost. However, the PCB is described as a learnable block that is part of the detection pipeline (Eq. 2), with an offset-prediction branch and a guided deformable convolution (Section V-B, Eq. 8, Figure 6). If this block runs at inference time, it necessarily adds FLOPs and parameters. The reported numbers in Tables II and IV are compatible with net parameter counts being unchanged or slightly lower, but no breakdown is given that isolates the PCB's contribution or shows how the FLOPs profile was computed. Please provide a direct measurement of parameters and FLOPs with and without the PCB, state explicitly whether the dynamic prior is applied at test time, and, if it is applied only during training, explain how training-time prior updates transfer to a detector that uses static priors at inference. The accuracy results may be valid, but the efficiency claim as written is currently unverified.
  2. [Section VI-C; Tables II, IV, V] The baseline comparisons may not be matched in prior configuration. Section VI-C says 'we tile one prior for each feature point in all experiments,' but it is not clear whether this statement applies to all main experiments or only to the ablations on DOTA-v2. Table II reports RetinaNet-O as 36.3M parameters and DCFL(RetinaNet-O) as 36.1M, and Table IV reports 36.2M versus 36.1M; these differences suggest that the number of priors or the head structure differs between the baseline and DCFL. If the baseline uses the default MMRotate multi-anchor configuration while DCFL uses a single prior plus the PCB, then the reported gains in Tables II, IV, and V conflate the DCFL learning scheme with a change in prior density. Please specify, for each main experiment, the exact prior/anchor configuration used by both the baseline and the DCFL variant, and provide a matched comparison in which the baseline uses the same prior configuration as DCFL. The DOTA-v2 ablation in Table XIIc is a step in this direction, but it is not sufficient to establish that the main-table comparisons are apples-to-apples.
minor comments (6)
  1. [Section V-C, Equations (11)-(12)] The mathematical notation in Equations (11) and (12) is garbled: the displayed expressions 'Σα = (ΣpΣg)Σ α' and 'µα = (µpµg)µ α' are typographically corrupted and should be written as the standard harmonic mean formulas for the Gaussians in the generalized Jensen-Shannon divergence.
  2. [Tables II, IV, V; Section VI-C] All main results are reported as single runs without standard deviations or multiple seeds. Given that the paper claims improvements of 2-8 AP points, reporting at least the mean and standard deviation over three seeds for the core comparisons (especially AI-TOD-R and SODA-A) would substantially increase confidence in the results.
  3. [Section VI-C] The ablations are performed only on DOTA-v2 val, not on AI-TOD-R. Since the paper's central motivation is oriented tiny objects, adding at least one ablation on AI-TOD-R (e.g., the individual effectiveness of CPS/MPS/DGMM and the PCB components) would directly support the causal story for the main dataset.
  4. [Table I] The table lists 'SODA-D' but the text never defines or discusses SODA-D; please add a citation and one sentence of context.
  5. [Section IV-C] The label-efficient benchmark includes only one SAOD method (Co-mining). The conclusion that SSOD outperforms SAOD should be qualified as based on a single SAOD baseline, since other SAOD methods may behave differently.
  6. [Figure 7 caption] The caption contains a run-on sentence beginning 'By separating the model's predictions...' that is not grammatical; please rephrase.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: DCFL is empirically validated and its self-citations are contextual; the 'costless' efficiency claim is an internal inconsistency rather than a circular derivation.

full rationale

The paper's central contribution, DCFL, is a training-time label-assignment scheme, not a derivation of predictions from fitted inputs. The dynamic prior (Eqs. 2 and 8), the coarse CPS construction using GJSD (Eqs. 10-12), and the fine MPS/DGMM refinement (Eqs. 13-14) are all learned or tuned modules; their gains are validated empirically on eight datasets against external baselines. Hyperparameters K, Q, and g are selected by ablation (Table XI), which is standard optimization rather than a fitted parameter being renamed as a prediction. The Gaussian/JSD similarity framework is imported from prior work, including the authors' RFLA [44], but the cited results are not invoked as a uniqueness theorem or as a substitute for validation; the paper's core claim is the empirical improvement, which is measured, not derived. The AI-TOD-R dataset is built on the authors' AI-TOD-v2, but orientation labels come from H2RBox-v2 plus manual refinement, so the new dataset is not circularly defined by the method being tested. No equation in the paper reduces to its own input by construction and no prediction is statistically forced by a prior fit. One non-circular inconsistency should be flagged: the 'Costless improvement' claim in Section I is contradicted by Eq. 2 and Figure 6, where the PCB (a learnable dilated convolution plus DCN offsets) is part of the inference forward pass, while Tables II and IV report identical FLOPs to baseline; this is an efficiency/correctness risk, not a circularity. Several self-citations exist, but none is load-bearing for the accuracy claims, so circularity is minimal.

