Fetching the paper…
Reading the bibliography…
In recent years, object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention and has been widely implemented across a diverse array of fields.
D. E. Broadbent, “Perception and communication,” 1958
1958
Earlier work this paper cites.
A. M. Treisman, “Selective attention in man.”
1964
Earlier work this paper cites.
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixtures of local experts,”
1991
Earlier work this paper cites.
M. I. Jordan and R. A. Jacobs, “Hierarchical mixtures of experts and the em algorithm,”
1994
Earlier work this paper cites.
J. Nascimento and J. Marques, “Performance evaluation of object detection algorithms for video surveillance,”
2006
Earlier work this paper cites.
J. Li, L.-Y. Duan, X. Chen, T. Huang, and Y. Tian, “Finding the secret of image saliency in the frequency domain,”
2015
Earlier work this paper cites.
S. Hwang, J. Park, N. Kim, Y. Choi, and I. So Kweon, “Multispectral pedestrian detection: Benchmark dataset and baseline,” in
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan, “Domain separation networks,”
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in
2016
Earlier work this paper cites.
S. Gross, M. Ranzato, and A. Szlam, “Hard mixtures of experts for large scale weakly supervised vision,” in
2017
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,”
2017
Earlier work this paper cites.
D. Konig, M. Adam, C. Jarvers, G. Layher, H. Neumann, and M. Teutsch, “Fully convolutional region proposal networks for multispectral person detection,” in
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in
2017
Earlier work this paper cites.
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
2018
Earlier work this paper cites.
C. Li, D. Song, R. Tong, and M. Tang, “Multispectral pedestrian detection via simultaneous detection and segmentation,” in
2018
Earlier work this paper cites.
Y. Cao, D. Guan, Y. Wu, J. Yang, Y. Cao, and M. Y. Yang, “Box-level segmentation supervised deep neural networks for accurate and real-time multispectral pedestrian detection,”
2019
Earlier work this paper cites.
D. Guan, Y. Cao, J. Yang, Y. Cao, and M. Y. Yang, “Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection,”
2019
Earlier work this paper cites.
C. Li, D. Song, R. Tong, and M. Tang, “Illumination-aware faster r-cnn for robust multispectral pedestrian detection,”
2019
Earlier work this paper cites.
M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic, “On mutual information maximization for representation learning,” in
2019
Earlier work this paper cites.
L. Zhang, X. Zhu, X. Chen, X. Yang, Z. Lei, and Z. Liu, “Weakly aligned cross-modal learning for multispectral pedestrian detection,” in
2019
Cited alongside, same era.
L. Zhang, Z. Liu, S. Zhang, X. Yang, H. Qiao, K. Huang, and A. Hussain, “Cross-modality interactive attention network for multispectral pedestrian detection,”
2019
Cited alongside, same era.
Z. Cai and N. Vasconcelos, “Cascade r-cnn: High quality object detection and instance segmentation,”
2019
Cited alongside, same era.
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell, “Bdd100k: A diverse driving dataset for heterogeneous multitask learning,” in
2020
Cited alongside, same era.
K. Zhou, L. Chen, and X. Cao, “Improving multispectral pedestrian detection by addressing modality imbalance problems,” in
2020
Cited alongside, same era.
Y.-T. Chen, J. Shi, Z. Ye, C. Mertz, D. Ramanan, and S. Kong, “Multimodal object detection via probabilistic ensembling,” in
2022
Later among the works it cites.
F. Qingyun and W. Zhaokui, “Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery,”
2022
Later among the works it cites.
Y. Wei, L. Zhao, W. Zheng, Z. Zhu, J. Zhou, and J. Lu, “Surroundocc: Multi-camera 3d occupancy prediction for autonomous driving,” in
2023
Later among the works it cites.
X. Wei and S. Zhao, “Boosting adversarial transferability with learnable patch-wise masks,”
2023
Later among the works it cites.
