Pith. sign in

REVIEW 57 references

Selective Structured State Space for Multispectral-fused Small Target Detection

T0 review · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A Mamba-based multispectral detector with three new modules reports state-of-the-art accuracy on VEDAI at real-time speed and with 17 MB size, though it trails some methods on larger objects.

arxiv 2505.14043 v3 pith:MMOLYWUH submitted 2025-05-20 cs.CV

Qianqian Zhang , WeiJun Wang , Yunxing Liu , Li Zhou , Hao Zhao , Junshe An , Zihan Wang This is my paper · ORCID
classification cs.CV
keywords smalltargetsmambamodulecomputationaltargetattentioncapture
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

Detecting small objects in aerial images is hard because they are tiny and easily confused with background. The Mamba architecture, a recent sequence model with linear computational scaling, is fast on high-resolution images but often fails on small objects because it lacks fine local detail. This paper adds three small modules to a Mamba-based detector. First, a fusion module combines visible and infrared images before detection, using learned masks to keep the important details and suppress redundant background. Second, a local-attention block captures fine-grained patterns inside the Mamba backbone. Third, a channel and spatial attention gate emphasizes target-related features while suppressing background. The resulting model, called S4^6-MSTD, has 4.4 million parameters, processes 43 frames per second on an A100 GPU, and reaches 81.2% mean average precision on the VEDAI benchmark, above previous methods. On the DroneVehicle dataset it is not the overall best, but it is the most accurate on the smallest target class, cars. These results suggest the design is useful for real-time remote sensing on limited hardware, though it trades accuracy on large objects.
Extended reading notes

Core claim

The strongest claim is that S4^6-MSTD achieves state-of-the-art small-target detection accuracy while being lightweight and real-time: "our method achieves 81.2% mAP, a state-of-the-art performance in this challenging benchmark test" (VEDAI), with 43.1 FPS and 17.07 MB on DroneVehicle. If correct, the paper demonstrates that a Mamba-based detector with the three proposed modules yields high accuracy for small targets at orders-of-magnitude lower cost than prior Mamba and Transformer fusion models.

Load-bearing premise

The central result depends on the assumption that the reported mAP numbers are comparable across methods and robust across runs. Specifically, the model's design choices, including the 1x1 spatial attention kernel, were selected using test-set performance (Table 5) rather than held-out validation, and results are reported without error bars or code. If the selection process exploited the test set or if evaluation protocols differ from baselines (e.g., input resolution, label modality, training schedule), the claimed state-of-the-art status could erode. This assumption is distinct from the architecture's design and directly affects whether the SOTA claim is true.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

This is an empirical deep-learning paper; the ledger contains no invented entities. Free parameters are standard hyperparameters plus the test-set-selected kernel size. The axioms are domain assumptions about dataset definition, metrics, labeling, and the theoretical foundation of Mamba.

free parameters (3)
  • Spatial attention kernel size = 1x1
    Selected by comparing test-set mAP for 1x1, 3x3, and 7x7 kernels in Table 5, choosing 1x1 for best overall mAP-parameter trade-off.
  • Network depth/width multipliers = 0.33/0.25
    Taken from YOLOv8n to keep the model lightweight; affects capacity and indirectly all reported numbers.
  • Training hyperparameters = LR 0.01, momentum 0.937, weight decay 0.0005, batch 8, 300 epochs
    Chosen by hand without sensitivity analysis; typical SGD settings for YOLO-style detectors.
assumptions (5)
  • domain assumption Small targets are defined by bounding-box size ratio below 0.1 (Gao et al.)
    Adopted in Section 2 and used to justify VEDAI as the small-target benchmark.
  • domain assumption mAP50 adequately represents detection quality for small targets
    Used as the primary metric in Section 5.3; no other metrics such as F1 or precision-recall curves are reported.
  • domain assumption Infrared ground-truth annotations are more comprehensive and can serve as labels for visible and fused training
    Stated in Section 5.2; if IR and RGB annotations are not well aligned, the reported mAP may be biased.
  • standard math State-space model discretization formulas (A=exp(Delta A), etc.) are valid
    Taken from Mamba/Vmamba (Section 3.1) and used without proof.
  • ad hoc to paper The proposed modules improve small-target detection without changing the evaluation protocol
    The whole contribution is an architectural claim; the ablations in Table 4 support it on one dataset, while Table 2 shows overall DroneVehicle mAP below several baselines.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Selective Structured State Space for Multispectral-fused Small Target Detection." pith.science (2026). https://pith.science/paper/MMOLYWUH