Assumptions & free parameters 6 free parameters · 4 assumptions · 2 invented entities

The method's performance depends on six hyperparameters and on the Gaussian/JSD framework imported from prior work. The PCB and DGMM are new constructs with no external falsifiable predictions. The dataset is built from the authors' own AI-TOD-v2, which is acknowledged.

free parameters (6)
  • K (coarse positive sample count) = 16
    Number of coarse positive samples per gt; selected by ablation on DOTA-v2 val (Table XI d).
  • Q (medium positive sample count) = 12
    Number of MPS candidates screened by PT score; selected by ablation (Table XI d).
  • g (DGMM threshold exponent) = 0.8
    Threshold e^{-g} for filtering samples by DGMM score; tuned in Table XI e.
  • w_i,1 (DGMM first Gaussian weight) = 0.7
    Weight for geometry-center Gaussian in DGMM; set to 0.7 per Section VI-C.
  • alpha (GJSD weighting) = 0.5
    Balances the two Gaussian components in Eq. 10; set to 0.5 by symmetry.
  • PCB dilation rate = 3
    Dilation rate for context convolution in Prior Capturing Block; set in Section VI-C.
assumptions (4)
  • domain assumption Oriented boxes can be faithfully represented as 2D Gaussians (Eq. 9), making JSD/KLD a valid similarity for sample assignment.
    Imported from GWD (ref [83]) and KLD (ref [70]); no re-derivation for sub-16px objects.
  • domain assumption Generalized Jensen-Shannon Divergence preserves scale-invariance and symmetry for tiny box matching (Eqs. 10-12).
    The paper relies on GJSD properties from Nielsen (ref [87]) and applies them to extremely tiny objects without new analysis.
  • domain assumption The learning bias observed with SOOD at 10% labels (Figure 5) is representative of all detection pipelines.
    The bias analysis in Section IV-D uses a single semi-supervised pipeline; the text generalizes to 'various detection pipelines'.
  • domain assumption The dynamic prior offsets learned by PCB do not require additional inference-time computation or parameters.
    The 'costless' claim implies PCB is training-only or negligible, but the paper does not specify inference-time usage.
invented entities (2)
  • Prior Capturing Block (PCB)
    purpose: Dynamically updates prior positions to better align with tiny objects during training.
    Internal module; no external falsifiable prediction outside the method.
  • Dynamic Gaussian Mixture Model (DGMM)
    purpose: Represents each instance with two Gaussians for fine-grained positive sample filtering.
    Internal representation; no independent empirical handle.

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Cite this review

Pith. "Pith review of Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning." pith.science (2026). https://pith.science/paper/FNIEMRTH

@misc{pith2026241211582,
  author       = {Pith},
  title        = {Pith review of: Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FNIEMRTH}},
  note         = {Machine review of arXiv:2412.11582}
}
read the original abstract

Detecting oriented tiny objects, which are limited in appearance information yet prevalent in real-world applications, remains an intricate and under-explored problem. To address this, we systemically introduce a new dataset, benchmark, and a dynamic coarse-to-fine learning scheme in this study. Our proposed dataset, AI-TOD-R, features the smallest object sizes among all oriented object detection datasets. Based on AI-TOD-R, we present a benchmark spanning a broad range of detection paradigms, including both fully-supervised and label-efficient approaches. Through investigation, we identify a learning bias presents across various learning pipelines: confident objects become increasingly confident, while vulnerable oriented tiny objects are further marginalized, hindering their detection performance. To mitigate this issue, we propose a Dynamic Coarse-to-Fine Learning (DCFL) scheme to achieve unbiased learning. DCFL dynamically updates prior positions to better align with the limited areas of oriented tiny objects, and it assigns samples in a way that balances both quantity and quality across different object shapes, thus mitigating biases in prior settings and sample selection. Extensive experiments across eight challenging object detection datasets demonstrate that DCFL achieves state-of-the-art accuracy, high efficiency, and remarkable versatility. The dataset, benchmark, and code are available at https://chasel-tsui.github.io/AI-TOD-R/.

Figures

Figures reproduced from arXiv: 2412.11582 by the authors.