Z. Tu, Y. Ma, Z. Li, C. Li, J. Xu, and Y. Liu, “Rgbt salient object detection: A large-scale dataset and benchmark,”
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…
E. H. Sanchez, M. Serrurier, and M. Ortner, “Learning disentangled representations via mutual information estimation,” in
2020
Cited alongside, same era.
H. Zhang, E. Fromont, S. Lefevre, and B. Avignon, “Multispectral fusion for object detection with cyclic fuse-and-refine blocks,” in
2020
Cited alongside, same era.
B. LI, X. Xiaoyang, W. Xingxing, and T. Wenting, “Ship detection and classification from optical remote sensing images: A survey,”
2021
Cited alongside, same era.
F. Zhao and W. Zhao, “Learning specific and general realm feature representations for image fusion,”
2021
Cited alongside, same era.
D. Lepikhin, H. Lee, Y. Xu, D. Chen, O. Firat, Y. Huang, M. Krikun, N. Shazeer, and Z. Chen, “{GS}hard: Scaling giant models with conditional computation and automatic sharding,” in
2021
Cited alongside, same era.
2021
Cited alongside, same era.
X. Jia, C. Zhu, M. Li, W. Tang, and W. Zhou, “Llvip: A visible-infrared paired dataset for low-light vision,” in
2021
Cited alongside, same era.
R. Li, J. Xiang, F. Sun, Y. Yuan, L. Yuan, and S. Gou, “Multiscale cross-modal homogeneity enhancement and confidence-aware fusion for multispectral pedestrian detection,”
2023
Later among the works it cites.
B. van Amsterdam, A. Kadkhodamohammadi, I. Luengo, and D. Stoyanov, “Aspnet: Action segmentation with shared-private representation of multiple data sources,” in
2023
Later among the works it cites.
H. Wang, Y. Chen, C. Ma, J. Avery, L. Hull, and G. Carneiro, “Multi-modal learning with missing modality via shared-specific feature modelling,” in
2023
Later among the works it cites.
B. Cao, Y. Sun, P. Zhu, and Q. Hu, “Multi-modal gated mixture of local-to-global experts for dynamic image fusion,” in
2023
Later among the works it cites.
Z. Chen, Y. Shen, M. Ding, Z. Chen, H. Zhao, E. G. Learned-Miller, and C. Gan, “Mod-squad: Designing mixtures of experts as modular multi-task learners,” in
2023
Later among the works it cites.
J. Zhang, H. Liu, K. Yang, X. Hu, R. Liu, and R. Stiefelhagen, “Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers,”
2023
Later among the works it cites.
S. Zhang, X. Wang, J. Wang, J. Pang, C. Lyu, W. Zhang, P. Luo, and K. Chen, “Dense distinct query for end-to-end object detection,” in
2023
Later among the works it cites.
X. Zuo, Z. Wang, Y. Liu, J. Shen, and H. Wang, “Lgadet: Light-weight anchor-free multispectral pedestrian detection with mixed local and global attention,”
2023
Later among the works it cites.
Y. Zhang, H. Yu, Y. He, X. Wang, and W. Yang, “Illumination-guided rgbt object detection with inter-and intra-modality fusion,”
2023
Later among the works it cites.
Y. Zhu, X. Sun, M. Wang, and H. Huang, “Multi-modal feature pyramid transformer for rgb-infrared object detection,”
2023
Later among the works it cites.
Y. Cao, J. Bin, J. Hamari, E. Blasch, and Z. Liu, “Multimodal object detection by channel switching and spatial attention,” in
2023
Later among the works it cites.
M. Yuan and X. Wei, “C 2 former: Calibrated and complementary transformer for rgb-infrared object detection,”
2024
Closest in time.
M. Yuan, X. Shi, N. Wang, Y. Wang, and X. Wei, “Improving rgb-infrared object detection with cascade alignment-guided transformer,”
2024
Closest in time.
J. Shen, Y. Chen, Y. Liu, X. Zuo, H. Fan, and W. Yang, “Icafusion: Iterative cross-attention guided feature fusion for multispectral object detection,”
2024
Closest in time.