@misc{pith2026250514043,
  author       = {Pith},
  title        = {Pith review of: Selective Structured State Space for Multispectral-fused Small Target Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MMOLYWUH}},
  note         = {Machine review of arXiv:2505.14043}
}
read the original abstract

Target detection in high-resolution remote sensing imagery faces challenges due to the low recognition accuracy of small targets and high computational costs. The computational complexity of the Transformer architecture increases quadratically with image resolution, while Convolutional Neural Networks (CNN) architectures are forced to stack deeper convolutional layers to expand their receptive fields, leading to an explosive growth in computational demands. To address these computational constraints, we leverage Mamba's linear complexity for efficiency. However, Mamba's performance declines for small targets, primarily because small targets occupy a limited area in the image and have limited semantic information. Accurate identification of these small targets necessitates not only Mamba's global attention capabilities but also the precise capture of fine local details. To this end, we enhance Mamba by developing the Enhanced Small Target Detection (ESTD) module and the Convolutional Attention Residual Gate (CARG) module. The ESTD module bolsters local attention to capture fine-grained details, while the CARG module, built upon Mamba, emphasizes spatial and channel-wise information, collectively improving the model's ability to capture distinctive representations of small targets. Additionally, to highlight the semantic representation of small targets, we design a Mask Enhanced Pixel-level Fusion (MEPF) module for multispectral fusion, which enhances target features by effectively fusing visible and infrared multimodal information.

Figures

Figures reproduced from arXiv: 2505.14043 by the authors.

Figure 1
Figure 1. The comparison of previous multispectral fusion detec [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The comparison of Vmamba (a) and Our method (b). [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. An overview of S 4 6 -MSTD. It consists of four components which are the Mask Enhanced Pixel-level Fusion Module, the Enhanced Small Target Detection State Space Backbone, the YOLOv8n Head, and the Detection Network. The captured visible and infrared images are firstly fused by the MEPF module to generate the fused images If . The task of the backbone module is to extract features from the fused images. We introduce… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: The architecture of the mask enhanced pixel-level fusion [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Structure of ESTVSS Block and VisionClueMerge. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Structure of ESTD block and CARG block. the ESTD Block, a tailored modification to the Mamba ar￾chitecture, specifically designed to optimize the receptive field for small target detection by enhancing local attention. As shown in [PITH_FULL_IMAGE:figures/full_fig_p00…
Figure 7
Figure 7. Figure 7: Distribution of target sizes in the datasets. The majority [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 10
Figure 10. Figure 10: Comparison of feature map visualizations for our [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 11
Figure 11. Figure 11: Comparison of feature map visualizations for our [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]
Figure 8
Figure 8. Figure 8: Visualization of experimental comparison plots and [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Comparison of feature map visualization between our [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

57 extracted references · 41 canonical work pages

  1. [1]

    Relu-function and derived function review

    Yuhan Bai. Relu-function and derived function review. In SHS Web of Conferences, page 02006. EDP Sciences, 2022. 4

    work page 2022

  2. [2]

    Distilling knowledge via knowledge review

    Pengguang Chen, Shu Liu, Hengshuang Zhao, and Jiaya Jia. Distilling knowledge via knowledge review. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5008–5017, 2021. 7

    work page 2021

  3. [3]

    Mim-istd: Mamba- in-mamba for efficient infrared small target detection

    Tianxiang Chen, Zhentao Tan, Tao Gong, Qi Chu, Yue Wu, Bin Liu, Jieping Ye, and Nenghai Yu. Mim-istd: Mamba- in-mamba for efficient infrared small target detection. arXiv preprint arXiv:2403.02148, 2024. 2

    arXiv 2024

  4. [4]

    Xception: Deep learning with depthwise separable convolutions

    Franc ¸ois Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE con- ference on computer vision and pattern recognition , pages 1251–1258, 2017. 6

    work page 2017

  5. [5]

    Transformers are ssms: General- ized models and efficient algorithms through structured state space duality

    Tri Dao and Albert Gu. Transformers are ssms: General- ized models and efficient algorithms through structured state space duality. arXiv:2405.21060, 2024. 3

    arXiv 2024

  6. [6]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representa- tions, 2021. 2

    2021

  7. [7]

    Sigmoid- weighted linear units for neural network function approxima- tion in reinforcement learning

    Stefan Elfwing, Eiji Uchibe, and Kenji Doya. Sigmoid- weighted linear units for neural network function approxima- tion in reinforcement learning. Neural networks, 107:3–11,

    work page

  8. [8]

