HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES

Ahmet Ornek; Murat Ceylan

doi:10.17780/ksujes.1430479

TR EN

HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES

Öz

When a classification process is performed using Class Activation Maps, which is one of the Explainable Artificial Intelligence approaches, the areas influencing the classification on the input image can be revealed. In other words, it is demonstrated which part of the image the classifier model looks at to make a decision. In this study, a 200-class classification model was trained using the open-source dataset CUB 200 2011, and the classification results were visualized using the EigenCAM and HayCAM methods. When comparing object detection performances based on the areas influencing classification, the EigenCAM method reaches an IoU (Intersection over Union) value of 30.88%, while the HayCAM method reaches a value of 41.95%. The obtained results indicate that outputs derived using Principal Component Analysis (HayCAM) are better than those obtained using Singular Value Decomposition (EigenCAM).

Anahtar Kelimeler

Destekleyen Kurum

Huawei Türkiye Ar-Ge Merkezi

Etik Beyan

Makale, yazarların kendi araştırmalarını ve analizlerini güvenilir ve eksiksiz bir şekilde yansıtmaktadır.

Teşekkür

Huawei Türkiye Ar-Ge Merkezi

Kaynakça

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., & Barbado, A. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018). Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 839–847). https://doi.org/10.1109/WACV.2018.00097
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90
Kornblith, S., Shlens, J., & Le, Q. V. (2019). Do better imagenet models transfer better? In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 2661–2671). https://doi.org/10.1109/CVPR.2019.00277
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25. https://doi.org/10.1145/3065386
Muhammad, M. B., & Yeasin, M. (2020). Eigen-cam: Class activation map using principal components. In 2020 international joint conference on neural networks (ijcnn) (pp. 1–7). https://doi.org/10.1109/IJCNN48605.2020.9206626
Ornek. (2023). Developing a new explainable artificial intelligence method (doctoral dissertation). Konya Technical University. (No DOI available for the dissertation)
Ornek, A., & Ceylan, M. (2022). Haycam: A novel visual explanation for deep convolutional neural networks. Traitement Du Signal, 39 (5), 1711–1719. https://doi.org/10.18280/ts.390529

Ornek, A. H., & Ceylan, M. (2022). A novel approach for visualization of class activation maps with reduced dimensions. In 2022 innovations in intelligent systems and applications conference (asyu) (pp. 1–5). https://doi.org/10.1109/ASYU56188.2022.9925400
Ornek, A. H., & Ceylan, M. (2023). Codcam: A new ensemble visual explanation for classification of medical thermal images. Quantitative InfraRed Thermography Journal, 1–25. https://doi.org/10.1080/17686733.2023.2167459
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the ieee international conference on computer vision (pp. 618–626). https://doi.org/10.1109/ICCV.2017.74
Shao, F., Chen, L., Shao, J., Ji, W., Xiao, S., Ye, L., Xiao, J. (2022). Deep learning for weakly-supervised object detection and localization: A survey. Neurocomputing. https://doi.org/10.48550/arXiv.2105.12694
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556
Stewart, G. W. (1993). On the early history of the singular value decomposition. SIAM review, 35 (4), 551–566. https://doi.org/10.1137/1035134
van der Velden, B. H., Kuijf, H. J., Gilhuijs, K. G., & Viergever, M. A. (2022). Explainable artificial intelligence (xai) in deep learning-based medical image analysis. Medical Image Analysis, 79 , 102470. doi: https://doi.org/10.1016/j.media.2022.102470
Wah, C., Branson, S., Welinder, P., Perona, P., & Belongie, S. (2011). Caltech (Tech. Rep. No. CNS-TR-2011-001). California Institute of Technology. (Technical reports typically do not have DOIs)
Wu, S. X., Wai, H.-T., Li, L., & Scaglione, A. (2018). A review of distributed algorithms for principal component analysis. Proceedings of the IEEE, 106 (8), 1321–1340. https://doi.org/10.1109/JPROC.2018.2846568
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 2921–2929). https://doi.org/10.1109/CVPR.2016.319 Zhou, D., Fang, J., Song, X., Guan, C., Yin, J., Dai, Y., & Yang, R. (2019). Iou loss for 2d/3d object detection. In 2019 international conference on 3d vision (3dv) (pp. 85–94). https://doi.org/10.1109/3DV.2019.00019

Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Görüşü, Görüntü İşleme, Derin Öğrenme

Bölüm

Araştırma Makalesi

Yazarlar

Ahmet Ornek ^*
0000-0001-7254-9316
Türkiye

Murat Ceylan
0000-0001-6503-9668
Türkiye

Yayımlanma Tarihi

3 Eylül 2024

Gönderilme Tarihi

2 Şubat 2024

Kabul Tarihi

22 Temmuz 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1430479

IZ

https://izlik.org/JA78LA67FG

Kaynak Göster

RIS / Bibtex

APA

Ornek, A., & Ceylan, M. (2024). HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 1078-1088. https://doi.org/10.17780/ksujes.1430479

AMA

1.Ornek A, Ceylan M. HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi. 2024;27(3):1078-1088. doi:10.17780/ksujes.1430479

Chicago

Ornek, Ahmet, ve Murat Ceylan. 2024. “HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 27 (3): 1078-88. https://doi.org/10.17780/ksujes.1430479.

EndNote

Ornek A, Ceylan M (01 Eylül 2024) HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 27 3 1078–1088.

IEEE

[1]A. Ornek ve M. Ceylan, “HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES”, Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, c. 27, sy 3, ss. 1078–1088, Eyl. 2024, doi: 10.17780/ksujes.1430479.

ISNAD

Ornek, Ahmet - Ceylan, Murat. “HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 27/3 (01 Eylül 2024): 1078-1088. https://doi.org/10.17780/ksujes.1430479.

JAMA

1.Ornek A, Ceylan M. HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi. 2024;27:1078–1088.

MLA

Ornek, Ahmet, ve Murat Ceylan. “HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, c. 27, sy 3, Eylül 2024, ss. 1078-8, doi:10.17780/ksujes.1430479.

Vancouver

1.Ahmet Ornek, Murat Ceylan. HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi. 01 Eylül 2024;27(3):1078-8. doi:10.17780/ksujes.1430479

FARKLI ÖLÇEKLERDE ZAYIF DENETİMLİ NESNE TESPİTİ İÇİN HAYCAM VE EIGEN KARŞILAŞTIRILMASI

Öz

Anahtar Kelimeler

HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES

Öz

Anahtar Kelimeler

Destekleyen Kurum

Etik Beyan

Teşekkür

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster