The DOI matches its Crossref record; 50 of the 52 references it cites were found in a registry or an index.
Transformer Interpretability Beyond Attention Visualization
Open accessAbstract
Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In order to visualize the parts of the image that led to a certain classification, existing methods either rely on the obtained attention maps or employ heuristic propagation along the attention graph. In this work, we propose a novel way to compute relevancy for Transformer networks. The method assigns local relevance based on the Deep Taylor Decomposition principle and then propagates these relevancy scores through the layers. This propagation involves attention layers and skip connections, which challenge existing methods. Our solution is based on a specific formulation that is shown to maintain the total relevancy across layers. We benchmark our method on very recent visual Transformer networks, as well as on a text classification problem, and demonstrate a clear advantage over the existing explainability methods. Our code is available at: https://github.com/hila-chefer/Transformer-Explainability.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Research works printed but not linked: 3
- radford, 2019, Language Models are Unsupervised Multitask Learners
- erhan, 2009, Visualizing higher-layer features of a deep network, University of Montreal, 1341, 1
- 10.1109/ICCV.2019.00304 · DOI
Cited by
Sorted by citations
Items 201–300 of 903
PDiscoNet: Semantically consistent part discovery for fine-grained recognition
van der Klis, R., Alaniz, S., Mancini, M., Dantas, C. F., Ienco, D., Akata, Z., & Marcos, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00179 · Cited by 22
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods
Hesse, R., Schaub-Meyer, S., & Roth, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00368 · Cited by 22
Explainability of Vision Transformers: a Comprehensive Review and New PerspectivesPreprint
Kashefi, R., Barekatain, L., Sabokrou, M., & Aghaeipoor, F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5055345 · Cited by 26
Fujii, Y., Uchida, D., Sato, R., Obata, T., Matsumi, A., Miyamoto, K., Morimoto, K., Terasawa, H., Yamazaki, T., Matsumoto, K., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-72312-3 · Cited by 21
Introduction to deep learning methods for multi‐species predictions
Hu, Y., Si‐Moussi, S., & Thuiller, W. (2024) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14466 · Cited by 29
Learning From Synthetic InSAR With Vision Transformers: The Case of Volcanic Unrest Detection
Bountos, N. I., Michail, D., & Papoutsis, I. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3180891 · Cited by 25
Revisiting a kNN-Based Image Classification System with High-Capacity Storage
Nakata, K., Ng, Y., Miyashita, D., Maki, A., Lin, Y.-C., & Deguchi, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_26 · Cited by 24
Understanding How Encoder-Decoder Architectures AttendPreprint
Aitken, K., Ramasesh, V., Cao, Y., & Maheswaranathan, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.15253 · Cited by 32
He, T. C., Chen, Y., Wang, L., & Cheng, H. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3400790 · Cited by 22
ResMT: A hybrid CNN-transformer framework for glioma grading with 3D MRI
Cui, H., Ruan, Z., Xu, Z., Luo, X., Dai, J., & Geng, D. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109745 · Cited by 23
Wang, Z., & Liu, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3575440 · Cited by 20
ContrXT: Generating contrastive explanations from any text classifier
Malandri, L., Mercorio, F., Mezzanzanica, M., Nobani, N., & Seveso, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.11.016 · Cited by 20
Making a Bird AI Expert Work for You and Me
Chang, D., Pang, K., Du, R., Tong, Y., Song, Y.-Z., Ma, Z., & Guo, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3274593 · Cited by 22
Emotion Forecasting: A Transformer-Based Approach
Paz-Arbaizar, L., López‐Castromán, J., Artés-Rodrı́guez, A., Olmos, P. M., & Ramírez, D. (2025) · Journal of Medical Internet Research · doi:10.2196/63962 · Cited by 20
Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation
Zhang, Z., Gu, J., Chowdhury, A. S., Mai, Z., Carlyn, D., Berger-Wolf, T. Y., Su, Y., & Chao, W.-L. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00898 · Cited by 23
Zesch, T., Horbach, A., & Zehner, F. (2023) · Educational Measurement Issues and Practice · doi:10.1111/emip.12544 · Cited by 26
Evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging
Chung, M., Won, J. B., Kim, G., Kim, Y., & Özbulak, U. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77610-6_11 · Cited by 21
A deep learning system for detecting systemic lupus erythematosus from retinal images
Li, T., Lin, S., Guan, Z., Zhou, Y., Zeng, D., Wang, Z., Zhou, Y., Fang, P., Yu, S., Liu, R., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102203 · Cited by 18
Graph Node Based Interpretability Guided Sample Selection for Active Learning
Mahapatra, D., Poellinger, A., & Reyes, M. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3215017 · Cited by 18
Local 3D Editing via 3D Distillation of CLIP Knowledge
Hyung, J., Hwang, S., Kim, D., Lee, H., & Choo, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01219 · Cited by 18
Knowledge Graphs and Their Applications in Drug Discovery
James, T., & Hennig, H. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3449-3_9 · Cited by 18
Vision transformer based interpretable metabolic syndrome classification using retinal Images
Lee, T. K., Kim, S. Y., Choi, H. J., Choe, E. K., & Sohn, K.-A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01588-0 · Cited by 17
Explainable AI: XAI-guided context-aware data augmentation
Mersha, M. A., Yigezu, M. G., Tonja, A. L., Shakil, H., Iskandar, S., Kolesnikova, O., & Kalita, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128364 · Cited by 24
Rajpoot, R., Jain, S., & Semwal, V. B. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110515 · Cited by 17
Rahman, M. I. (2025) · Journal of Transformative Technologies and Sustainable Development · doi:10.1007/s41314-025-00079-0 · Cited by 17
García-Olano, D., Onoe, Y., & Ghosh, J. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524648 · Cited by 20
Liu, Q., Jiang, N., Hao, Y., Hao, C., Wang, W., Bian, T., Wang, X., Li, H., Zhang, Y., Kang, Y., et al. (2023) · Cancer Medicine · doi:10.1002/cam4.6437 · Cited by 17
An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis
Han, L., Zhang, T., Huang, Y., Dou, H., Wang, X., Gao, Y., Lu, C., Tan, T., & Mann, R. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43999-5_5 · Cited by 17
MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning
Ding, W., Geng, Y., Huang, J., Ju, H., Wang, H., & Lin, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3326283 · Cited by 17
Multi-perspective neural network for dual drug repurposing in Alzheimer’s disease
Zhao, L., Li, Z., Chen, G., Yin, Y., & Chen, C. Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111195 · Cited by 17
Explaining through Transformer Input Sampling
Englebert, A., Stassin, S., Nanfack, G., Mahmoudi, S. A., Siebert, X., Cornu, O., & Vleeschouwer, C. D. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00088 · Cited by 17
RSTC: Residual Swin Transformer Cascade to approximate Taylor expansion for image denoising
Liu, J., Yang, Y., Xu, B., Yu, H., Zhang, Y., Li, Q., & Huang, Z. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104132 · Cited by 17
Jović, A., Frid, N., Brkić, K., & Cifrek, M. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108153 · Cited by 17
GMAR: Gradient-Driven Multi-Head Attention Rollout for Vision Transformer Interpretability
Jo, S., Jang, G., & Park, H. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084559 · Cited by 22
Ferrando, J., Gállego, G. I., Alastruey, B., Escolano, C., & Costa‐jussà, M. R. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.599 · Cited by 25
Mylonas, N., Mollas, I., & Tsoumakas, G. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00962-4 · Cited by 18
Liang, M., Wang, R., Liang, J., Wang, L., Li, B., Jia, X., Zhang, Y., Chen, Q., Zhang, T., & Zhang, C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3326467 · Cited by 15