    Recent advances in small object detection

    Xinbo Gao, Mengjingcheng Mo, Haitao Wang, and Jiaxv Leng. Recent advances in small object detection. Journal of Data Acquisition and Processing, 36(3), 2021. 2, 6, 7

    work page 2021

Show all 57 references
  1. [9]

    Event-based visible and in- frared fusion via multi-task collaboration

    Mengyue Geng, Lin Zhu, Lizhi Wang, Wei Zhang, Ruiqin Xiong, and Yonghong Tian. Event-based visible and in- frared fusion via multi-task collaboration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 26929–26939, 2024. 2

    2024

  2. [10]

    Mamba: Linear-time sequence modeling with selective state spaces

    Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023. 3

    2023 arXiv

  3. [11]

    Efficiently modeling long sequences with structured state spaces

    Albert Gu, Karan Goel, and Christopher R ´e. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396, 2021. 3

    2021 arXiv

  4. [12]

    Dpdetr: Decoupled position detection trans- former for infrared-visible object detection

    Junjie Guo, Chenqiang Gao, Fangcen Liu, and Deyu Meng. Dpdetr: Decoupled position detection trans- former for infrared-visible object detection. arXiv preprint arXiv:2408.06123, 2024. 7

    2024

  5. [13]

    Align deep features for oriented object detection

    Jiaming Han, Jian Ding, Jie Li, and Gui-Song Xia. Align deep features for oriented object detection. IEEE transac- tions on geoscience and remote sensing, 60:1–11, 2021. 7

    2021

  6. [14]

    Redet: A rotation-equivariant detector for aerial object detection

    Jiaming Han, Jian Ding, Nan Xue, and Gui-Song Xia. Redet: A rotation-equivariant detector for aerial object detection. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 2786–2795, 2021. 7

    2021

  7. [15]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016. 2

    2016

  8. [16]

    Squeeze-and-excitation net- works

    Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation net- works. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018. 5

    2018

  9. [17]

    Weinberger

    Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kil- ian Q. Weinberger. Densely connected convolutional net- works. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2261–2269, 2017. 2

    2017

  10. [18]

    Multimodal knowl- edge distillation for arbitrary-oriented object detection in aerial images

    Zhanchao Huang, Wei Li, and Ran Tao. Multimodal knowl- edge distillation for arbitrary-oriented object detection in aerial images. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE, 2023. 7

    2023

  11. [19]

    Training data-efficient image transformers & distillation through at- tention

    Touvron Hugo, Matthieu Cord, Douze Matthijs, Massa Fran- cisco, Sablayrolles Alexandre, and Jegou Herve. Training data-efficient image transformers & distillation through at- tention. In ICML, 2021. 2

    2021

  12. [20]

    Batch normalization: Accelerating deep network training by reducing internal co- variate shift

    Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal co- variate shift. In International conference on machine learn- ing, pages 448–456. pmlr, 2015. 5

    2015

  13. [21]

    Show, attend and distill: Knowledge distillation via attention-based fea- ture matching

    Mingi Ji, Byeongho Heo, and Sungrae Park. Show, attend and distill: Knowledge distillation via attention-based fea- ture matching. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7945–7952, 2021. 7

    2021

  14. [22]

    Ultralytics yolov8, 2023

    Glenn Jocher, Ayush Chaurasia, and Jing Qiu. Ultralytics yolov8, 2023. 6, 7

    2023

  15. [23]

    Imagenet classification with deep convolutional neural net- works

    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural net- works. Advances in neural information processing systems , 25, 2012. 4

    2012

  16. [24]

    Gelu activation function in deep learning: a comprehensive mathematical analysis and performance

    Minhyeok Lee. Gelu activation function in deep learning: a comprehensive mathematical analysis and performance. arXiv preprint arXiv:2305.12073, 2023. 5

    2023 arXiv

  17. [25]

    Ori- ented reppoints for aerial object detection

    Wentong Li, Yijie Chen, Kaixuan Hu, and Jianke Zhu. Ori- ented reppoints for aerial object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1829–1838, 2022. 7

    2022

  18. [26]

    Large selective kernel network for remote sensing object detection

    Yuxuan Li, Qibin Hou, Zhaohui Zheng, Ming-Ming Cheng, Jian Yang, and Xiang Li. Large selective kernel network for remote sensing object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 16794–16805, 2023. 7

    2023

  19. [27]

    Network in net- work

    Min Lin, Qiang Chen, and Shuicheng Yan. Network in net- work. arXiv preprint arXiv:1312.4400, 2013. 4

    2013 arXiv

  20. [28]

