Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Sadeghi, M. H., Sina, S., Omidi, H., Farshchitabrizi, A. H., & Alavi, M. (2024) · Polish Journal of Radiology · doi:10.5114/pjr.2024.134817 · Cited by 54

Probabilistic simulation of quantum circuits using a deep-learning architecture

Carrasquilla, J., Luo, D., Pérez, F., Milsted, A., Clark, B. K., Volkovs, M., & Aolita, L. (2021) · Physical Review A · doi:10.1103/physreva.104.032610 · Cited by 53

Learning Spatio-temporal features to detect manipulated facial videos created by the Deepfake techniques

Nguyen, X. H., Tran, T. S., Le, V. T., Nguyen, K. D., & Truong, D.-T. (2021) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2021.301108 · Cited by 53

Emotion recognition from thermal infrared images using deep Boltzmann machine

Wang, S., He, M., Gao, Z., He, S., & Ji, Q. (2014) · Frontiers of Computer Science · doi:10.1007/s11704-014-3295-3 · Cited by 51

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology

Wang, F.-A., Zhuang, Z., Gao, F., He, R., Zhang, S., Wang, L., Liu, J., & Li, Y. (2024) · Genome Biology · doi:10.1186/s13059-024-03293-9 · Cited by 51

CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images

Yang, P., Yin, X., Lu, H., Hu, Z., Zhang, X., Jiang, R., & Lv, H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102539 · Cited by 50

A novel content-selected image encryption algorithm based on the LS chaotic model

Wang, J., Liu, L., Xu, M., & Li, X. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.08.007 · Cited by 49

MemFlow: Optical Flow Estimation and Prediction with Memory

Dong, Q., & Fu, Y. (2024) · arXiv · doi:10.48550/arxiv.2404.04808 · Cited by 49

Semi-supervised facial expression recognition using reduced spatial features and Deep Belief Networks

Rajendra Kurup, A., Ajith, M., & Martínez Ramón, M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.029 · Cited by 49

Towards DeepFake video forensics based on facial textural disparities in multi-color channels

Xia, Z., Qiao, T., Xu, M., Zheng, N., & Xie, S. (2022) · Information Sciences · doi:10.1016/j.ins.2022.06.003 · Cited by 47

Approaching the Real-World: Supporting Activity Recognition Training with Virtual IMU Data

Kwon, H., Wang, B., Abowd, G. D., & Plötz, T. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3478096 · Cited by 48

Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities

Zhang, G., Luo, Y., Dai, X., & Dai, Z. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad333 · Cited by 47

Vision transformer attention with multi-reservoir echo state network for anomaly recognition

Ullah, W., Hussain, T., & Baik, S. W. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103289 · Cited by 47

Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach

Bayram, F., & Ahmed, B. S. (2025) · ACM Computing Surveys · doi:10.1145/3708497 · Cited by 48

A review of explainable AI techniques and their evaluation in mammography for breast cancer screening

Shifa, N., Saleh, M., Akbari, Y., & Al Maadeed, S. (2025) · Clinical Imaging · doi:10.1016/j.clinimag.2025.110492 · Cited by 45

A hierarchical deep convolutional regression framework with sensor network fail-safe adaptation for acoustic-emission-based structural health monitoring

Guo, S., Ding, H., Li, Y., Feng, H., Xiong, X., Su, Z., & Feng, W. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109508 · Cited by 43

Automated ultrasound assessment of amniotic fluid index using deep learning

Cho, H. C., Sun, S., Min Hyun, C., Kwon, J.-Y., Kim, B., Park, Y., & Seo, J. K. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101951 · Cited by 43

Deep learning-based real time detection for cardiac objects with fetal ultrasound video

Iriani Sapitri, A., Nurmaini, S., Naufal Rachmatullah, M., Tutuko, B., Darmawahyuni, A., Firdaus, F., Rini, D. P., & Islami, A. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101150 · Cited by 42

PVSPE: A pyramid vision multitask transformer network for spacecraft pose estimation

Yang, H., Xiao, X., Yao, M., Xiong, Y., Cui, H., & Fu, Y. (2024) · Advances in Space Research · doi:10.1016/j.asr.2024.05.011 · Cited by 41

ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding

Guo, Z., Tang, Y., Zhang, R., Wang, D., Wang, Z., Zhao, B., & Li, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01410 · Cited by 44

Ag-YOLO: A Real-Time Low-Cost Detector for Precise Spraying With Case Study of Palms

Qin, Z., Wang, W., Dammer, K.-H., Guo, L., & Cao, Z. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.753603 · Cited by 41

Real-time tool detection in smart manufacturing using You-Only-Look-Once (YOLO)v5

Zendehdel, N., Chen, H., & Leu, M. C. (2023) · Manufacturing Letters · doi:10.1016/j.mfglet.2023.08.062 · Cited by 40

Artificial intelligence approaches to human-microbiome protein–protein interactions

Lim, H., Cankara, F., Tsai, C.-J., Keskin, O., Nussinov, R., & Gursoy, A. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102328 · Cited by 39

Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images

Zhang, J., Cao, Y., Wang, Y., Wen, C., & Chen, C. W. (2018) · Proceedings of the 26th ACM international conference on Multimedia · doi:10.1145/3240508.3240653 · Cited by 40

Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network

Li, N., Chang, F., & Liu, C. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.023 · Cited by 36

Mapping oil palm density at country scale: An active learning approach

Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112479 · Cited by 36

Bidirectional Transformer GAN for Long-term Human Motion Prediction

Zhao, M., Tang, H., Xie, P., Dai, S., Sebe, N., & Wang, W. (2023) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3579359 · Cited by 38

Unsupervised person re-identification with multi-label learning guided self-paced clustering

Li, Q., Peng, X., Qiao, Y., & Hao, Q. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108521 · Cited by 35

Video ReCap: Recursive Captioning of Hour-Long Videos

Islam, M. M., Ho, N., Yang, X., Nagarajan, T., Torresani, L., & Bertasius, G. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01723 · Cited by 34

Gliding and saccadic gaze gesture recognition in real time

Rozado, D., Agustin, J. S., Rodriguez, F. B., & Varona, P. (2012) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2070719.2070723 · Cited by 32

Optimization of the area under the ROC curve using neural network supervectors for text-dependent speaker verification

Mingote, V., Miguel, A., Ortega, A., & Lleida, E. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101078 · Cited by 32

Improved performance and robustness of multi-task representation learning with consistency loss between pretexts for intracranial hemorrhage identification in head CT

Kyung, S., Shin, K., Jeong, H., Kim, K. D., Park, J., Cho, K., Lee, J. H., Hong, G., & Kim, N. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102489 · Cited by 32

A new generation of brain-computer interfaces driven by discovery of latent EEG-fMRI linkages using tensor decomposition.

Deshpande, G., Rangaprakash, D., Oeding, L., Cichocki, A., & Hu, X. P. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00246 · Cited by 29

PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis

Song, M., Wang, J., Yu, Z., Wang, J., Yang, L., Lu, Y., Li, B., Wang, X., Wang, X., Huang, Q., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103248 · Cited by 31

RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components

Koyun, O. C., Keser, R. K., Şahin, S. O., Bulut, D., Yorulmaz, M., Yücesoy, V., & Töreyin, B. U. (2024) · ACS Omega · doi:10.1021/acsomega.3c09247 · Cited by 32

Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations

Wilde, J., Serjeant, S., Bromley, J. M., Dickinson, H., Koopmans, L. V. E., & Metcalf, R. B. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac562 · Cited by 31

Missing-modality enabled multi-modal fusion architecture for medical data

Wang, M., Fan, S., Li, Y., Xie, Z., & Chen, H. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104796 · Cited by 31

Pathological Voice Detection and Classification Based on Multimodal Transmission Network

Geng, L., Liang, Y., Shan, H., Xiao, Z., Wang, W., & Wei, M. (2025) · Journal of Voice · doi:10.1016/j.jvoice.2022.11.018 · Cited by 30

Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine

Heinemann, T., Kornauth, C., Severin, Y., Vladimer, G. I., Pemovska, T., Hadzijusufovic, E., Agis, H., Krauth, M.-T., Sperr, W. R., Valent, P., et al. (2022) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-21-0219 · Cited by 28

Why pay more? A simple and efficient named entity recognition system for tweets

Suman, C., Reddy, S. M., Saha, S., & Bhattacharyya, P. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114101 · Cited by 27

YOLOv5-CD: Strip steel surface defect detection method based on coordinate attention and a decoupled head

Wang, B., Wang, M., Yang, J., & Luo, H. (2023) · Measurement: Sensors · doi:10.1016/j.measen.2023.100909 · Cited by 27

Visual Question Generation: The State of the Art

Patil, C., & Patwardhan, M. (2021) · ACM Computing Surveys · doi:10.1145/3383465 · Cited by 25

A regularization method to improve adversarial robustness of neural networks for ECG signal classification

Ma, L., & Liang, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105345 · Cited by 26