Figure 1
Figure 1. This paper systemically introduces the challenging task of oriented [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Statistical analysis of the AI-TOD-R. From left to right, we show the dataset’s object size distribution, object angle distribution, object number [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The labelling process of the AI-TOD-R. The coarse labels are [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Visualization of annotations in AI-TOD-R. Compared to AI-TOD-v2, using oriented bounding boxes to represent tiny objects can significantly reduce [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: An illustration of the sample learning bias. SOOD [ [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: An overview of the proposed method. The proposed DCFL learning scheme can be adapted into both one-stage and two-stage detection pipelines [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Visualization analysis of the predicted results. The first row shows the predicted results by the Oriented R-CNN while the second row shows results [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Analysis of the learning bias across different methods. The first [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]

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Reference graph

Works this paper leans on

100 extracted references · 66 canonical work pages

  1. [16]

    Dynamic coarse-to-fine learning for oriented tiny object detection,

    C. Xu, J. Ding, J. Wang, W. Yang, H. Yu, L. Yu, and G.-S. Xia, “Dynamic coarse-to-fine learning for oriented tiny object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , June 2023, pp. 7318–7328

    2023

  2. [1]

    Detecting lesion bounding ellipses with gaussian proposal networks,

    Y . Li, “Detecting lesion bounding ellipses with gaussian proposal networks,” in Machine Learning in Medical Imaging: 10th Interna- tional Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10 . Springer, 2019, pp. 337–344

    2019

  3. [2]

    Detection and tracking meet drones challenge,

    P. Zhu, L. Wen, D. Du, X. Bian, H. Fan, Q. Hu, and H. Ling, “Detection and tracking meet drones challenge,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 11, pp. 7380–7399, 2021

    2021

  4. [3]

    Object detection in aerial images: A large-scale benchmark and challenges,

    J. Ding, N. Xue, G.-S. Xia, X. Bai, W. Yang, M. Y . Yang, S. Belongie, J. Luo, M. Datcu, M. Pelillo et al., “Object detection in aerial images: A large-scale benchmark and challenges,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 11, pp. 7778–7796, 2021

    2021

  5. [4]

    Vehicle perception from satellite,

    B. Zhao, P. Han, and X. Li, “Vehicle perception from satellite,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023

    2023

  6. [5]

    Smart border surveillance system using wireless sensor network and computer vision,

    N. Bhadwal, V . Madaan, P. Agrawal, A. Shukla, and A. Kakran, “Smart border surveillance system using wireless sensor network and computer vision,” in 2019 international conference on Automation, Computational and Technology Management (ICACTM). IEEE, 2019, pp. 183–190

    2019

  7. [6]

    A small- sized object detection oriented multi-scale feature fusion approach with application to defect detection,

    N. Zeng, P. Wu, Z. Wang, H. Li, W. Liu, and X. Liu, “A small- sized object detection oriented multi-scale feature fusion approach with application to defect detection,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–14, 2022

    2022

  8. [7]

    Object detection in 20 years: A survey,

    Z. Zou, K. Chen, Z. Shi, Y . Guo, and J. Ye, “Object detection in 20 years: A survey,” Proceedings of the IEEE , vol. 111, no. 3, pp. 257– 276, 2023

    2023

Show all 100 references
  1. [8]

    Object detection in optical remote sensing images: A survey and a new benchmark,

    K. Li, G. Wan, G. Cheng, L. Meng, and J. Han, “Object detection in optical remote sensing images: A survey and a new benchmark,” ISPRS journal of photogrammetry and remote sensing , vol. 159, pp. 296–307, 2020

    2020

  2. [9]

    Oriented r-cnn for object detection,

    X. Xie, G. Cheng, J. Wang, X. Yao, and J. Han, “Oriented r-cnn for object detection,” in IEEE International Conference on Computer Vision, 2021, pp. 3520–3529

    2021

  3. [10]

    Towards large-scale small object detection: Survey and benchmarks,

    G. Cheng, X. Yuan, X. Yao, K. Yan, Q. Zeng, X. Xie, and J. Han, “Towards large-scale small object detection: Survey and benchmarks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 11, pp. 13 467–13 488, 2023

    2023

  4. [11]

    Microsoft coco: Common objects in context,

    T.-Y . Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll ´ar, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European Conference on Computer Vision. Springer, 2014, pp. 740–755

    2014

  5. [12]

    Tiny object detection in aerial images,

    J. Wang, W. Yang, H. Guo, R. Zhang, and G.-S. Xia, “Tiny object detection in aerial images,” in International Conference on Pattern Recognition, 2021, pp. 3791–3798

    2021

  6. [13]

    Detecting tiny objects in aerial images: A normalized wasserstein distance and a new benchmark,

    C. Xu, J. Wang, W. Yang, H. Yu, L. Yu, and G.-S. Xia, “Detecting tiny objects in aerial images: A normalized wasserstein distance and a new benchmark,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 190, pp. 79–93, 2022. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8,...