Occlusion Sensitivity Analysis with Augmentation Subspace Perturbation in Deep Feature Space
Valois, P. H. V., Niinuma, K., & Fukui, K. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00476 · Cited by 16
Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers
Phan, H., Le, C. X., Le, V., He, Y., & Nguyen, A. T. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00618 · Cited by 18
Sibilano, E., Buongiorno, D., Lassi, M., Grippo, A., Bessi, V., Sorbi, S., Mazzoni, A., Bevilacqua, V., & Brunetti, A. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3390606 · Cited by 17
A Counterfactual Framework for Learning and Evaluating Explanations for Recommender Systems
Barkan, O., Bogina, V., Gurevitch, L., Asher, Y., & Koenigstein, N. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645560 · Cited by 24
Improved Visual Grounding through Self-Consistent Explanations
He, R., Cascante-Bonilla, P., Yang, Z., Berg, A. C., & Ordóñez, V. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01244 · Cited by 20
Multimodal fusion based few-shot network intrusion detection system
Xu, C., Zhan, Y., Wang, Z., & Yang, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-05217-4 · Cited by 15
Tang, Q., Cen, X., & Pan, C. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022457 · Cited by 16
Amador, K., Winder, A. J., Fiehler, J., Wilms, M., & Forkert, N. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_62 · Cited by 15
Chen, H., Wang, Y., Peng, J., Zhang, R., & Jiang, P. (2023) · Applied Sciences · doi:10.3390/app13095589 · Cited by 15
Qi, J., Ruan, G., Ping, Y., Xiao, Z., Liu, K., Cheng, Y., Liu, R., Zhang, B., Zhi, M., Chen, J., et al. (2023) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848231170945 · Cited by 15
Automated deformation detection and interpretation using InSAR data and a multi-task ViT model
Abdallah, M., Younis, S., Wu, S., & Ding, X. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103758 · Cited by 15
Manifestations of xenophobia in AI systems
Tomašev, N., Maynard, J. L., & Gabriel, I. (2024) · AI & Society · doi:10.1007/s00146-024-01893-4 · Cited by 22
On the Faithfulness of Vision Transformer Explanations
Wu, J., Kang, W., Tang, H., Hong, Y., & Yan, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01040 · Cited by 22
Aeroelastic force prediction via temporal fusion transformers
Montoya, M. C., Mishra, A., Verma, S., Mures, O. A., & Rubio-Medrano, C. E. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13381 · Cited by 16
Yang, L., Wan, Y., & Pan, F. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01446-1 · Cited by 16
Cho, K., Kim, K., Kim, J., Park, S., Kim, J., Yun, J., Ahn, Y., Oh, S. Y., Lee, S. M., Seo, J. B., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102894 · Cited by 15
Token Transformation Matters: Towards Faithful Post-Hoc Explanation for Vision Transformer
Wu, J., Duan, B., Kang, W., Tang, H., & Yan, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01039 · Cited by 14
ICEv2: Interpretability, Comprehensiveness, and Explainability in Vision Transformer
Choi, H.-Y., Jin, S., & Han, K. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02290-6 · Cited by 14
Efficient convolutional dual-attention transformer for automatic modulation recognition
Yi, Z., Meng, H., Gao, L., He, Z., & Yang, M. (2024) · Applied Intelligence · doi:10.1007/s10489-024-06202-6 · Cited by 14
Liao, Y., Gao, Y., & Zhang, W. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111607 · Cited by 14
Multi-Tier Platform for Cognizing Massive Electroencephalogram
Chen, Z., Zhu, L., Yang, Z., & Zhang, R. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/342 · Cited by 14
Attribution rollout: a new way to interpret visual transformer
Xu, L., Yan, X., Ding, W., & Liu, Z. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04354-2 · Cited by 13
Computing and evaluating saliency maps for image classification: a tutorial
Gomez, T., & Mouchère, H. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.2.020801 · Cited by 13
Masked Text Modeling: A Self-Supervised Pre-training Method for Scene Text Detection
