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REVIEW 3 major objections 4 minor 98 references

Open-Vocabulary Object Detection in UAV Imagery: A Review and Future Perspectives

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Open-vocabulary object detection in drone imagery can be organized into pseudo-labeling and CLIP-driven integration families, and the field's main obstacle is the lack of a standard benchmark.

desk verdict A useful but sloppy survey of UAV open-vocabulary detection; the dataset overview and benchmark diagnosis are worth having, but the two-family taxonomy is undercut by the paper's own figure, and the comparison table mixes incompatible metrics. read the letter →

arxiv 2507.13359 v1 pith:2OZZFMVT submitted 2025-07-04 cs.CV

Yang Zhou , Junjie Li , CongYang Ou , Dawei Yan , Haokui Zhang , Xizhe Xue This is my paper
classification cs.CV
keywords Open-vocabularyobjectdetectionUAVimageryAerialVision-languagemodelsCLIPPseudo-labelingRemotesensingSurvey
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

This review argues that open-vocabulary object detection (OVOD) is the next step for drone perception, because it lets an aerial system search for objects described in natural language without retraining on new categories. The paper's central contribution is a taxonomy that sorts existing aerial OVOD methods into two families: pseudo-labeling methods, which use a teacher-student loop to label unlabeled drone images, and CLIP-driven integration methods, which fuse text-image alignment directly into the detector. It reviews representative systems in each family, surveys the datasets used to train and test them, and lays out a research agenda of challenges from the ground-to-aerial domain gap and small objects to prompt ambiguity and missing benchmarks. The survey matters because it maps a rapidly growing field and identifies the missing standardized benchmark as the main obstacle to fair progress.

What carries the argument

The organizing device is the taxonomy itself: the two-way split between pseudo-labeling methods and CLIP-driven integration methods, anchored in the contrastive image-text alignment learned by CLIP. Pseudo-labeling methods rely on a teacher-student flywheel in which an external vision-language model labels proposals that a student detector learns from, while CLIP-driven methods reframe detection as region-text grounding inside a single network. The comparison rests on the standard OVOD evaluation setup of separate mAP on base and novel classes combined into a harmonic mean, which is the metric that exposes the claimed novel-class advantage of pseudo-labeling.

What would settle it

Running CastDet, OVA-DETR, and DescReg under one shared base/novel split on DOTA v1.0 with the same evaluation code would settle the family comparison; if a CLIP-driven method then matches or exceeds CastDet on novel mAP, the paper's central comparative claim collapses. Alternatively, identifying an aerial OVOD method from the same period that fits neither the pseudo-labeling nor the CLIP-driven family would falsify the taxonomy's exhaustiveness.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that the literature on open-vocabulary object detection in UAV imagery can be organized by a single dichotomy: methods that generate pseudo-labels for unlabeled aerial data (CastDet and the MLLM-based discovery pipeline) versus methods that integrate CLIP-style vision-language alignment into the detector itself (OVA-Det, LAE-DINO, OpenRSD, LLaMA-Unidetector, and DescReg). It further claims that under the mAP-based protocol on DIOR and DOTA v1.0, pseudo-labeling methods such as CastDet generalize better to novel classes, while CLIP-driven methods such as DescReg retain higher base-class accuracy. The paper presents this as a trade-off, and it closes by arguing that the field's most urgent need is a community-standard UAV-OVOD benchmark with agreed base/novel splits and evaluation metrics.

Load-bearing premise

The paper's claim that pseudo-labeling methods generally beat CLIP-driven methods on novel classes depends on numbers collected from different papers, datasets, base/novel splits, and evaluation criteria being comparable, something the paper itself concedes is not yet the case.

Editorial extensions

If this is right

  • If the taxonomy is right, future aerial OVOD work can be positioned by which family it extends, making method comparison and combination more systematic.
  • The claimed novel-class advantage of pseudo-labeling would motivate hybrid designs that add CLIP-driven alignment on top of teacher-student labeling.
  • The paper's challenge list implies that solving the domain gap and small-object alignment are prerequisites for real deployment, not optional refinements.
  • The absence of a standard benchmark means that reported numbers across papers cannot yet be compared fairly, so a shared base/novel split on DOTA or DIOR would be a concrete first step.

Reading between the lines

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

  • The family-level comparison in Section 3.3 is weaker than it looks: the mAP rows mix different base/novel splits, and the AP50 rows use a different metric, so the novel-class ordering rests on very few contemporaneous methods and a unified protocol could reverse it.
  • The taxonomy may not stay exhaustive: methods that combine pseudo-labeling with end-to-end fusion are foreseeable, and the dichotomy may need to become a spectrum as the field grows.
  • The datasets built by automatic label engines (LAE-1M, ORSD+, MI-OAD) suggest that the next bottleneck will be label quality and vocabulary coverage, which the survey's challenge list does not yet foreground.
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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

3 major / 4 minor

Summary. This manuscript is a survey of open-vocabulary object detection (OVOD) applied to UAV/aerial imagery. It motivates the topic, reviews background material on traditional detection and CLIP-based alignment, proposes a taxonomy that separates methods into pseudo-labeling and CLIP-driven integration, describes six representative methods, compiles relevant datasets and evaluation metrics, and concludes with challenges and future directions. The paper is positioned as the first comprehensive survey in this specific niche.