    2022

  7. [14]

    DOTA: A large-scale dataset for object detection in aerial images,

    G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang, “DOTA: A large-scale dataset for object detection in aerial images,” in IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 3974–3983

    2018

  8. [15]

    Anchor-free oriented proposal generator for object detection,

    G. Cheng, J. Wang, K. Li, X. Xie, C. Lang, Y . Yao, and J. Han, “Anchor-free oriented proposal generator for object detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2022

    2022

  9. [17]

    Wider face: A face detection benchmark,

    S. Yang, P. Luo, C.-C. Loy, and X. Tang, “Wider face: A face detection benchmark,” in IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 5525–5533

    2016

  10. [18]

    Eurocity persons: A novel benchmark for person detection in traffic scenes,

    M. Braun, S. Krebs, F. Flohr, and D. M. Gavrila, “Eurocity persons: A novel benchmark for person detection in traffic scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, no. 8, pp. 1844–1861, 2019

    2019

  11. [19]

    Scale match for tiny person detection,

    X. Yu, Y . Gong, N. Jiang, Q. Ye, and Z. Han, “Scale match for tiny person detection,” in IEEE Workshops on Applications of Computer Vision, 2020, pp. 1257–1265

    2020

  12. [20]

    Dense tiny object detection: A scene context guided approach and a unified benchmark,

    Z. Zhao, J. Du, C. Li, X. Fang, Y . Xiao, and J. Tang, “Dense tiny object detection: A scene context guided approach and a unified benchmark,” IEEE Transactions on Geoscience and Remote Sensing , 2024

    2024

  13. [21]

    Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds,

    Z. Liu, H. Wang, L. Weng, and Y . Yang, “Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 8, pp. 1074–1078, 2016

    2016

  14. [22]

    Orientation robust object detection in aerial images using deep convolutional neural network,

    H. Zhu, X. Chen, W. Dai, K. Fu, Q. Ye, and J. Jiao, “Orientation robust object detection in aerial images using deep convolutional neural network,” in IEEE International Conference on Image Processing , 2015, pp. 3735–3739

    2015

  15. [23]

    Vehicle detection in aerial imagery: A small target detection benchmark,

    S. Razakarivony and F. Jurie, “Vehicle detection in aerial imagery: A small target detection benchmark,” Journal of Visual Communication and Image Representation , vol. 34, pp. 187–203, 2016

    2016

  16. [24]

    Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery,

    X. Sun, P. Wang, Z. Yan, F. Xu, R. Wang, W. Diao, J. Chen, J. Li, Y . Feng, T. Xu et al., “Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 184, pp. 116– 130, 2022

    2022

  17. [25]

    Sar ship detection dataset (ssdd): Official release and comprehensive data analysis,

    T. Zhang, X. Zhang, J. Li, X. Xu, B. Wang, X. Zhan, Y . Xu, X. Ke, T. Zeng, H. Su et al., “Sar ship detection dataset (ssdd): Official release and comprehensive data analysis,” Remote Sensing, vol. 13, no. 18, p. 3690, 2021

    2021

  18. [26]

    Icdar 2015 competition on robust reading,

    D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. Ghosh, A. Bagdanov, M. Iwamura, J. Matas, L. Neumann, V . R. Chandrasekhar, S. Lu et al., “Icdar 2015 competition on robust reading,” in 2015 13th international conference on document analysis and recognition (ICDAR) . IEEE, 2015,...

    2015

  19. [27]

    Precise detection in densely packed scenes,

    E. Goldman, R. Herzig, A. Eisenschtat, J. Goldberger, and T. Hassner, “Precise detection in densely packed scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 5227–5236

    2019

  20. [28]

    The devil is in the crack orientation: A new perspective for crack detection,

    Z. Chen, J. Zhang, Z. Lai, G. Zhu, Z. Liu, J. Chen, and J. Li, “The devil is in the crack orientation: A new perspective for crack detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 6653–6663

    2023

  21. [29]

    Arbitrary-oriented scene text detection via rotation proposals,

    J. Ma, W. Shao, H. Ye, L. Wang, H. Wang, Y . Zheng, and X. Xue, “Arbitrary-oriented scene text detection via rotation proposals,” IEEE Transactions on Multimedia , vol. 20, no. 11, pp. 3111–3122, 2018

    2018

  22. [30]

    Learning roi transformer for detecting oriented objects in aerial images,

    J. Ding, N. Xue, Y . Long, G.-S. Xia, and Q. Lu, “Learning roi transformer for detecting oriented objects in aerial images,” in IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2849–2858

    2019

  23. [31]

    R3det: Refined single-stage detector with feature refinement for rotating object,

    X. Yang, Q. Liu, J. Yan, A. Li, Z. Zhang, and G. Yu, “R3det: Refined single-stage detector with feature refinement for rotating object,” CoRR, vol. abs/arXiv:1908.05612, 2019. [Online]. Available: https://arxiv.org/abs/1908.05612

