OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations
Wolfgang Stammer, Marius Memmel, Patrick Schramowski, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 10307-10318
Open Access | Times Cited: 9

Showing 9 citing articles:

Learning Bottleneck Concepts in Image Classification
Bowen Wang, Liangzhi Li, Yuta Nakashima, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
Open Access | Times Cited: 24

Compositionally Equivariant Representation Learning
Xiao Liu, Pedro Sanchez, Spyridon Thermos, et al.
IEEE Transactions on Medical Imaging (2024) Vol. 43, Iss. 6, pp. 2169-2179
Open Access | Times Cited: 2

Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models
Poulami Sinhamahapatra, Lena Heidemann, Maureen Monnet, et al.
Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2023), pp. 878-887
Open Access | Times Cited: 5

Leaf cultivar identification via prototype-enhanced learning
Yiyi Zhang, Zhiwen Ying, Ying Zheng, et al.
Computer Vision and Image Understanding (2024) Vol. 250, pp. 104221-104221
Open Access | Times Cited: 1

Anomaly detection in multifactor data
Hans Skvara, Václav Šmídl, Tomás̆ Pevný
Neural Computing and Applications (2024)
Closed Access

Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning
Emanuele Marconato, Andrea Passerini, Stefano Teso
Entropy (2023) Vol. 25, Iss. 12, pp. 1574-1574
Open Access | Times Cited: 1

FAPI-Net: A lightweight interpretable network based on feature augmentation and prototype interpretation
Xiaoyang Zhao, Xinzheng Xu, Chen Hu, et al.
Mathematical Biosciences & Engineering (2023) Vol. 20, Iss. 4, pp. 6191-6214
Open Access

Interpretable attribution based on concept vectors
Lu Liu, Zhichao Lian
(2023)
Closed Access

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