Significance. If the manuscript were revised to fix its internal inconsistencies, it would be a useful entry point for researchers entering the intersection of OVOD and UAV perception: it collects recent methods, datasets, and open problems in one place, and it correctly identifies the absence of a standardized aerial OVOD benchmark as a key community gap. The descriptive portions of the survey are broadly faithful to the cited works, and the dataset table in Section 4.2 is a useful reference. However, the paper's two main intellectual contributions—the taxonomy in Section 3 and the comparative conclusions in Section 3.3—are currently undercut by an internal inconsistency in the taxonomy and by a comparison table that the text itself says is not protocol-comparable. These issues are central to the survey's organization and claims, so they require substantive revision rather than copyediting.

major comments (3)
  1. [Section 3, Figure 4] The central taxonomy is internally inconsistent as written. The text says that existing methods are divided into two major types, Pseudo-Labeling and CLIP-driven integration, but Figure 4 displays four sub-branches: End-to-End Vision Language Fusion, Semi-Supervised Pseudo-Labeling, Decoupled Recognition with MLLMs, and Representation Regularization. LLaMA-Unidetector is discussed in Section 3.2 as a CLIP-driven integration method even though the text explicitly describes its recognition stage as TerraOV-LLM, an MLLM, not CLIP; DescReg is also placed in Section 3.2 even though the text says it 'does not propose a new detector architecture' and regularizes the embedding space with visual descriptions. Either the taxonomy should be expanded to the four families shown in Figure 4, or the definition of 'CLIP-driven integration' should be made precise enough that an MLLM-decoupled pipeline and a representation-regularization method are genuinely instances of it. As presented, the two-family claim does not organize the surveyed methods, and this weakens the family-level comparison in Section 3.3.
  2. [Section 3.3, Table 1] The comparative conclusion that 'pseudo-labeling-based algorithms generally achieve superior performance on novel classes' is not supported by the evidence in Table 1. The table mixes mAP-based evaluations with AP50-based evaluations, and the text itself states that 'these scores are not directly comparable to the mAP-based results due to the differing evaluation criteria.' The mAP rows do not state the base/novel split definitions used by each source paper, and the novel-class comparison driving the narrative is based on two contemporaneous methods, DescReg and CastDet, plus one later method, OVA-DETR. In addition, the AP50 rows are incomplete for the dataset columns, with dashes for LAE-DINO on DOTA and OPEN-RSD on DIOR, making those rows difficult to read. The family-level claim should either be removed or restricted to a direct comparison under a single, explicitly described evaluation protocol; at minimum, the table should report the split definition and metric for each method rather than mixing protocols.
  3. [Section 3.2 and Section 4.2] The manuscript does not disclose that two of the works it assesses are co-authored by the survey's authors: OVA-DETR [49] includes H. Zhang, and MI-OAD [81] includes X. Xue. Because the survey makes favorable comparative statements about OVA-DETR in Section 3.2 and about MI-OAD in Section 4.2, this overlap is material to a reader's ability to calibrate the evaluation. I am not suggesting misconduct, but a survey with comparative conclusions about the authors' own methods should include a conflict-of-interest or author-contribution statement, and ideally should separate the description of those works from the comparison section.
minor comments (4)
  1. [Throughout] There are numerous language and typographical errors, including 'an indispensable tools,' 'application scenarios have extend from,' 'UA V' spacing, 'Figure 3)(a),' and 'OVOD UVA' in Figure 4; a thorough proofreading pass is needed.
  2. [Section 4.2, Table 2] The text discusses the STAR dataset [82] but STAR is omitted from the dataset statistics in Table 2; adding it would make the table consistent with the narrative.
  3. [Section 1] The claim that this is 'the first comprehensive survey' would be easier to evaluate if the manuscript described its search and inclusion criteria; without such a statement, the completeness claim is hard to verify.
  4. [Section 2.2] The illustration text in Figure 2 repeats 'An image of Parking Lot' four times; this is presumably a placeholder and should be cleaned up.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the survey contains no derived prediction that reduces to its inputs; two self-authored works are cited, but the taxonomy and challenge analysis do not depend on them by construction.

full rationale

This is a review article, so the claimed 'derivations' are organizational rather than mathematical. Section 3 proposes a two-family taxonomy ('Pseudo-Labeling Methods' and 'CLIP-driven integrations methods'), while Figure 4 actually lists four sub-branches and the Section 3.2 text places LLaMA-Unidetector and DescReg in the CLIP-driven family despite describing them as MLLM-decoupled and representation-regularization. That is an internal inconsistency affecting the survey's central organization, but it is a classification error, not a circular reduction: no equation or fitted parameter makes the taxonomy equivalent to its inputs. Similarly, Table 1 mixes mAP-based and AP50-based results across sources; the paper concedes 'these scores are not directly comparable to the mAP-based results due to the differing evaluation criteria,' so the family comparison is a benchmarking weakness, not a fitted-input-called-prediction. The only self-citation concern is that OVA-DETR (ref. 49, co-authored by H. Zhang) and MI-OAD (ref. 81, co-authored by X. Xue and H. Zhang) are discussed favorably; however, the taxonomy, dataset review, and challenge agenda do not reduce to those two citations, and the survey's structure is carried by external works such as CastDet, LAE-DINO, OpenRSD, and standard datasets. No step meets the standard of 'Eq. X = Eq. Y by construction' or a fitted parameter renamed as a prediction, so there is no significant circularity. Score 2 reflects the presence of minor, non-load-bearing self-citations, not a circular derivation.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey introduces no fitted parameters, no new quantities, and no invented entities; it organizes existing methods and datasets. The load-bearing premises are framing assumptions: exhaustiveness of the Section 3 taxonomy, comparability of the heterogeneous results in Table 1, and the suitability of CLIP-style alignment for aerial imagery after adaptation. One factual error sits in the dataset ledger: Table 2's VisDrone row points to reference [74], which is actually Bansal et al.'s zero-shot detection paper. No postulates beyond the cited literature are introduced.

assumptions (3)
  • domain assumption The two-family taxonomy (pseudo-labeling, CLIP-driven integration) exhaustively partitions current aerial OVOD methods.
    Section 3 introduces the taxonomy without a systematic literature search or coverage analysis; completeness is asserted, not demonstrated.
  • domain assumption Mixed-protocol reported numbers (Table 1) can support family-level performance conclusions.
    Section 3.3 compares methods across papers, datasets, splits, and metric types (mAP vs. AP50), relying on the comparability that Section 4.2 admits is not yet standardized.
  • domain assumption CLIP-style cross-modal alignment is the appropriate substrate for aerial open-vocabulary detection, given domain adaptation.
    Sections 2.2 and 6 assume the VLM embedding space learned from ground-level web data can be adapted (via PEFT, secondary pretraining) to top-down views; the survey does not verify this empirically.

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Cite this review

Pith. "Pith review of Open-Vocabulary Object Detection in UAV Imagery: A Review and Future Perspectives." pith.science (2026). https://pith.science/paper/2OZZFMVT

@misc{pith2026250713359,
  author       = {Pith},
  title        = {Pith review of: Open-Vocabulary Object Detection in UAV Imagery: A Review and Future Perspectives},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2OZZFMVT}},
  note         = {Machine review of arXiv:2507.13359}
}
read the original abstract

Due to its extensive applications, aerial image object detection has long been a hot topic in computer vision. In recent years, advancements in Unmanned Aerial Vehicles (UAV) technology have further propelled this field to new heights, giving rise to a broader range of application requirements. However, traditional UAV aerial object detection methods primarily focus on detecting predefined categories, which significantly limits their applicability. The advent of cross-modal text-image alignment (e.g., CLIP) has overcome this limitation, enabling open-vocabulary object detection (OVOD), which can identify previously unseen objects through natural language descriptions. This breakthrough significantly enhances the intelligence and autonomy of UAVs in aerial scene understanding. This paper presents a comprehensive survey of OVOD in the context of UAV aerial scenes. We begin by aligning the core principles of OVOD with the unique characteristics of UAV vision, setting the stage for a specialized discussion. Building on this foundation, we construct a systematic taxonomy that categorizes existing OVOD methods for aerial imagery and provides a comprehensive overview of the relevant datasets. This structured review enables us to critically dissect the key challenges and open problems at the intersection of these fields. Finally, based on this analysis, we outline promising future research directions and application prospects. This survey aims to provide a clear road map and a valuable reference for both newcomers and seasoned researchers, fostering innovation in this rapidly evolving domain. We keep tracing related works at https://github.com/zhouyang2002/OVOD-in-UVA-imagery

Figures

Figures reproduced from arXiv: 2507.13359 by the authors.

Figure 1
Figure 1. Comparison between traditional close-set detection, few shot detection and open vocabulary detection. (a) UAV image object detection; (b) Traditional close-set detection. All test categories have appeared in the training set; (c) Few shot detection. Part test categories have only a few training samples; (d) Open vocabulary detection. Some categories have never been seen in the training set, only their names are prov… view at source ↗
Figure 2
Figure 2. Illustration of how CLIP is used in open vocabulary object detection. Region features extracted from the image are matched against category embeddings generated from textual prompts, enabling category expansion and unsupervised detection. entire pipeline, from data collection and annotation to model retraining and deployment, must be repeated. This process is not only resource-intensive and time-consuming, but also … view at source ↗
Figure 3
Figure 3. Comparison of open-vocabulary object detection strategies. (a) Pseudo-Labeling Based Methods. (b) CLIP-driven Integration Methods. it ascends. This multi-scale challenge requires a detector to be robustly invariant to scale. For OVOD, this means the visual encoder must produce consistent embeddings for the same object category across a wide range of resolutions, a non-trivial requirement that pushes the limits of st… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Several typical open-vocabulary object detection methods and their representative works in UAV imagery. 3.1. Pseudo-Labeling Based Methods This class of methods addresses the critical bottleneck of limited annotated data in the aerial domain. As illustrated in [PITH_F…
Figure 5
Figure 5. Figure 5: Interaction between human operators and drones. a "heat source" or "a warm object with a human-like shape." This requires developing novel fusion architectures. Instead of simple early or late fusion, sophisticated cross-modal attention mechanisms could allow features …

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Works this paper leans on

98 extracted references · 36 canonical work pages

  1. [49]

    OVA-DETR: Open vocabulary aerial object detection using image-text alignment and fusion

    Wei, G.; Yuan, X.; Liu, Y.; Shang, Z.; Yao, K.; Li, C.; Yan, Q.; Zhao, C.; Zhang, H.; Xiao, R. OVA-DETR: Open vocabulary aerial object detection using image-text alignment and fusion. arXiv preprint arXiv:2408.12246 2024

    arXiv 2024

  2. [81]

    From Word to Sentence: A Large-Scale Multi-Instance Dataset for Open-Set Aerial Detection.arXiv preprint arXiv:2505.03334 2025

    Wei, G.; Liu, Y.; Yuan, X.; Xue, X.; Guo, L.; Yang, Y.; Zhao, C.; Bai, Z.; Zhang, H.; Xiao, R. From Word to Sentence: A Large-Scale Multi-Instance Dataset for Open-Set Aerial Detection.arXiv preprint arXiv:2505.03334 2025

    work page arXiv 2025

  3. [1]

    State-of-the-art and future research challenges in uav swarms

    Javed, S.; Hassan, A.; Ahmad, R.; Ahmed, W.; Ahmed, R.; Saadat, A.; Guizani, M. State-of-the-art and future research challenges in uav swarms. IEEE Internet of Things Journal 2024, 11, 19023–19045

    2024

  4. [2]

    A survey on channel sounding technologies and measurements for UAV-assisted communications

