SIMNet: an infrared image action recognition network based on similarity evaluation

Han, J., Zhu, J., Cui, Y., et al.: Action detection by double hierarchical multi-structure space–time statistical matching model. Opt. Rev. 25, 301–315 (2018)

MATH  Google Scholar 

Hong, K.: Facial expression recognition based on anomaly feature. Opt. Rev. 29, 178–187 (2022)

MATH  Google Scholar 

Liu, F., Huang, Z., Gu, T., et al.: An efficient evaluation model of fusion splice with different transverse offset and angular misalignment for few mode fiber. Opt Rev. 32(1), 1–13 (2024)

MATH  Google Scholar 

Liu, H., Wu, Y., Sun, F.: Extreme trust region policy optimization for active object recognition. IEEE Trans Neural Netw Learn Syst 29(6), 2253–2258 (2018)

MathSciNet  MATH  Google Scholar 

Zhang, X.Y., Li, C., Shi, X., et al.: AdapNet: adaptability decomposing encoder–decoder network for weakly supervised action recognition and localization. IEEE Trans Neural Netw Learn Syst 34(4), 1852–1863 (2023)

MATH  Google Scholar 

Li, M.S., Chen, S.H., Chen, X., et al.: Symbiotic graph neural networks for 3D skeleton-based human action recognition and motion prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3316–3333 (2022)

MATH  Google Scholar 

Fang, T.Y., An, J.S., Chen, Q., et al.: Progress and comparison in nondestructive detection, imaging and recognition technology for defects of wafers, chips and solder joints. Nondestruct Testing Eval. 39(6), 1599–1654 (2023)

MATH  Google Scholar 

Yi, Q., Tian, G., Malekmohammadi, H., et al.: New features for delamination depth evaluation in carbon fiber reinforced plastic materials using eddy current pulse-compression thermography. Ndt E Int. 102, 264–273 (2019)

ElSheikh, A., Abu-Nabah, B.A., Hamdan, M.O., et al.: Infrared camera geometric calibration: a review and a precise thermal radiation checkerboard target. Sensors 23(7), 3479 (2023)

ADS  MATH  Google Scholar 

Zhu, Y., Guo, G.: A study on visible to infrared action recognition. IEEE Signal Process. Lett. 20(9), 897–900 (2013)

ADS  MATH  Google Scholar 

Hilsenbeck, B., Münch, D., Grosselfinger, A.K. et al. Action recognition in the longwave infrared and the visible spectrum using hough forests. 2016 IEEE International Symposium on Multimedia (ISM) San Jose, CA, USA. pp 329–332 (2013).

Tan, Y., Yan, W., Huang, S., et al.: A motion deviation image-based phase feature for recognition of thermal infrared human activities. Eng. Lett. 28, 48–55 (2020)

MATH  Google Scholar 

Gao, C., Hauptmann, G., Meng, D.: InfAR dataset: infrared action recognition at different times. Neurocomputing 212, 36–47 (2016)

MATH  Google Scholar 

Liu, Y., Lu, Z., Li, J.: Global temporal representation based CNNs for infrared action recognition. Eng. Lett. 25(6), 848–852 (2018)

ADS  MATH  Google Scholar 

Jiang, Z., Wang, Y., Davis, L. et al. Learning discriminative features via label consistent neural network. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA, 207–216 (2017).

Imran, J., Raman, B.: Deep residual infrared action recognition by integrating local and global spatio-temporal cues. Infrared Phys. Technol. 102, 103014 (2019)

MATH  Google Scholar 

Yan, A., Wang, Y., Li, Z. et al. PA3D: Pose-action 3D machine for video recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7922–7931 (2019).

Lee, E.J., Ko, B.C., Nam, J.Y.: Recognizing pedestrian’s unsafe behaviors in far-infrared imagery at night. Infrared Phys. Technol. 76, 261–270 (2016)

ADS  MATH  Google Scholar 

Akula, A., Shah, A.: Deep learning approach for human action recognition in infrared images. Cogn. Syst. Res. 50, 146–154 (2018)

MATH  Google Scholar 

Gochoo, M., Tan, T.H., Huang, S.C.: Novel IoT-based privacy-preserving yoga posture recognition system using low-resolution infrared sensors and deep learning. IEEE Internet Things J. 6, 7192–7200 (2019)

MATH  Google Scholar 

Wang, D., Lai, R., Guan, J.T.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)

ADS  MATH  Google Scholar 

Wang, Z., Simoncelli, E.P., Bovik, A.C. Multiscale structural similarity for image quality assessment. Systems & Computers CA, USA. pp 1398–1402 (2003).