    1908 arXiv

  24. [32]

    Align deep features for ori- ented object detection,

    J. Han, J. Ding, J. Li, and G.-S. Xia, “Align deep features for ori- ented object detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–11, 2021

    2021

  25. [33]

    Fcosr: A simple anchor-free rotated detector for aerial object detection,

    Z. Li, B. Hou, Z. Wu, L. Jiao, B. Ren, and C. Yang, “Fcosr: A simple anchor-free rotated detector for aerial object detection,” arXiv preprint arXiv:2111.10780, 2021

    2021 arXiv

  26. [34]

    Oriented reppoints for aerial object detection,

    W. Li, Y . Chen, K. Hu, and J. Zhu, “Oriented reppoints for aerial object detection,” in IEEE Conference on Computer Vision and Pattern Recognition, 2022, pp. 1829–1838

    2022

  27. [35]

    Ars-detr: Aspect ratio- sensitive detection transformer for aerial oriented object detection,

    Y . Zeng, Y . Chen, X. Yang, Q. Li, and J. Yan, “Ars-detr: Aspect ratio- sensitive detection transformer for aerial oriented object detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–15, 2024

    2024

  28. [36]

    Probabilistic anchor assignment with iou prediction for object detection,

    K. Kim and H. S. Lee, “Probabilistic anchor assignment with iou prediction for object detection,” in European Conference on Computer Vision. Springer, 2020, pp. 355–371

    2020

  29. [37]

    Ota: Optimal transport assignment for object detection,

    Z. Ge, S. Liu, Z. Li, O. Yoshie, and J. Sun, “Ota: Optimal transport assignment for object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 303–312

    2021

  30. [38]

    Iqdet: Instance-wise quality distribu- tion sampling for object detection,

    Y . Ma, S. Liu, Z. Li, and J. Sun, “Iqdet: Instance-wise quality distribu- tion sampling for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1717–1725

    2021

  31. [39]

    Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,

    S. Zhang, C. Chi, Y . Yao, Z. Lei, and S. Z. Li, “Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,” in IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 9759–9768

    2020

  32. [40]

    Dynamic anchor learning for arbitrary-oriented object detection,

    Q. Ming, Z. Zhou, L. Miao, H. Zhang, and L. Li, “Dynamic anchor learning for arbitrary-oriented object detection,” in AAAI Conference on Artificial Intelligence , vol. 35, no. 3, 2021, pp. 2355–2363

    2021

  33. [41]

    Shape-adaptive selection and measurement for oriented object detection,

    L. Hou, K. Lu, J. Xue, and Y . Li, “Shape-adaptive selection and measurement for oriented object detection,” in AAAI Conference on Artificial Intelligence, 2022

    2022

  34. [42]

    A general gaussian heatmap label assignment for arbitrary-oriented object detection,

    Z. Huang, W. Li, X.-G. Xia, and R. Tao, “A general gaussian heatmap label assignment for arbitrary-oriented object detection,” IEEE Trans- actions on Image Processing , vol. 31, pp. 1895–1910, 2022

    1910

  35. [43]

    Reppoints: Point set representation for object detection,

    Z. Yang, S. Liu, H. Hu, L. Wang, and S. Lin, “Reppoints: Point set representation for object detection,” in IEEE International Conference on Computer Vision , 2019, pp. 9657–9666

    2019

  36. [44]

    Rfla: Gaussian receptive field based label assignment for tiny object detection,

    C. Xu, J. Wang, W. Yang, H. Yu, L. Yu, and G.-S. Xia, “Rfla: Gaussian receptive field based label assignment for tiny object detection,” in European Conference on Computer Vision . Springer, 2022, pp. 526– 543

    2022

  37. [45]

    Small object detection via coarse-to-fine proposal generation and imitation learning,

    X. Yuan, G. Cheng, K. Yan, Q. Zeng, and J. Han, “Small object detection via coarse-to-fine proposal generation and imitation learning,” in IEEE International Conference on Computer Vision, 2023, pp. 6317– 6327

    2023

  38. [46]

    Perceptual generative adversarial networks for small object detection,

    J. Li, X. Liang, Y . Wei, T. Xu, J. Feng, and S. Yan, “Perceptual generative adversarial networks for small object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1222–1230

    2017

  39. [47]

    Sod-mtgan: Small object detection via multi-task generative adversarial network,

    Y . Bai, Y . Zhang, M. Ding, and B. Ghanem, “Sod-mtgan: Small object detection via multi-task generative adversarial network,” in European Conference on Computer Vision . Springer, 2018, pp. 206–221

    2018

  40. [48]

    Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection,

    J. Noh, W. Bae, W. Lee, J. Seo, and G. Kim, “Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection,” in IEEE International Conference on Computer Vision, 2019, pp. 9725–9734