    Mao, K.; Zhu, Q.; Wang, C.X.; Ye, X.; Gomez-Ponce, J.; Cai, X.; Miao, Y.; Cui, Z.; Wu, Q.; Fan, W. A survey on channel sounding technologies and measurements for UAV-assisted communications. IEEE Transactions on Instrumentation and Measurement 2024

    2024

  5. [3]

    Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM)

    Sadgrove, E.J.; Falzon, G.; Miron, D.; Lamb, D.W. Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM). Computers in Industry 2018, 98, 183–191

    2018

  6. [4]

    Machine learning algorithms for urban land use planning: A review.Urban Science 2021, 5, 68

    Chaturvedi, V .; de Vries, W.T. Machine learning algorithms for urban land use planning: A review.Urban Science 2021, 5, 68

    2021

  7. [5]

    Strategies for optimized uav surveillance in various tasks and scenarios: A review

    Fang, Z.; Savkin, A.V . Strategies for optimized uav surveillance in various tasks and scenarios: A review. Drones 2024, 8, 193

    2024

  8. [6]

    A systematic review of trustworthy artificial intelligence applications in natural disasters

    Albahri, A.; Khaleel, Y.L.; Habeeb, M.A.; Ismael, R.D.; Hameed, Q.A.; Deveci, M.; Homod, R.Z.; Albahri, O.; Alamoodi, A.; Alzubaidi, L. A systematic review of trustworthy artificial intelligence applications in natural disasters. Computers and Electrical Engineering 2024, 118, 109409

    2024

Show all 98 references
  1. [7]

    Detection and tracking of large number of targets in wide area surveillance

    Reilly, V .; Idrees, H.; Shah, M. Detection and tracking of large number of targets in wide area surveillance. In Proceedings of the Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part III 11...

    2010

  2. [8]

    UAV environmental perception and autonomous obstacle avoidance: A deep learning and depth camera combined solution

    Wang, D.; Li, W.; Liu, X.; Li, N.; Zhang, C. UAV environmental perception and autonomous obstacle avoidance: A deep learning and depth camera combined solution. Computers and Electronics in Agriculture 2020, 175, 105523

    2020

  3. [9]

    The view from above: A survey of the public’s perception of unmanned aerial vehicles and privacy

    Nelson, J.R.; Grubesic, T.H.; Wallace, D.; Chamberlain, A.W. The view from above: A survey of the public’s perception of unmanned aerial vehicles and privacy. Journal of urban technology 2019, 26, 83–105

    2019

  4. [10]

    YOLO9000: better, faster, stronger

    Redmon, J.; Farhadi, A. YOLO9000: better, faster, stronger. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263–7271

    2017

  5. [11]

    You only look once: Unified, real-time object detection

    Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788

    2016

  6. [12]

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

    Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence 2016, 39, 1137–1149. 23 of 27

    2016

  7. [13]

    AI Mapping Risks to Wildlife in Tanzania: Rapid scanning aerial images to flag the changing frontier of human-wildlife proximity

    Yi, Z.F.; Frederick, H.; Mendoza, R.L.; Avery, R.; Goodman, L. AI Mapping Risks to Wildlife in Tanzania: Rapid scanning aerial images to flag the changing frontier of human-wildlife proximity. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposiu...

    2021

  8. [14]

    Car detection in low resolution aerial images

    Zhao, T.; Nevatia, R. Car detection in low resolution aerial images. Image and vision computing 2003, 21, 693–703

    2003

  9. [15]

    What’s in a Name? Beyond Class Indices for Image Recognition, 2024, [arXiv:cs.CV/2304.02364]

    Han, K.; Huang, X.; Li, Y.; Vaze, S.; Li, J.; Jia, X. What’s in a Name? Beyond Class Indices for Image Recognition, 2024, [arXiv:cs.CV/2304.02364]

    2024 arXiv

  10. [16]

    Open-set recognition: A good closed-set classifier is all you need? 2021

    Vaze, S.; Han, K.; Vedaldi, A.; Zisserman, A. Open-set recognition: A good closed-set classifier is all you need? 2021

    2021

  11. [17]

    Open Vocabulary Scene Parsing, 2017, [arXiv:cs.CV/1703.08769]

    Zhao, H.; Puig, X.; Zhou, B.; Fidler, S.; Torralba, A. Open Vocabulary Scene Parsing, 2017, [arXiv:cs.CV/1703.08769]

    2017 arXiv

  12. [18]

    Open-Vocabulary Object Detection Using Captions, 2021, [arXiv:cs.CV/2011.10678]

    Zareian, A.; Rosa, K.D.; Hu, D.H.; Chang, S.F. Open-Vocabulary Object Detection Using Captions, 2021, [arXiv:cs.CV/2011.10678]

    2021 arXiv

  13. [20]

    Learning Transferable Visual Models From Natural Language Supervision, 2021, [arXiv:cs.CV/2103.00020]

    Radford, A.; Kim, J.W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P .; Clark, J.; et al. Learning Transferable Visual Models From Natural Language Supervision, 2021, [arXiv:cs.CV/2103.00020]

    2021 arXiv

  14. [21]

    Optimizing the Trade-Off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction

    Soviany, P .; Ionescu, R.T. Optimizing the Trade-Off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction. In Proceedings of the 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2018, ...

    2018

  15. [22]

    Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

    Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580–587. https://doi.org/10.1109/CVPR.2014.81

    2014 doi

  16. [24]

    Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

    Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In Proceedings of the Advances in Neural Information Processing Systems; Cortes, C.; Lawrence, N.; Lee, D.; Sugiyama, M.; Garnett, R., Eds. Curran Associates,...

