Fetching the paper…
Reading the bibliography…
Recently, infrared small target detection (ISTD) has made significant progress, thanks to the development of basic models.
T. Chen, Q. Chu, Z. Tan, B. Liu, and N. Yu, “Abmnet: Coupling transformer with cnn based on adams-bashforth-moulton method for infrared small target detection,” in 2023 IEEE International Conference on Multimedia and Expo (ICME) . IEEE, 2023, pp. 1901–1906
1906
Earlier work this paper cites.
J.-F. Rivest and R. Fortin, “Detection of dim targets in digital infrared imagery by morphological image processing,” Optical Engineering , vol. 35, no. 7, pp. 1886–1893, 1996
1996
Earlier work this paper cites.
2008
Earlier work this paper cites.
C. P. Chen, H. Li, Y. Wei, T. Xia, and Y. Y. Tang, “A local contrast method for small infrared target detection,” IEEE transactions on geoscience and remote sensing , vol. 52, no. 1, pp. 574–581, 2013
2013
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning . pmlr, 2015, pp. 448–456
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. Jorge Cardoso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep learning in medical image analysis and multimodal learning for clinical decision support . Springer, 2017, pp. 240–248
2017
Earlier work this paper cites.
X. Bai and Y. Bi, “Derivative entropy-based contrast measure for infrared small-target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 56, no. 4, pp. 2452–2466, 2018
2018
Earlier work this paper cites.
F. S. Marvasti, M. R. Mosavi, and M. Nasiri, “Flying small target detection in ir images based on adaptive toggle operator,” IET Computer Vision , vol. 12, no. 4, pp. 527–534, 2018
2018
Earlier work this paper cites.
L. Zhang, L. Peng, T. Zhang, S. Cao, and Z. Peng, “Infrared small target detection via non-convex rank approximation minimization joint l 2, 1 norm,” Remote Sensing , vol. 10, no. 11, p. 1821, 2018
2018
Earlier work this paper cites.
S. Yao, Y. Chang, and X. Qin, “A coarse-to-fine method for infrared small target detection,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 2, pp. 256–260, 2018
2018
Earlier work this paper cites.
H. Zhu, S. Liu, L. Deng, Y. Li, and F. Xiao, “Infrared small target detection via low-rank tensor completion with top-hat regularization,” IEEE Transactions on Geoscience and Remote Sensing , vol. 58, no. 2, pp. 1004–1016, 2019
2019
Earlier work this paper cites.
L. Zhang and Z. Peng, “Infrared small target detection based on partial sum of the tensor nuclear norm,” Remote Sensing , vol. 11, no. 4, p. 382, 2019
2019
Earlier work this paper cites.
J. Han, S. Liu, G. Qin, Q. Zhao, H. Zhang, and N. Li, “A local contrast method combined with adaptive background estimation for infrared small target detection,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 9, pp. 1442–1446, 2019
2019
Earlier work this paper cites.
H. Wang, L. Zhou, and L. Wang, “Miss detection vs. false alarm: Adversarial learning for small object segmentation in infrared images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 8509–8518
2019
Earlier work this paper cites.
Y. Sun, J. Yang, and W. An, “Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 5, pp. 3737–3752, 2020
2020
Earlier work this paper cites.
B. Zhao, C. Wang, Q. Fu, and Z. Han, “A novel pattern for infrared small target detection with generative adversarial network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 5, pp. 4481–4492, 2020
2020
Earlier work this paper cites.
M. Zhao, W. Li, L. Li, P. Ma, Z. Cai, and R. Tao, “Three-order tensor creation and tucker decomposition for infrared small-target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–16, 2021
2021
Earlier work this paper cites.
Y. Dai, Y. Wu, F. Zhou, and K. Barnard, “Attentional local contrast networks for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 11, pp. 9813–9824, 2021
2021
Cited alongside, same era.
Y. Dai, Y. Wu, F. Zhou, and K. Barnard, “Asymmetric contextual modulation for infrared small target detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 950–959
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
T. Chen, Q. Chu, Z. Tan, B. Liu, and N. Yu, “Bauenet: Boundary-aware uncertainty enhanced network for infrared small target detection,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Later among the works it cites.
T. Zhang, L. Li, S. Cao, T. Pu, and Z. Peng, “Attention-guided pyramid context networks for detecting infrared small target under complex background,” IEEE Transactions on Aerospace and Electronic Systems , 2023
2023
Later among the works it cites.
T. Chen, Q. Chu, B. Liu, and N. Yu, “Fluid dynamics-inspired network for infrared small target detection,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence , 2023, pp. 590–598
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Han, A. Xiao, E. Wu, J. Guo, C. Xu, and Y. Wang, “Transformer in transformer,” Advances in Neural Information Processing Systems , vol. 34, pp. 15 908–15 919, 2021
2021
Cited alongside, same era.
F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature methods , vol. 18, no. 2, pp. 203–211, 2021
2021
Cited alongside, same era.
A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu, “Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,” in International MICCAI Brainlesion Workshop . Springer, 2021, pp. 272–284
2021
Cited alongside, same era.
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in neural information processing systems , vol. 34, pp. 12 077–12 090, 2021
2021
Cited alongside, same era.
B. Li, C. Xiao, L. Wang, Y. Wang, Z. Lin, M. Li, W. An, and Y. Guo, “Dense nested attention network for infrared small target detection,” IEEE Transactions on Image Processing , 2022
2022
Cited alongside, same era.
M. Zhang, R. Zhang, Y. Yang, H. Bai, J. Zhang, and J. Guo, “Isnet: Shape matters for infrared small target detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 877–886
2022
Cited alongside, same era.
K. Wang, S. Du, C. Liu, and Z. Cao, “Interior attention-aware network for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2022
2022
Cited alongside, same era.
M. Qi, L. Liu, S. Zhuang, Y. Liu, K. Li, Y. Yang, and X. Li, “Ftc-net: Fusion of transformer and cnn features for infrared small target detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 15, pp. 8613–8623, 2022
2022
Cited alongside, same era.
M. Zhang, R. Zhang, J. Zhang, J. Guo, Y. Li, and X. Gao, “Dim2clear network for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–14, 2023
2023
Later among the works it cites.
T. Chen, Z. Tan, Q. Chu, Y. Wu, B. Liu, and N. Yu, “Tci-former: Thermal conduction-inspired transformer for infrared small target detection,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 2, pp. 1201–1209, Mar. 2024. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/27882
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
L. Fan, Y. Wang, G. Hu, F. Li, Y. Dong, H. Zheng, C. Ling, Y. Huang, and X. Ding, “Diffusion-based continuous feature representation for infrared small-dim target detection,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.