    2019

  41. [49]

    Small object detection in remote sensing images based on super-resolution with auxiliary generative adversarial networks,

    L. Courtrai, M.-T. Pham, and S. Lef `evre, “Small object detection in remote sensing images based on super-resolution with auxiliary generative adversarial networks,” Remote Sensing , vol. 12, no. 19, p. 3152, 2020

    2020

  42. [50]

    Small object detection in remote sensing images with residual feature aggregation-based super-resolution and object detector network,

    S. M. A. Bashir and Y . Wang, “Small object detection in remote sensing images with residual feature aggregation-based super-resolution and object detector network,” Remote Sensing, vol. 13, no. 9, p. 1854, 2021

    2021

  43. [51]

    Small- object detection in remote sensing images with end-to-end edge- enhanced gan and object detector network,

    J. Rabbi, N. Ray, M. Schubert, S. Chowdhury, and D. Chao, “Small- object detection in remote sensing images with end-to-end edge- enhanced gan and object detector network,” Remote Sensing , vol. 12, no. 9, p. 1432, 2020

    2020

  44. [52]

    Dot distance for tiny object detection in aerial images,

    C. Xu, J. Wang, W. Yang, and L. Yu, “Dot distance for tiny object detection in aerial images,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021, pp. 1192–1201

    2021

  45. [53]

    A normalized gaus- sian wasserstein distance for tiny object detection,

    J. Wang, C. Xu, W. Yang, and L. Yu, “A normalized gaus- sian wasserstein distance for tiny object detection,” arXiv preprint arXiv:2110.13389, 2021

    2021 arXiv

  46. [54]

    Kldet: Detecting tiny objects in remote sensing images via kullback-leibler divergence,

    Z. Zhou and Y . Zhu, “Kldet: Detecting tiny objects in remote sensing images via kullback-leibler divergence,” IEEE Transactions on Geo- science and Remote Sensing , 2024

    2024

  47. [55]

    Generalized intersection over union: A metric and a loss for bounding box regression,

    H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized intersection over union: A metric and a loss for bounding box regression,” in IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 658–666. JOURNAL OF LATEX CLASS FILES, VOL...

    2019

  48. [56]

    Distance-iou loss: Faster and better learning for bounding box regression,

    Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, “Distance-iou loss: Faster and better learning for bounding box regression,” in AAAI Conference on Artificial Intelligence, vol. 34, no. 07, 2020, pp. 12 993– 13 000

    2020

  49. [57]

    Unitbox: An advanced object detection network,

    J. Yu, Y . Jiang, Z. Wang, Z. Cao, and T. Huang, “Unitbox: An advanced object detection network,” 2016, pp. 516–520

    2016

  50. [58]

    Detecting rotated objects as gaussian distributions and its 3-d generalization,

    X. Yang, G. Zhang, X. Yang, Y . Zhou, W. Wang, J. Tang, T. He, and J. Yan, “Detecting rotated objects as gaussian distributions and its 3-d generalization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, pp. 4335–4354, 2023

    2023

  51. [59]

    Phase-shifting coder: Predicting accurate orientation in oriented object detection,

    Y . Yu and F. Da, “Phase-shifting coder: Predicting accurate orientation in oriented object detection,” in IEEE Conference on Computer Vision and Pattern Recognition, 2023, pp. 13 354–13 363

    2023

  52. [60]

    Object detection in optical remote sensing images: A survey and a new benchmark,

    K. Li, G. Wan, G. Cheng, L. Meng, and J. Han, “Object detection in optical remote sensing images: A survey and a new benchmark,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 159, pp. 296–307, 2020

    2020

  53. [61]

    Visdrone-det2019: The vision meets drone object detection in image challenge results,

    D. Du, P. Zhu, L. Wen, and et al., “Visdrone-det2019: The vision meets drone object detection in image challenge results,” in IEEE International Conference on Computer Vision Workshops , 2019, pp. 213–226

    2019

  54. [62]

    xview: Objects in context in overhead imagery,

    D. Lam, R. Kuzma, K. McGee, S. Dooley, M. Laielli, M. Klaric, Y . Bulatov, and B. McCord, “xview: Objects in context in overhead imagery,” arXiv preprint arXiv:1802.07856 , 2018

    2018 arXiv

  55. [63]

    H2rbox: Horizontal box annotation is all you need for oriented object detection,

    X. Yang, G. Zhang, W. Li, Y . Zhou, X. Wang, and J. Yan, “H2rbox: Horizontal box annotation is all you need for oriented object detection,” in The Eleventh International Conference on Learning Representations, 2022

    2022

  56. [64]

    H2rbox- v2: Incorporating symmetry for boosting horizontal box supervised oriented object detection,