    2015

  17. [25]

    Mask R-CNN

    He, K.; Gkioxari, G.; Dollár, P .; Girshick, R. Mask R-CNN. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2980–2988. https://doi.org/10.1109/ICCV .2017.322

    2017 doi

  18. [26]

    You Only Look Once: Unified, Real-Time Object Detection, 2016, [arXiv:cs.CV/1506.02640]

    Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection, 2016, [arXiv:cs.CV/1506.02640]

    2016 arXiv

  19. [27]

    YOLOv3: An Incremental Improvement, 2018, [arXiv:cs.CV/1804.02767]

    Redmon, J.; Farhadi, A. YOLOv3: An Incremental Improvement, 2018, [arXiv:cs.CV/1804.02767]

    2018 arXiv

  20. [28]

    In Computer Vision – ECCV 2016 ; Springer International Publishing, 2016; p

    Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.Y.; Berg, A.C., SSD: Single Shot MultiBox Detector. In Computer Vision – ECCV 2016 ; Springer International Publishing, 2016; p. 21–37. https: //doi.org/10.1007/978-3-319-46448-0_2

    2016 doi

  21. [29]

    Focal Loss for Dense Object Detection, 2018, [arXiv:cs.CV/1708.02002]

    Lin, T.Y.; Goyal, P .; Girshick, R.; He, K.; Dollár, P . Focal Loss for Dense Object Detection, 2018, [arXiv:cs.CV/1708.02002]

    2018 arXiv

  22. [30]

    Focal Loss for Dense Object Detection

    Lin, T.Y.; Goyal, P .; Girshick, R.; He, K.; Dollar, P . Focal Loss for Dense Object Detection. In Proceedings of the Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct 2017

    2017

  23. [31]

    Feature Pyramid Networks for Object Detection, 2017, [arXiv:cs.CV/1612.03144]

    Lin, T.Y.; Dollár, P .; Girshick, R.; He, K.; Hariharan, B.; Belongie, S. Feature Pyramid Networks for Object Detection, 2017, [arXiv:cs.CV/1612.03144]

    2017 arXiv

  24. [32]

    Effective Fusion Factor in FPN for Tiny Object Detection

    Gong, Y.; Yu, X.; Ding, Y.; Peng, X.; Zhao, J.; Han, Z. Effective Fusion Factor in FPN for Tiny Object Detection. In Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 1159–1167. https://doi.org/10.1109/WACV48630.2021.00120

    2021

  25. [33]

    Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection

    Li, X.; Wang, W.; Wu, L.; Chen, S.; Hu, X.; Li, J.; Tang, J.; Yang, J. Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection. In Proceedings of the Proceedings of the 34th International Conference on Neural Information Processing ...

    2020

  26. [34]

    Recent advances in open set recognition: A survey

    Geng, C.; Huang, S.j.; Chen, S. Recent advances in open set recognition: A survey. IEEE transactions on pattern analysis and machine intelligence 2020, 43, 3614–3631

    2020

  27. [35]

    Convolutional prototype network for open set recognition

    Yang, H.M.; Zhang, X.Y.; Yin, F.; Yang, Q.; Liu, C.L. Convolutional prototype network for open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020, 44, 2358–2370. 24 of 27

    2020

  28. [36]

    Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

    Wu, W.; Liu, H.; Li, L.; Long, Y.; Wang, X.; Wang, Z.; Li, J.; Chang, Y. Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image. PloS one 2021, 16, e0259283

    2021

  29. [37]

    Real-time flying object detection with YOLOv8

    Reis, D.; Kupec, J.; Hong, J.; Daoudi, A. Real-time flying object detection with YOLOv8. arXiv preprint arXiv:2305.09972 2023

    2023 arXiv

  30. [38]

    Yolov8: A novel object detection algorithm with enhanced performance and robustness

    Varghese, R.; Sambath, M. Yolov8: A novel object detection algorithm with enhanced performance and robustness. In Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE, 2024, pp. 1–6

    2024

  31. [39]

    Open-vocabulary object detection via vision and language knowledge distillation

    Gu, X.; Lin, T.Y.; Kuo, W.; Cui, Y. Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921 2021

    2021 arXiv

  32. [40]

    Simple open-vocabulary object detection

    Minderer, M.; Gritsenko, A.; Stone, A.; Neumann, M.; Weissenborn, D.; Dosovitskiy, A.; Mahendran, A.; Arnab, A.; Dehghani, M.; Shen, Z.; et al. Simple open-vocabulary object detection. In Proceedings of the European conference on computer vision. Springer, 2022, pp. 728–755

    2022

  33. [41]

    Learning to prompt for open-vocabulary object detection with vision-language model

    Du, Y.; Wei, F.; Zhang, Z.; Shi, M.; Gao, Y.; Li, G. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 14084–14093

    2022

  34. [42]

    Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection, 2022, [arXiv:cs.CV/2207.03482]

    Rasheed, H.; Maaz, M.; Khattak, M.U.; Khan, S.; Khan, F.S. Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection, 2022, [arXiv:cs.CV/2207.03482]

    2022 arXiv

  35. [43]

    RegionCLIP: Region-based Language-Image Pretraining, 2021, [arXiv:cs.CV/2112.09106]

    Zhong, Y.; Yang, J.; Zhang, P .; Li, C.; Codella, N.; Li, L.H.; Zhou, L.; Dai, X.; Yuan, L.; Li, Y.; et al. RegionCLIP: Region-based Language-Image Pretraining, 2021, [arXiv:cs.CV/2112.09106]

    2021 arXiv

  36. [44]

    Toward open vocabulary aerial object detection with clip-activated student-teacher learning

    Li, Y.; Guo, W.; Yang, X.; Liao, N.; He, D.; Zhou, J.; Yu, W. Toward open vocabulary aerial object detection with clip-activated student-teacher learning. In Proceedings of the European Conference on Computer Vision. Springer, 2024, pp. 431–448

    2024

  37. [45]

    RemoteCLIP: A Vision Language Foundation Model for Remote Sensing, 2024, [arXiv:cs.CV/2306.11029]

    Liu, F.; Chen, D.; Guan, Z.; Zhou, X.; Zhu, J.; Ye, Q.; Fu, L.; Zhou, J. RemoteCLIP: A Vision Language Foundation Model for Remote Sensing, 2024, [arXiv:cs.CV/2306.11029]

    2024 arXiv

  38. [46]

    Advancing open-set object detection in remote sensing using multimodal large language model

    Saini, N.; Dubey, A.; Das, D.; Chattopadhyay, C. Advancing open-set object detection in remote sensing using multimodal large language model. In Proceedings of the Proceedings of the Winter Conference on Applications of Computer Vision, 2025, pp. 451–458

    2025

  39. [47]

    PaLM 2 Technical Report, 2023, [arXiv:cs.CL/2305.10403]

    Anil, R.; Dai, A.M.; Firat, O.; Johnson, M.; Lepikhin, D.; Passos, A.; Shakeri, S.; Taropa, E.; Bailey, P .; Chen, Z.; et al. PaLM 2 Technical Report, 2023, [arXiv:cs.CL/2305.10403]

    2023 arXiv

  40. [48]

    Llama 2: Open Foundation and Fine-Tuned Chat Models, 2023, [arXiv:cs.CL/2307.09288]

    Touvron, H.; Martin, L.; Stone, K.; Albert, P .; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P .; Bhosale, S.; et al. Llama 2: Open Foundation and Fine-Tuned Chat Models, 2023, [arXiv:cs.CL/2307.09288]

    2023 arXiv

  41. [50]

    Locate anything on earth: Advancing open-vocabulary object detection for remote sensing community

    Pan, J.; Liu, Y.; Fu, Y.; Ma, M.; Li, J.; Paudel, D.P .; Van Gool, L.; Huang, X. Locate anything on earth: Advancing open-vocabulary object detection for remote sensing community. In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, 2025, Vol. 3...

    2025

  42. [51]

    Openrsd: Towards open-prompts for object detection in remote sensing images

    Huang, Z.; Feng, Y.; Yang, S.; Liu, Z.; Liu, Q.; Wang, Y. Openrsd: Towards open-prompts for object detection in remote sensing images. arXiv preprint arXiv:2503.06146 2025

    2025 arXiv

  43. [52]

    LLaMA-Unidetector: An LLaMA-Based Universal Framework for Open-Vocabulary Object Detection in Remote Sensing Imagery

    Xie, J.; Wang, G.; Zhang, T.; Sun, Y.; Chen, H.; Zhuang, Y.; Li, J. LLaMA-Unidetector: An LLaMA-Based Universal Framework for Open-Vocabulary Object Detection in Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing 2025, 63, 1–18. https://doi.org/10.1109/...

    2025

  44. [53]

    Zero-shot aerial object detection with visual description regularization

    Zang, Z.; Lin, C.; Tang, C.; Wang, T.; Lv, J. Zero-shot aerial object detection with visual description regularization. In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, 2024, Vol. 38, pp. 6926–6934

    2024

  45. [54]

    DETRs Beat YOLOs on Real-time Object Detection, 2024, [arXiv:cs.CV/2304.08069]

    Zhao, Y.; Lv, W.; Xu, S.; Wei, J.; Wang, G.; Dang, Q.; Liu, Y.; Chen, J. DETRs Beat YOLOs on Real-time Object Detection, 2024, [arXiv:cs.CV/2304.08069]

    2024 arXiv

  46. [55]

    LLaMA: Open and Efficient Foundation Language Models, 2023, [arXiv:cs.CL/2302.13971]

    Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. LLaMA: Open and Efficient Foundation Language Models, 2023, [arXiv:cs.CL/2302.13971]

    2023 arXiv

  47. [56]

    Making sense of implementation theories, models, and frameworks

    Nilsen, P . Making sense of implementation theories, models, and frameworks. InImplementation Science 3.0; Springer, 2020; pp. 53–79

    2020

  48. [57]

    FEDS: a framework for evaluation in design science research

    Venable, J.; Pries-Heje, J.; Baskerville, R. FEDS: a framework for evaluation in design science research. European journal of information systems 2016, 25, 77–89. 25 of 27

    2016

  49. [58]

    Microsoft COCO: Common Objects in Context, 2015, [arXiv:cs.CV/1405.0312]

    Lin, T.Y.; Maire, M.; Belongie, S.; Bourdev, L.; Girshick, R.; Hays, J.; Perona, P .; Ramanan, D.; Zitnick, C.L.; Dollár, P . Microsoft COCO: Common Objects in Context, 2015, [arXiv:cs.CV/1405.0312]

    2015 arXiv

  50. [59]

    LVIS: A Dataset for Large Vocabulary Instance Segmentation, 2019, [arXiv:cs.CV/1908.03195]

    Gupta, A.; Dollár, P .; Girshick, R. LVIS: A Dataset for Large Vocabulary Instance Segmentation, 2019, [arXiv:cs.CV/1908.03195]

    2019 arXiv

  51. [60]

    V3Det: Vast Vocabulary Visual Detection Dataset, 2023, [arXiv:cs.CV/2304.03752]

    Wang, J.; Zhang, P .; Chu, T.; Cao, Y.; Zhou, Y.; Wu, T.; Wang, B.; He, C.; Lin, D. V3Det: Vast Vocabulary Visual Detection Dataset, 2023, [arXiv:cs.CV/2304.03752]

    2023 arXiv

  52. [61]

    How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection, 2023, [arXiv:cs.CV/2308.13177]

    Yao, Y.; Liu, P .; Zhao, T.; Zhang, Q.; Liao, J.; Fang, C.; Lee, K.; Wang, Q. How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection, 2023, [arXiv:cs.CV/2308.13177]

    2023 arXiv

  53. [62]

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

    Zhu, H.; Chen, X.; Dai, W.; Fu, K.; Ye, Q.; Jiao, J. Orientation robust object detection in aerial images using deep convolutional neural network. In Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 3735–3739. https://doi.org/10.1109/...