Zhang, L., Zhang, L., Mou, X.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20, 2378–2386 (2011)

ADS  MathSciNet  MATH  Google Scholar 

Mantiuk, R., Kim, K.J., Rempel, A.G.: FSIM: HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans Graphics (TOG) 30, 1–14 (2011)

Google Scholar 

Zhang, R., Isola, P., Efros, A.A. The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, USA. pp 586–595 (2018).

Yang, S., Jiang, L., Liu, Z. Pastiche Master: exemplar-based high-resolution portrait style transfer. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA. pp 7693–7702 (2022).

Deng, Y., Tang, F., Dong, W. et al. StyTr2: Image style transfer with transformers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA. pp 11326–11336 (2022).

Luo, Z., Huang, H., Yu, L. et al. Deep constrained least squares for blind image super-resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA. pp 17642–17652 (2022).

Zamir, S.W., Arora, A., Khan, S. et al. Restormer: efficient transformer for high-resolution image restoration. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA. pp 5728–5739 (2022).

Johnson, J., Alahi, A. Perceptual losses for real-time style transfer and super-resolution. European conference on computer vision, Amsterdam, Netherlands. pp 694–711 (2016).

Hadsell, R., Chopra, S. Dimensionality reduction by learning an invariant mapping. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, New York, USA. pp 1735–1742 (2006).

Hou, Q., Zhou, D., Feng, J. Coordinate attention for efficient mobile network design. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp 13713–13722 (2021).

Fu, Y., Zhang, L., Wang, J. et al. Depth guided adaptive meta-fusion network for few-shot video recognition. Proceedings of the 28th ACM International Conference on Multimedia, New York, USA. pp 1142–1151 (2020).

Zhou, X.H., Yu, L., He, X., et al.: Research on human behavior recognition method in infrared image based on improved resnet-18. Laser & Infrared 51, 1178–1184 (2021)

MATH  Google Scholar 

Zhang, M.M., Choi, J., Daniilidis, K. et al. VAIS: A dataset for recognizing maritime imagery in the visible and infrared spectrums. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Boston, USA. pp 10–16 (2015).

Wu, T.M., Cao, Z., Gao, Z., et al.: STMixer: a one-stage sparse action detector. IEEE Trans. Pattern Anal. Mach. Intell. 46(10), 6842–6857 (2024)

MATH  Google Scholar 

Feng, Z.Q., Wang, X.G., Zhou, J.Y., et al.: MDJ: a multi-scale difference joint keyframe extraction algorithm for infrared surveillance video action recognition. Digital Signal Process 148, 104469 (2024)

Google Scholar 

Hong, Q., Sun, H., Li, B. et al. MpVit-Unet: Multi-path vision transformer unet for sellar region lesions segmentation. 2023 5th International Conference on Intelligent Medicine and Image Processing (IMIP), Tianjin, China. pp 51–58 (2023).

Quan, Z.Z., Chen, Q.S., Li, Y.J., et al.: ARCTIC: a knowledge distillation approach via attention-based relation matching and activation region constraint for RGB-to-Infrared videos action recognition. Comput. Vis. Image Underst. 237, 103853 (2023)

Google Scholar 

Wang, B.S., Wang, C.Y., Chiu, W.C. MCPNet: An interpretable classifier via multi-level concept prototypes. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA. pp 10885–10894 (2024).

Woo, S., Park, J., Lee, J.Y. et al. CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision. pp 3–19 (2018).

Zhang, H., Wu, C.J., Zhang, Z.Y. et al. ResNeSt: Split-attention networks. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. pp 2735–2745 (2022).

Quan, Y., Zhang, D., Zhang, L.Y., et al.: Centralized feature pyramid for object detection. IEEE Trans. Image Process. 32, 4341–4354 (2023)

ADS  MATH  Google Scholar 

Zhu, L., Wang, X.J., Ke, Z.H. et al. BiFormer: Vision transformer with bi-level routing attention. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp 10323–10333 (2023).

Comments (0)

No login
gif