    Y . Yu, X. Yang, Q. Li, Y . Zhou, F. Da, and J. Yan, “H2rbox- v2: Incorporating symmetry for boosting horizontal box supervised oriented object detection,” Advances in Neural Information Processing Systems, vol. 36, 2024

    2024

  57. [65]

    Sood: Towards semi-supervised oriented object detection,

    W. Hua, D. Liang, J. Li, X. Liu, Z. Zou, X. Ye, and X. Bai, “Sood: Towards semi-supervised oriented object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2023, pp. 15 558–15 567

    2023

  58. [66]

    Focal loss for dense object detection,

    T.-Y . Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, “Focal loss for dense object detection,” in IEEE International Conference on Computer Vision, 2017, pp. 2980–2988

    2017

  59. [67]

    Fcos: A simple and strong anchor-free object detector,

    Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: A simple and strong anchor-free object detector,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 4, pp. 1922–1933, 2022

    1922

  60. [68]

    Faster R-CNN: Towards real- time object detection with region proposal networks,

    S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real- time object detection with region proposal networks,” in Advances in Neural Information Processing Systems , 2015, pp. 91–99

    2015

  61. [69]

    Deformable detr: Deformable transformers for end-to-end object detection,

    X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable detr: Deformable transformers for end-to-end object detection,” in International Conference on Learning Representations , 2021

    2021

  62. [70]

    Learning high-precision bounding box for rotated object detection via kullback-leibler divergence,

    X. Yang, X. Yang, J. Yang, Q. Ming, W. Wang, Q. Tian, and J. Yan, “Learning high-precision bounding box for rotated object detection via kullback-leibler divergence,” Advances in Neural Information Process- ing Systems, vol. 34, pp. 18 381–18 394, 2021

    2021

  63. [71]

    The kfiou loss for rotated object detection,

    X. Yang, Y . Zhou, G. Zhang, J. Yang, W. Wang, J. Yan, X. ZHANG, and Q. Tian, “The kfiou loss for rotated object detection,” in The Eleventh International Conference on Learning Representations , 2022

    2022

  64. [72]

    Gliding vertex on the horizontal bounding box for multi-oriented object detection,

    Y . Xu, M. Fu, Q. Wang, Y . Wang, K. Chen, G.-S. Xia, and X. Bai, “Gliding vertex on the horizontal bounding box for multi-oriented object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 4, pp. 1452–1459, 2021

    2021

  65. [73]

    Beyond bounding- box: Convex-hull feature adaptation for oriented and densely packed object detection,

    Z. Guo, C. Liu, X. Zhang, J. Jiao, X. Ji, and Q. Ye, “Beyond bounding- box: Convex-hull feature adaptation for oriented and densely packed object detection,” in IEEE Conference on Computer Vision and Pattern Recognition, 2021, pp. 8792–8801

    2021

  66. [74]

    Redet: A rotation-equivariant detector for aerial object detection,

    J. Han, J. Ding, N. Xue, and G.-S. Xia, “Redet: A rotation-equivariant detector for aerial object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 2786–2795

    2021

  67. [75]

    Swin transformer: Hierarchical vision transformer using shifted win- dows,

    Z. Liu, Y . Lin, Y . Cao, H. Hu, Y . Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted win- dows,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10 012–10 022

    2021

  68. [76]

    Unbiased teacher for semi- supervised object detection,

    Y .-C. Liu, C.-Y . Ma, Z. He, C.-W. Kuo, K. Chen, P. Zhang, B. Wu, Z. Kira, and P. Vajda, “Unbiased teacher for semi- supervised object detection,” in International Conference on Learning Representations, 2021. [Online]. Available: https://openreview.net/ forum?id=MJIve1zgR

    2021

  69. [77]

    End-to-end semi-supervised object detection with soft teacher,

    M. Xu, Z. Zhang, H. Hu, J. Wang, L. Wang, F. Wei, X. Bai, and Z. Liu, “End-to-end semi-supervised object detection with soft teacher,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 3060–3069

    2021

  70. [78]

    Co-mining: Self-supervised learning for sparsely annotated object detection,

    T. Wang, T. Yang, J. Cao, and X. Zhang, “Co-mining: Self-supervised learning for sparsely annotated object detection,” in AAAI Conference on Artificial Intelligence , vol. 35, no. 4, 2021, pp. 2800–2808

    2021

  71. [79]

    Au- toassign: Differentiable label assignment for dense object detection,

    B. Zhu, J. Wang, Z. Jiang, F. Zong, S. Liu, Z. Li, and J. Sun, “Au- toassign: Differentiable label assignment for dense object detection,” arXiv preprint arXiv:2007.03496 , 2020

    2007 arXiv

  72. [80]

    Sparse r-cnn: End-to-end object detection with learnable proposals,

    P. Sun, R. Zhang, Y . Jiang, T. Kong, C. Xu, W. Zhan, M. Tomizuka, L. Li, Z. Yuan, C. Wang, and P. Luo, “Sparse r-cnn: End-to-end object detection with learnable proposals,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 454–14 463

    2021

  73. [81]

    De- formable convolutional networks,

    J. Dai, H. Qi, Y . Xiong, Y . Li, G. Zhang, H. Hu, and Y . Wei, “De- formable convolutional networks,” in IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 764–773

    2017

  74. [82]

    End-to-end object detection with transformers,

    N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision . Springer, 2020, pp. 213– 229

    2020

  75. [83]

    Rethink- ing rotated object detection with gaussian wasserstein distance loss,

    X. Yang, J. Yan, Q. Ming, W. Wang, X. Zhang, and Q. Tian, “Rethink- ing rotated object detection with gaussian wasserstein distance loss,” in International Conference on Machine Learning , vol. 139, 2021, pp. 11 830–11 841

    2021

  76. [84]

    Objectbox: From centers to boxes for anchor-free object detection,

    M. Zand, A. Etemad, and M. Greenspan, “Objectbox: From centers to boxes for anchor-free object detection,” in European Conference on Computer Vision, 2022, pp. 390–406

    2022

  77. [85]

    Feature selective anchor-free module for single-shot object detection,

    C. Zhu, Y . He, and M. Savvides, “Feature selective anchor-free module for single-shot object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 840–849

    2019

  78. [86]

    A new metric for probability distributions,

    D. M. Endres and J. E. Schindelin, “A new metric for probability distributions,” IEEE Transactions on Information Theory (TIT), vol. 49, no. 7, pp. 1858–1860, 2003

    2003

  79. [87]

    On a generalization of the jensen–shannon divergence and the jensen–shannon centroid,

    F. Nielsen, “On a generalization of the jensen–shannon divergence and the jensen–shannon centroid,” Entropy, vol. 22, no. 2, p. 221, 2020

    2020

  80. [88]

    Learning center probability map for detecting objects in aerial images,

    J. Wang, W. Yang, H.-c. Li, H. Zhang, and G.-S. Xia, “Learning center probability map for detecting objects in aerial images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 5, pp. 4307–4323, 2021

    2021

  81. [89]

    Dual-aligned oriented detector,

    G. Cheng, Y . Yao, S. Li, K. Li, X. Xie, J. Wang, X. Yao, and J. Han, “Dual-aligned oriented detector,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2022

    2022

  82. [90]

    Multi-oriented object detection in aerial images with double horizontal rectangles,

    G. Nie and H. Huang, “Multi-oriented object detection in aerial images with double horizontal rectangles,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, pp. 4932–4944, 2023

    2023

  83. [91]

    Mask R-CNN,

    K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN,” in IEEE International Conference on Computer Vision , 2017, pp. 2961– 2969

    2017

  84. [92]

    Hybrid task cascade for instance segmentation,

    K. Chen, J. Pang, J. Wang, Y . Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang et al. , “Hybrid task cascade for instance segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 4974–4983

    2019

  85. [94]

    Mmrotate: A rotated object detection benchmark using pytorch,

    Y . Zhou, X. Yang, G. Zhang, J. Wang, Y . Liu, L. Hou, X. Jiang, X. Liu, J. Yan, C. Lyu et al., “Mmrotate: A rotated object detection benchmark using pytorch,” in Proceedings of the 30th ACM International Confer- ence on Multimedia , 2022, pp. 7331–7334

    2022

  86. [95]

    Pytorch: An imperative style, high-performance deep learning library,

    A. Paszke, S. Gross, F. Massa, A. Lerer et al., “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems , 2019, pp. 8024–8035

    2019

  87. [96]

    Imagenet large scale visual recognition challenge,

    O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015

    2015

  88. [97]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778

    2016

  89. [98]

    Feature pyramid networks for object detection,

    T.-Y . Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2117–2125

    2017

  90. [99]

    FCOS: Fully convolutional one- stage object detection,

    Z. Tian, C. Shen, H. Chen, and T. He, “FCOS: Fully convolutional one- stage object detection,” in IEEE International Conference on Computer Vision, 2019, pp. 9627–9636

    2019

  91. [100]

    Deformable convnets v2: More deformable, better results,

    X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” in IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 9308–9316. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 18 Chang Xu received his B.S. degree in e...

    2019

  92. [2019]

    Available: https://arxiv.org/abs/1906.07155

    [Online]. Available: https://arxiv.org/abs/1906.07155

    1906 arXiv

Pith tools

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