    Zero-shot adversarial quantization

    Yuang Liu, Wei Zhang, and Jun Wang. Zero-shot adversarial quantization. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 1512–1521,

    2021

  21. [29]

    Vmamba: Visual state space model

    Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu. Vmamba: Visual state space model. arXiv preprint arXiv:2401.10166,

    arXiv

  22. [30]

    Detecting moving wildlife using the time difference between two ther- mal airborne images

    Yu Oishi, Natsuki Yoshida, and Hiroyuki Oguma. Detecting moving wildlife using the time difference between two ther- mal airborne images. Remote Sensing, 16(8):1439, 2024. 1

    2024

  23. [31]

    Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery

    Fang Qingyun and Wang Zhaokui. Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery. Pattern Recognition, 130:108786, 2022. 7

    2022

  24. [32]

    Vehicle detec- tion in aerial imagery: A small target detection benchmark

    Sebastien Razakarivony and Frederic Jurie. Vehicle detec- tion in aerial imagery: A small target detection benchmark. Journal of Visual Communication and Image Representation, 34:187–203, 2016. 6, 7

    2016

  25. [33]

    Implicit discriminative knowl- edge learning for visible-infrared person re-identification

    Kaijie Ren and Lei Zhang. Implicit discriminative knowl- edge learning for visible-infrared person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 393–402, 2024. 2

    2024

  26. [34]

    Icafusion: Iterative cross-attention guided feature fusion for multispectral object detection

    Jifeng Shen, Yifei Chen, Yue Liu, Xin Zuo, Heng Fan, and Wankou Yang. Icafusion: Iterative cross-attention guided feature fusion for multispectral object detection. Pattern Recognition, 145:109913, 2024. 7

    2024

  27. [35]

    Spatially-adaptive feature modulation for efficient image super-resolution

    Long Sun, Jiangxin Dong, Jinhui Tang, and Jinshan Pan. Spatially-adaptive feature modulation for efficient image super-resolution. In Proceedings of the IEEE/CVF interna- tional conference on computer vision , pages 13190–13199,

  28. [36]

    Drone-based rgb-infrared cross-modality vehicle detection via uncertainty-aware learning

    Yiming Sun, Bing Cao, Pengfei Zhu, and Qinghua Hu. Drone-based rgb-infrared cross-modality vehicle detection via uncertainty-aware learning. IEEE Transactions on Cir- cuits and Systems for Video Technology, 32(10):6700–6713,

  29. [37]

    On the importance of initialization and momentum in deep learning

    Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. On the importance of initialization and momentum in deep learning. In International conference on machine learning, pages 1139–1147. PMLR, 2013. 6

    2013

  30. [38]

    Going deeper with convolutions

    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1–9, 2015. 2

    2015

  31. [39]

    Going deeper with convolutions

    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 4, 5

    2015

  32. [40]

    Soar: Advancements in small body object detection for aerial imagery using state space models and programmable gradients

    Tushar Verma, Jyotsna Singh, Yash Bhartari, Rishi Jarwal, Suraj Singh, and Shubhkarman Singh. Soar: Advancements in small body object detection for aerial imagery using state space models and programmable gradients. arXiv preprint arXiv:2405.01699, 2024. 2, 3

    2024 arXiv

  33. [41]

    Mask-guided mamba fusion for drone-based visible-infrared vehicle detection

    Simiao Wang, Chunpeng Wang, Chaoyi Shi, Yunan Liu, and Mingyu Lu. Mask-guided mamba fusion for drone-based visible-infrared vehicle detection. IEEE Transactions on Geoscience and Remote Sensing , 62:1–12, 2024. 1, 2, 3, 7

    2024

  34. [42]

    Vegetable disease detection us- ing an improved yolov8 algorithm in the greenhouse plant environment

    Xuewei Wang and Jun Liu. Vegetable disease detection us- ing an improved yolov8 algorithm in the greenhouse plant environment. Scientific Reports, 14(1):4261, 2024. 1

    2024

  35. [43]

    Mamba yolo: Ssms-based yolo for object detection

    Zeyu Wang, Chen Li, Huiying Xu, and Xinzhong Zhu. Mamba yolo: Ssms-based yolo for object detection. arXiv preprint arXiv:2406.05835, 2024. 1

    2024 arXiv

  36. [44]

    Airborne small tar- get detection method based on multimodal and adaptive fea- ture fusion

    Shufang Xu, Xu Chen, Haiwei Li, Tianci Liu, Zhonghao Chen, Hongmin Gao, and Yiyan Zhang. Airborne small tar- get detection method based on multimodal and adaptive fea- ture fusion. IEEE Transactions on Geoscience and Remote Sensing, 62:1–15, 2024. 3