The convergence of AI, IoT, and big data for advancing flood analytics research

Samadi, S. (2022) · Frontiers in Water · doi:10.3389/frwa.2022.786040 · Cited by 25

Retrieval-Augmented Egocentric Video Captioning

Xu, J., Huang, Y., Hou, J., Chen, G., Zhang, Y., Feng, R., & Xie, W. (2024) · arXiv · doi:10.48550/arxiv.2401.00789 · Cited by 24

Robust 3D face modeling and reconstruction from frontal and side images

Jin, H., Wang, X., Zhong, Z., & Hua, J. (2017) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2016.11.001 · Cited by 24

MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning

Liu, B., Chen, C., Gong, Z., Liao, C., Wang, H., Lei, Z., Liang, M., Chen, D., Shen, M., Zhou, H., et al. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671609 · Cited by 23

Cross-modal multiscale multi-instance learning for long-term ECG classification

Chen, L., Lian, C., Zeng, Z., Xu, B., & Su, Y. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119230 · Cited by 23

Quality assessment in the era of large models: A survey

Zhang, Z., Zhou, Y., Li, C., Zhao, B., Liu, X., & Zhai, G. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3722559 · Cited by 23

Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)

Thukral, M., Dhekane, S. G., Hiremath, S. K., Haresamudram, H., & Ploetz, T. (2024) · arXiv · doi:10.48550/arxiv.2405.12368 · Cited by 21

Enhancing Debris Flow Warning via Machine Learning Feature Reduction and Model Selection

Zhou, Q., Tang, H., Hibert, C., Chmiel, M., Walter, F., Dietze, M., & Turowski, J. M. (2025) · Journal of Geophysical Research: Earth Surface · doi:10.1029/2024jf008094 · Cited by 20

Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning

Zhang, Z., Mao, S., Coyle, J., & Sejdić, E. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102218 · Cited by 18

GAN-MVAE: a discriminative latent feature generation framework for generalized zero-shot learning

Ma, P., Lu, H., Yang, B., & Ran, W. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.02.002 · Cited by 18

RAMS: Remote and automatic mammogram screening

Cogan, T., Cogan, M., & Tamil, L. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.01.024 · Cited by 18

A comprehensive review of AI-Based detection of Arrhythmia using Electrocardiogram (ECG)

Ayyub, A., Politis, C., & Usman, M. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110594 · Cited by 18

Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols

Li, B. M., Castorina, L. V., Valdés Hernández, M. D. C., Clancy, U., Wiseman, S. J., Sakka, E., Storkey, A. J., Jaime Garcia, D., Cheng, Y., Doubal, F., et al. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.887633 · Cited by 17

Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes

Meirelles, A. L., Kurc, T., Saltz, J., & Teodoro, G. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106828 · Cited by 17

Artificial intelligence in the experimental determination and prediction of macromolecular structures

Thorn, A. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102368 · Cited by 16

Open-world continual learning: Unifying novelty detection and continual learning

Kim, G., Xiao, C., Konishi, T., Ke, Z., & Liu, B. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2024.104237 · Cited by 19

Self-supervised Regularization for Text Classification

Zhou, M., Li, Z., & Xie, P. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00389 · Cited by 14

DeepFake detection in the AIGC era: A survey, benchmarks, and future perspectives

Xie, S., Qiao, T., Li, S., Zhang, X., Zhou, J., & Feng, G. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103740 · Cited by 14

Lightweight deep learning for visual perception: A survey of models, compression strategies, and edge deployment challenges

Raza, S. M., Abidi, S. M. H., Masuduzzaman, M., & Shin, S. Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131357 · Cited by 13

Unit Testing for Concepts in Neural Networks

Lovering, C., & Pavlick, E. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00514 · Cited by 12

Analysis of RNA translation with a deep learning architecture provides new insight into translation control

(2025) · doi:10.1093/nar/gkaf277 · Cited by 12

GUIPilot: A Consistency-Based Mobile GUI Testing Approach for Detecting Application-Specific Bugs

Liu, R., Teoh, X., Lin, Y., Chen, G., Ren, R., Poshyvanyk, D., & Dong, J. S. (2025) · Proceedings of the ACM on Software Engineering · doi:10.1145/3728909 · Cited by 12

A novel detection fusion network for solid waste sorting

Yu, Y., Zou, S., & Yin, K. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420941779 · Cited by 11

A multi-stage transfer learning strategy for diagnosing a class of rare laryngeal movement disorders

Yao, Y., Powell, M., White, J., Feng, J., Fu, Q., Zhang, P., & Schmidt, D. C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107534 · Cited by 10

Context in Photo Albums: Understanding and Modeling User Behavior in Clustering and Selection

Kuzovkin, D., Pouli, T., Meur, O. L., Cozot, R., Kervec, J., & Bouatouch, K. (2019) · ACM Transactions on Applied Perception · doi:10.1145/3333612 · Cited by 10

TransHLA: a Hybrid Transformer model for HLA-presented epitope detection

Lu, T., Wang, X., Nie, W., Huo, M., & Li, S. (2025) · GigaScience · doi:10.1093/gigascience/giaf008 · Cited by 10

Algebraically rigorous quaternion framework for the neural network pose estimation problem

Lin, C., Hanson, A. J., & Hanson, S. M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01296 · Cited by 9

On evaluating the effects of feedback for Sign language learning using Explainable AI

Paudyal, P., Banerjee, A., & Gupta, S. (2020) · Companion Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3379336.3381469 · Cited by 9

DeepGuard: Backdoor Attack Detection and Identification Schemes in Privacy-Preserving Deep Neural Networks

Chen, C., Wei, L., Zhang, L., Peng, Y., & Ning, J. (2022) · Security and Communication Networks · doi:10.1155/2022/2985308 · Cited by 8

Computing Education Research Today

Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7

Rapid identification of metal resistance genes using an enhanced ResNet deep learning model trained on a largely expanded BacMet-based database

(2025) · doi:10.1016/j.jhazmat.2025.139625 · Cited by 7

Do Vision and Language Encoders Represent the World Similarly?

Maniparambil, M., Akshulakov, R., Dahou Djilali, Y. A., Seddik, M. E. A., Narayan, S., Mangalam, K., & O'Connor, N. E. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01359 · Cited by 7

Object-Centric Scene Representations Using Active Inference

Van de Maele, T., Verbelen, T., Mazzaglia, P., Ferraro, S., & Dhoedt, B. (2024) · Neural Computation · doi:10.1162/neco_a_01637 · Cited by 5

Transfer learning for multimodal dialog

Palaskar, S., Sanabria, R., & Metze, F. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101093 · Cited by 6

Introspective perception for mobile robots

Rabiee, S., & Biswas, J. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103999 · Cited by 6

A Descriptive Review of Image Datasets for Accessible Alternative Descriptions in STEM Domains

Cardia, M., Buzzi, M., Galesi, G., & Leporini, B. (2025) · Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter · doi:10.1145/3750069.3750122 · Cited by 5

Cross-modal similar clinical case retrieval using a modular model based on contrastive learning and k-nearest neighbor search

Fang, S., Hong, S., Li, Q., Li, P., Coats, T., Zou, B., & Kong, G. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105680 · Cited by 5

Learning skeletal articulations with neural blend shapes

Li, P., Aberman, K., Hanocka, R., Liu, L., Sorkine-Hornung, O., & Chen, B. (2021) · ACM Transactions on Graphics · doi:10.1145/3476576.3476702 · Cited by 4

AgCV: An Agentic framework for automating computer vision application

Saxena, A., Chaudhari, A. Y., & Gupta, A. (2025) · MethodsX · doi:10.1016/j.mex.2025.103424 · Cited by 4

Toward Transformer-compatible multivariate time series learning via visibility graph-based structural encoding

Chen, T., Ren, X., Lai, J., Tan, H., Liu, F., & Chan, W. K. V. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114389 · Cited by 3

CLFormer: a cross-lingual transformer framework for temporal forgery localization

Cheng, H., Liu, H., Cai, J., & Ye, L. (2025) · Visual Intelligence · doi:10.1007/s44267-025-00084-z · Cited by 2

FISMI-DRL: A Framework for Interactive Segmentation of Medical Image Based On Deep Reinforcement Learning

Qian, C., Yang, H., & Li, J. (2023) · 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc53992.2023.10393973 · Cited by 1

BigVectorBench: Heterogeneous Data Embedding and Compound Queries are Essential in Evaluating Vector Databases

Kang, G., Ge, Z., Hu, J., Zhang, X., Wang, L., & Zhan, J. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3718057.3718078 · Cited by 1

Unbiased Embodied Visual Representation Learning with Causal Inference and Cross-Modality Alignment

Kang, J., Chen, B., Zhong, P., Wang, Y., Yang, H., & Sheng, Y. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3760261

Epi4Ab: a data-driven prediction model of conformational epitopes for specific antibody VH/VL families and CDRs sequences

Tran, N. D., Subramani, K., & Su, C. T.-T. (2025) · mAbs · doi:10.1080/19420862.2025.2531227