    2015

  54. [63]

    Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks

    Long, Y.; Gong, Y.; Xiao, Z.; Liu, Q. Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing 2017, 55, 2486–2498. https://doi.org/10.1109/TGRS.2016.2645610

    2017

  55. [64]

    A high resolution optical satellite image dataset for ship recognition and some new baselines

    Liu, Z.; Yuan, L.; Weng, L.; Yang, Y. A high resolution optical satellite image dataset for ship recognition and some new baselines. In Proceedings of the International conference on pattern recognition applications and methods. SciTePress, 2017, Vol. 2, pp. 324–331

    2017

  56. [65]

    Multi-class geospatial object detection and geographic image clas- sification based on collection of part detectors

    Cheng, G.; Han, J.; Zhou, P .; Guo, L. Multi-class geospatial object detection and geographic image clas- sification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing 2014, 98, 119–132

    2014

  57. [66]

    Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images

    Zou, Z.; Shi, Z. Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images. IEEE Transactions on Image Processing 2018, 27, 1100–1111. https://doi.org/10.1109/ TIP .2017.2773199

    2018

  58. [67]

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

    Xia, G.S.; Bai, X.; Ding, J.; Zhu, Z.; Belongie, S.; Luo, J.; Datcu, M.; Pelillo, M.; Zhang, L. DOTA: A large-scale dataset for object detection in aerial images. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3974–3983

    2018

  59. [68]

    Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection

    Zhang, Y.; Yuan, Y.; Feng, Y.; Lu, X. Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 5535–5548. https://doi.org/10.1109/TGRS.2019.2900302

    2019

  60. [69]

    Multisized Object Detection Using Spaceborne Optical Imagery

    Haroon, M.; Shahzad, M.; Fraz, M.M. Multisized Object Detection Using Spaceborne Optical Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 3032–3046. https: //doi.org/10.1109/JSTARS.2020.3000317

    2020

  61. [70]

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

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

    2020

  62. [71]

    Anchor-free oriented proposal generator for object detection

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

    2022

  63. [72]

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

    Ding, J.; Xue, N.; Xia, G.S.; Bai, X.; Yang, W.; Yang, M.Y.; Belongie, S.; Luo, J.; Datcu, M.; Pelillo, M.; et al. Object detection in aerial images: A large-scale benchmark and challenges. IEEE transactions on pattern analysis and machine intelligence 2021, 44, 7778–7796

    2021

  64. [73]

    xview: Objects in context in overhead imagery

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

    2018 arXiv

  65. [74]

    Zero-shot object detection

    Bansal, A.; Sikka, K.; Sharma, G.; Chellappa, R.; Divakaran, A. Zero-shot object detection. In Proceedings of the Proceedings of the European conference on computer vision (ECCV), 2018, pp. 384–400

    2018

  66. [75]

    FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery

    Sun, X.; Wang, P .; Yan, Z.; Xu, F.; Wang, R.; Diao, W.; Chen, J.; Li, J.; Feng, Y.; Xu, T.; et al. FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2022, 184, 116–130

    2022

  67. [76]

    Learning to holistically detect bridges from large-size vhr remote sensing imagery

    Li, Y.; Luo, J.; Zhang, Y.; Tan, Y.; Yu, J.G.; Bai, S. Learning to holistically detect bridges from large-size vhr remote sensing imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence 2024

    2024

  68. [77]

    Towards Large-Scale Small Object Detection: Survey and Benchmarks

    Cheng, G.; Yuan, X.; Yao, X.; Yan, K.; Zeng, Q.; Xie, X.; Han, J. Towards Large-Scale Small Object Detection: Survey and Benchmarks. IEEE Transactions on Pattern Analysis and Machine Intelligence 2023, 45, 13467–13488. https://doi.org/10.1109/TPAMI.2023.3290594

    2023

  69. [78]

    Visual Grounding in Remote Sensing Images

    Sun, Y.; Feng, S.; Li, X.; Ye, Y.; Kang, J.; Huang, X. Visual Grounding in Remote Sensing Images. In Proceedings of the Proceedings of the 30th ACM International Conference on Multimedia, New York, NY, USA, 2022; MM ’22, p. 404–412. https://doi.org/10.1145/3503161.3548316. 26 of 27

    2022

  70. [79]

    Language-Guided Progressive Attention for Visual Grounding in Remote Sensing Images

    Li, K.; Wang, D.; Xu, H.; Zhong, H.; Wang, C. Language-Guided Progressive Attention for Visual Grounding in Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2024, 62, 1–13. https: //doi.org/10.1109/TGRS.2024.3423663

    2024

  71. [80]

    RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data

    Zhan, Y.; Xiong, Z.; Yuan, Y. RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing 2023, 61, 1–13. https://doi.org/10.1109/TGRS.2023 .3250471

    2023 doi

  72. [82]

    Star: A first-ever dataset and a large-scale benchmark for scene graph generation in large-size satellite imagery

    Li, Y.; Wang, L.; Wang, T.; Yang, X.; Luo, J.; Wang, Q.; Deng, Y.; Wang, W.; Sun, X.; Li, H.; et al. Star: A first-ever dataset and a large-scale benchmark for scene graph generation in large-size satellite imagery. IEEE Trans. Pattern Anal. Mach. Intell 2025, 47, 1832–1849

    2025

  73. [83]