Wang, K., Xie, H., Wang, Y., Zhang, D., Qu, Y., Gao, Z., & Zhang, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612370 · Cited by 13
Cockrum, J. W., Nakashima, M., Ammoury, C., Rizkallah, D., Mauch, J., López, D., Wolinksy, D., Hwang, T., Kapadia, S. R., Svensson, L. G., et al. (2024) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2024.09.010 · Cited by 13
Alifuse: Aligning and Fusing Multimodal Medical Data for Computer-Aided Diagnosis
Chen, Q., & Yi, H. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821851 · Cited by 15
FGAM: A pluggable light-weight attention module for medical image segmentation
Qiu, Z., Hu, Y., Zhang, J., Chen, X., & Liu, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105628 · Cited by 13
Studying How to Efficiently and Effectively Guide Models with Explanations
Rao, S., Böhle, M., Parchami-Araghi, A., & Schiele, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00184 · Cited by 13
Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models
Barkan, O., Asher, Y., Eshel, A., Elisha, Y., & Koenigstein, N. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00105 · Cited by 14
A human activity recognition model based on deep neural network integrating attention mechanism
Xu, F., Gao, X., & Wang, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-98763-w · Cited by 15
Advances in Facial Micro-Expression Detection and Recognition: A Comprehensive Review
Shuai, T., Beng, N. S., Khalid, F., & Rahmat, R. W. B. O. K. (2025) · Information · doi:10.3390/info16100876 · Cited by 20
Towards Explainable Deep Learning in Computational Neuroscience: Visual and Clinical Applications
Mehmood, A., Mehmood, F., & Kim, J. (2025) · Mathematics · doi:10.3390/math13203286 · Cited by 14
Chromosome Cluster Type Identification Using a Swin Transformer
Joshi, I., Mondal, A. K., & Navab, N. (2023) · Applied Sciences · doi:10.3390/app13148007 · Cited by 12
Initialization Noise in Image Gradients and Saliency Maps
Woerl, A.-C., Disselhoff, J., & Wand, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00176 · Cited by 12
Rasoulian, A., Salari, S., & Xiao, Y. (2023) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2023-553a · Cited by 13
Kwak, B., Park, J., Kang, T., Jo, J., Lee, B., & Yoon, S. (2023) · ACS Omega · doi:10.1021/acsomega.3c05753 · Cited by 12
DeepCover: Advancing RNN test coverage and online error prediction using state machine extraction
Golshanrad, P., & Faghih, F. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.111987 · Cited by 14
Multi-modal recursive prompt learning with mixup embedding for generalization recognition
Jia, Y., Ye, X., Liu, Y., & Guo, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111726 · Cited by 12
Khan, S. R., & Nauman, M. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2023.9020025 · Cited by 13
Transformer-CNN hybrid network for improving PET time of flight prediction
Feng, X., Muhashi, A., Onishi, Y., Ota, R., & Liu, H. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad4c4d · Cited by 12
T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
Ntrougkas, M. V., Gkalelis, N., & Mezaris, V. (2024) · IEEE Access · doi:10.1109/access.2024.3405788 · Cited by 12
Streamlined jet tagging network assisted by jet prong structure
Hammad, A., & Nojiri, M. M. (2024) · Journal of High Energy Physics · doi:10.1007/jhep06(2024)176 · Cited by 12
A Convolutional-Transformer Model for FFR and iFR Assessment From Coronary Angiography
Mineo, R., Salanitri, F. P., Bellitto, G., Kavasidis, I., FILIPPO, O. D., Millesimo, M., Ferrari, G. M. D., Aldinucci, M., Giordano, D., Palazzo, S., et al. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3383283 · Cited by 13
Hu, J., Luo, J., Xu, Z., Liao, B., Dong, S., Peng, B., & Hou, G. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.08.014 · Cited by 13
Adaptive class token knowledge distillation for efficient vision transformer
Kang, M., Son, S., & Kim, D. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112531 · Cited by 15
Zhou, G., Xie, Y., Fu, Y., & Wang, Z. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106937 · Cited by 12