    2024

  37. [45]

    Shallow-deep collab- orative learning for unsupervised visible-infrared person re- identification

    Bin Yang, Jun Chen, and Mang Ye. Shallow-deep collab- orative learning for unsupervised visible-infrared person re- identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16870–16879, 2024. 2

    2024

  38. [46]

    Deco: Decoupling token compres- sion from semantic abstraction in multimodal large language models

    Linli Yao, Lei Li, Shuhuai Ren, Lean Wang, Yuanxin Liu, Xu Sun, and Lu Hou. Deco: Decoupling token compres- sion from semantic abstraction in multimodal large language models. arXiv preprint arXiv:2405.20985, 2024. 6

    2024 arXiv

  39. [47]

    Small object detection algorithm based on improved yolov8 for remote sensing

    Hao Yi, Bo Liu, Bin Zhao, and Enhai Liu. Small object detection algorithm based on improved yolov8 for remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17:1734–1747, 2024. 3

    2024

  40. [48]

    Mambaout: Do we really need mamba for vision? arXiv preprint arXiv:2405.07992,

    Weihao Yu and Xinchao Wang. Mambaout: Do we really need mamba for vision? arXiv preprint arXiv:2405.07992,

    arXiv

  41. [49]

    C 2 former: Calibrated and complementary transformer for rgb-infrared object de- tection

    Maoxun Yuan and Xingxing Wei. C 2 former: Calibrated and complementary transformer for rgb-infrared object de- tection. IEEE Transactions on Geoscience and Remote Sens- ing, 2024. 2, 7

    2024

  42. [50]

    Transla- tion, scale and rotation: cross-modal alignment meets rgb- infrared vehicle detection

    Maoxun Yuan, Yinyan Wang, and Xingxing Wei. Transla- tion, scale and rotation: cross-modal alignment meets rgb- infrared vehicle detection. In European Conference on Com- puter Vision, pages 509–525. Springer, 2022. 7

    2022

  43. [51]

    Superyolo: Super resolution as- sisted object detection in multimodal remote sensing im- agery

    Jiaqing Zhang, Jie Lei, Weiying Xie, Zhenman Fang, Yun- song Li, and Qian Du. Superyolo: Super resolution as- sisted object detection in multimodal remote sensing im- agery. IEEE Transactions on Geoscience and Remote Sens- ing, 61:1–15, 2023. 2, 3, 4, 7

    2023

  44. [52]

    Guided hybrid quantization for ob- ject detection in remote sensing imagery via one-to-one self- teaching

    Jiaqing Zhang, Jie Lei, Weiying Xie, Yunsong Li, Geng Yang, and Xiuping Jia. Guided hybrid quantization for ob- ject detection in remote sensing imagery via one-to-one self- teaching. IEEE Transactions on Geoscience and Remote Sensing, 2023. 7

    2023

  45. [53]

    Ffca-yolo for small object detection in remote sensing images

    Yin Zhang, Mu Ye, Guiyi Zhu, Yong Liu, Pengyu Guo, and Junhua Yan. Ffca-yolo for small object detection in remote sensing images. IEEE Transactions on Geoscience and Re- mote Sensing, 62:1–15, 2024. 3

    2024

  46. [54]

    Rs-mamba for large remote sensing image dense prediction

    Sijie Zhao, Hao Chen, Xueliang Zhang, Pengfeng Xiao, Lei Bai, and Wanli Ouyang. Rs-mamba for large remote sensing image dense prediction. IEEE Transactions on Geoscience and Remote Sensing, 62:1–14, 2024. 2

    2024

  47. [55]

    Probing synergistic high- order interaction in infrared and visible image fusion

    Naishan Zheng, Man Zhou, Jie Huang, Junming Hou, Haoy- ing Li, Yuan Xu, and Feng Zhao. Probing synergistic high- order interaction in infrared and visible image fusion. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 26384–26...

    2024

  48. [56]

    Dmm: Disparity-guided multispectral mamba for ori- ented object detection in remote sensing

    Minghang Zhou, Tianyu Li, Chaofan Qiao, Dongyu Xie, Guoqing Wang, Ningjuan Ruan, Lin Mei, and Yang Yang. Dmm: Disparity-guided multispectral mamba for ori- ented object detection in remote sensing. arXiv preprint arXiv:2407.08132, 2024. 2, 7, 8

    2024 arXiv

  49. [57]

    Vision mamba: Efficient visual representation learning with bidirectional state space model

    Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. In ICML, 2024. 2

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.