    Scalable Person Re-Identification: A Benchmark

    Zheng, L.; Shen, L.; Tian, L.; Wang, S.; Wang, J.; Tian, Q. Scalable Person Re-Identification: A Benchmark. In Proceedings of the Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2015

    2015

  74. [84]

    ROUGE: A Package for Automatic Evaluation of Summaries

    Lin, C.Y. ROUGE: A Package for Automatic Evaluation of Summaries. In Proceedings of the Text Summa- rization Branches Out, Barcelona, Spain, 2004; pp. 74–81

    2004

  75. [85]

    The Pascal Visual Object Classes (VOC) Challenge

    Everingham, M.; Gool, L.; Williams, C.K.; Winn, J.; Zisserman, A. The Pascal Visual Object Classes (VOC) Challenge. Int. J. Comput. Vision 2010, 88, 303–338. https://doi.org/10.1007/s11263-009-0275-4

    2010 doi

  76. [86]

    Microsoft COCO: Common Objects in Context

    Lin, T.Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P .; Ramanan, D.; Dollár, P .; Zitnick, C.L. Microsoft COCO: Common Objects in Context. In Proceedings of the Computer Vision – ECCV 2014; Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T., Eds., Cham, 2014; pp. 740–755

    2014

  77. [87]

    Recent Advances for Aerial Object Detection: A Survey

    Leng, J.; Ye, Y.; Mo, M.; Gao, C.; Gan, J.; Xiao, B.; Gao, X. Recent Advances for Aerial Object Detection: A Survey. ACM Comput. Surv. 2024, 56. https://doi.org/10.1145/3664598

    2024 doi

  78. [88]

    Detection and Tracking Meet Drones Challenge, 2021, [arXiv:cs.CV/2001.06303]

    Zhu, P .; Wen, L.; Du, D.; Bian, X.; Fan, H.; Hu, Q.; Ling, H. Detection and Tracking Meet Drones Challenge, 2021, [arXiv:cs.CV/2001.06303]

    2021 arXiv

  79. [89]

    A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

    Cazzato, D.; Cimarelli, C.; Sanchez-Lopez, J.L.; Voos, H.; Leo, M. A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles. Journal of Imaging 2020, 6. https://doi.org/10.3390/ jimaging6080078

    2020

  80. [90]

    A Benchmark and Simulator for UAV Tracking

    Mueller, M.; Smith, N.; Ghanem, B. A Benchmark and Simulator for UAV Tracking. In Proceedings of the Computer Vision – ECCV 2016; Leibe, B.; Matas, J.; Sebe, N.; Welling, M., Eds., Cham, 2016; pp. 445–461

    2016

  81. [91]

    DroneSURF: Benchmark Dataset for Drone- based Face Recognition

    Kalra, I.; Singh, M.; Nagpal, S.; Singh, R.; Vatsa, M.; Sujit, P .B. DroneSURF: Benchmark Dataset for Drone- based Face Recognition. In Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). IEEE Press, 2019, p. 1–7. https...

    2019

  82. [92]

    The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking, 2018, [arXiv:cs.CV/1804.00518]

    Du, D.; Qi, Y.; Yu, H.; Yang, Y.; Duan, K.; Li, G.; Zhang, W.; Huang, Q.; Tian, Q. The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking, 2018, [arXiv:cs.CV/1804.00518]

    2018 arXiv

  83. [93]

    LoRA: Low-Rank Adaptation of Large Language Models, 2021, [arXiv:cs.CL/2106.09685]

    Hu, E.J.; Shen, Y.; Wallis, P .; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W. LoRA: Low-Rank Adaptation of Large Language Models, 2021, [arXiv:cs.CL/2106.09685]

    2021 arXiv

  84. [94]

    Prefix-Tuning: Optimizing Continuous Prompts for Generation

    Li, X.L.; Liang, P . Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Proceedings of the Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1:...

    2021 doi

  85. [95]

    LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models, 2023, [arXiv:cs.CL/2304.01933]

    Hu, Z.; Wang, L.; Lan, Y.; Xu, W.; Lim, E.P .; Bing, L.; Xu, X.; Poria, S.; Lee, R.K.W. LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models, 2023, [arXiv:cs.CL/2304.01933]

    2023 arXiv

  86. [96]

    The Power of Scale for Parameter-Efficient Prompt Tuning, 2021, [arXiv:cs.CL/2104.08691]

    Lester, B.; Al-Rfou, R.; Constant, N. The Power of Scale for Parameter-Efficient Prompt Tuning, 2021, [arXiv:cs.CL/2104.08691]

    2021 arXiv

  87. [97]

    Feature-Enhanced CenterNet for Small Object Detection in Remote Sensing Images

    Shi, T.; Gong, J.; Hu, J.; Zhi, X.; Zhang, W.; Zhang, Y.; Zhang, P .; Bao, G. Feature-Enhanced CenterNet for Small Object Detection in Remote Sensing Images. Remote Sensing 2022, 14. https://doi.org/10.3390/rs14215488

    2022 doi

  88. [98]

    A Normalized Gaussian Wasserstein Distance for Tiny Object Detection, 2022, [arXiv:cs.CV/2110.13389]

    Wang, J.; Xu, C.; Yang, W.; Yu, L. A Normalized Gaussian Wasserstein Distance for Tiny Object Detection, 2022, [arXiv:cs.CV/2110.13389]

    2022 arXiv

  89. [99]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, 2021, [arXiv:cs.CV/2010.11929]

    Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, 2021, [arXiv:cs.CV/2010.11929]

    2021 arXiv

  90. [100]

    Attention Is All You Need, 2023, [arXiv:cs.CL/1706.03762]

    Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, L.; Polosukhin, I. Attention Is All You Need, 2023, [arXiv:cs.CL/1706.03762]. 27 of 27 Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely t...

    2023 arXiv

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