Wang, N., Wang, S., Li, M., Wu, L., Zhang, Z., Guan, Z., & Zhu, L. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3516579 · Cited by 14
Spatio-temporal transformers for decoding neural movement control
Candelori, B., Bardella, G., Spinelli, I., Ramawat, S., Pani, P., Ferraina, S., & Scardapane, S. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adaef0 · Cited by 15
Wan, Z., Wan, J., Cheng, W., Yu, J., Yan, Y., Tan, H., & Wu, J. (2023) · Applied Sciences · doi:10.3390/app13116569 · Cited by 11
ViT: Quantifying Chest X-Ray Images Using Vision Transformer & XAI Technique
Salini, Y., & Harikiran, J. (2023) · SN Computer Science · doi:10.1007/s42979-023-02204-2 · Cited by 11
Zhang, X., Wei, Y., & Wang, W. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3327552 · Cited by 12
All-pairs Consistency Learning for Weakly Supervised Semantic Segmentation
Sun, W., Zhang, Y., Qin, Z., Liu, Z., Cheng, L., Wang, F., Zhong, Y., & Barnes, N. M. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00090 · Cited by 11
PragFormer: Data-Driven Parallel Source Code Classification with Transformers
Harel, R., Kadosh, T., Hasabnis, N., Mattson, T. G., Pinter, Y., & Oren, G. (2024) · International Journal of Parallel Programming · doi:10.1007/s10766-024-00778-9 · Cited by 11
IVTP: Instruction-Guided Visual Token Pruning for Large Vision-Language Models
Huang, K., Zou, H., Ye, X., Wang, B., Xie, Z., & Liang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72643-9_13 · Cited by 17
MemesViTa: A Novel Multimodal Fusion Technique for Troll Memes Identification
Sultan, T., Rony, M. A. T., Islam, M. S., Saad, A., & El‐Shafai, W. (2024) · IEEE Access · doi:10.1109/access.2024.3505614 · Cited by 12
Quantifying interpretation reproducibility in Vision Transformer models with TAVAC
Zhao, Y., Agyemang, D., Liu, Y., Mahoney, J. M., & Li, S. (2024) · Science Advances · doi:10.1126/sciadv.abg0264 · Cited by 11
ViTGuard: Attention-aware Detection against Adversarial Examples for Vision Transformer
Sun, S., Nwodo, K., Sugrim, S., Stavrou, A., & Wang, H. (2024) · Annual Computer Security Applications Conference · doi:10.1109/acsac63791.2024.00100 · Cited by 13
Food Recognition with Visual Transformers
Bianco, S., Buzzelli, M., Chiriaco, G., Napoletano, P., & Piccoli, F. (2023) · IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin) · doi:10.1109/icce-berlin58801.2023.10375660 · Cited by 13
Bao, H., Li, W., Chen, H., Miao, H., Wang, Q., Tang, Z., Liu, F., & Wang, W. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103943 · Cited by 15
A privacy preservation method for multiple-source unstructured data in online social networks
Wang, C., Zhu, T., Xiong, P., Ren, W., & Choo, K. R. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102574 · Cited by 11
I Saw: A Self-Attention Weighted Method for Explanation of Visual Transformers
Mallick, R., Benois‐Pineau, J., & Zemmari, A. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897347 · Cited by 11
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?Preprint
Gonçalves, T., Rio-Torto, I., Teixeira, L. F., & Cardoso, J. S. (2022) · Research Square · doi:10.21203/rs.3.rs-1594205/v1 · Cited by 11
Interpreting Stress Detection Models Using SHAP and Attention for MuSe-Stress 2022
Park, H.-M., Kim, G., Oh, J., Messem, A. V., & Neve, W. D. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3488112 · Cited by 12
Metadata
- Authors
- Chefer, H., Gur, S., & Wolf, L.
- Affiliations
- Tel Aviv University (IL)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 782-791
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3112516115
- Cited by
- 903
- References
- 52
- Citations by year
- 2026: 185 · 2025: 226 · 2024: 193 · 2023: 178 · 2022: 90 · 2021: 31
- FWCI
- 56.82
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- European Research Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- transformer interpretability, vision transformer, attention visualization, local relevance, relevance propagation, attention layers, skip connections, explainable AI, text classification, image classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
