A survey on deep learning-based fine-grained object classification and semantic segmentation

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989

Proceedings of IEEE International Conference on Computer Vision

(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117

Class Discriminative Adversarial Learning for Unsupervised Domain Adaptation

Zhou, L., Ye, M., Zhu, X., Li, S., & Liu, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548143 · Cited by 20

Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics

Li, A., Zhang, K., & Wang, L. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1177-8 · Cited by 26

Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques

Minakshi, M., Bharti, P., McClinton, W., Mirzakhalov, J., Carney, R. M., & Chellappan, S. (2020) · ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3378393.3402260 · Cited by 19

An efficient image super resolution model with dense skip connections between complex filter structures in Generative Adversarial Networks

Sharma, S., & Kumar, V. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115780 · 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

A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images

Siriapisith, T., Kusakunniran, W., & Haddawy, P. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.806 · Cited by 19

Bird Species Classification Using Deep Learning

Anusha, P., & ManiSai, K. (2022) · International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) · doi:10.1109/iciccsp53532.2022.9862344 · Cited by 19

Fine-grained transfer learning based on deep feature decomposition for rotating equipment fault diagnosis

Dong, J., Su, D., Gao, Y., Wu, X., Jiang, H., & Chen, T. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acc04a · Cited by 19

Trusted Fine-Grained Image Classification through Hierarchical Evidence Fusion

Xu, Z., Yue, X., Lv, Y., Liu, W., & Li, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26265 · Cited by 19

Deep neural networks for efficient steganographic payload location

Sun, Y., Zhang, H., Zhang, T., & Wang, R. (2019) · Journal of Real-Time Image Processing · doi:10.1007/s11554-019-00849-y · Cited by 19

A Comprehensive Review of Deep Learning Architectures for Computer Vision Applications

Sarraf, A., Azhdari, M., & Sarraf, S. (2021) · American Scientific Research Journal for Engineering, Technology, and Sciences · Cited by 19

An efficient deep learning model for cultivar identification of a pistachio tree

Heidary-Sharifabad, A., Zarchi, M. S., Emadi, S., & Zarei, G. (2021) · British Food Journal · doi:10.1108/bfj-12-2020-1100 · Cited by 18

Improving Whole-Heart CT Image Segmentation by Attention Mechanism

Wang, W., Ye, C., Zhang, S., Xu, Y. D., & Wang, K. (2019) · IEEE Access · doi:10.1109/access.2019.2961410 · Cited by 19

Homeomorphism Alignment for Unsupervised Domain Adaptation

Zhou, L., Ye, M., Zhu, X., Xiao, S., Fan, X.-Q., & Neri, F. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01714 · Cited by 18

Brain programming is immune to adversarial attacks: Towards accurate and robust image classification using symbolic learning

Ibarra-Vázquez, G., Gustavo Olague, A., Chan-Ley, M., Puente, C., & Soubervielle‐Montalvo, C. (2022) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2022.101059 · Cited by 18

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Mamchur, D., Pekša, J., Clainche, S. L., & Vinuesa, R. (2022) · Sensors · doi:10.3390/s22062121 · Cited by 18

A review:deep learning technique for image classification

Aggarwal, V., & Kaur, G. (2018) · ACCENTS Transactions on Image Processing and Computer Vision · doi:10.19101/tipcv.2018.411003 · Cited by 17

Deep Model-Based Semi-Supervised Learning Way for Outlier Detection in Wireless Capsule Endoscopy Images

Gao, Y., Lu, W., Si, X., & Lan, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2991115 · Cited by 19

An Empirical Study on Using CNNs for Fast Radio Signal Prediction

Ozyegen, O., Mohammadjafari, S., Çevik, M., Mokhtari, K. E., Ethier, J., & Basar, A. (2022) · SN Computer Science · doi:10.1007/s42979-022-01022-2 · Cited by 17

Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks

Kyslytsyna, A., Xia, K., Kislitsyn, A., Kader, I. A. E., & Wu, Y. (2021) · Sensors · doi:10.3390/s21217405 · Cited by 17

Efficient instance segmentation using deep learning for species identification in fish markets

García-D’Urso, N., Galán-Cuenca, A., Climent-Pérez, P., Saval-Calvo, M., Azorín-López, J., & Fuster-Guilló, A. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892945 · Cited by 16

Fine-Grained Hierarchical Classification of Plant Leaf Images Using Fusion of Deep Models

Araujo, V. M., Britto, A. S., Brun, A. L., Koerich, A. L., & Oliveira, L. S. (2018) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2018.00011 · Cited by 16

Developing a deep neural network model for predicting carrots volume

Örnek, M. N., & Kahramanlı, H. (2021) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-021-00923-9 · Cited by 17

Transformers for Multi-Modal Image Analysis in Healthcare

Sagheer, S. V. M., Meghana, K. H., Ameer, P. M., Parayangat, M., & Abbas, M. A. E. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.063726 · Cited by 19

PDS-Net: A novel point and depth-wise separable convolution for real-time object detection

Junayed, M. S., Islam, M. B., Imani, H., & Aydın, T. (2022) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-022-00229-6 · Cited by 16

A Review of Latest Multi-instance Learning

Tian, Y., Hao, W., Jin, D., Chen, G., & Zou, A. (2020) · doi:10.1145/3445815.3445822 · Cited by 15

Deep learning integral imaging for three-dimensional visualization, object detection, and segmentation

Yi, F., Jeong, O., Moon, I., & Javidi, B. (2021) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2021.106695 · Cited by 15

Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques

Anilkumar, P. H., & Venugopal, P. (2022) · Security and Communication Networks · doi:10.1155/2022/6010912 · Cited by 16

Recursive Multi-Scale Channel-Spatial Attention for Fine-Grained Image Classification

Dichao, L. I. U., Wang, Y., Mase, K., & Kato, J. (2022) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7166 · Cited by 16

A Twofold Convolutional Regression Tracking Network With Temporal and Spatial Mechanism

Li, X., Huang, L., & Wei, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3077640 · Cited by 13

MambaReID: Exploiting Vision Mamba for Multi-Modal Object Re-Identification

Zhang, R., Xu, L., Song, Y., & Li, W. (2024) · Sensors · doi:10.3390/s24144639 · Cited by 13

Quick-CapsNet (QCN): A Fast Alternative to Capsule Networks

Shiri, P., Sharifi, R., & Baniasadi, A. (2020) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa50499.2020.9316525 · Cited by 14

A Fine-Grained Attention Model for High Accuracy Operational Robot Guidance

Chu, Y., Feng, D., Liu, Z., Zhang, L., Zhao, Z., Wang, Z., Feng, Z., & Xia, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3206388 · Cited by 15

Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN

Parras, J., Zazo, S., Pérez-Álvarez, I., & González, J. L. S. (2019) · Sensors · doi:10.3390/s19163530 · Cited by 14

Measurement Method and Application of a Deep Learning Digital Economy Scale Based on a Big Data Cloud Platform

Zhao, Y., & Zhou, Y. (2021) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.20220501.oa1 · Cited by 16

Challenges for computer vision as a tool for screening urban trees through street-view images

Arévalo-Ramirez, T., Alfaro, A., Figueroa, J., Ponce-Donoso, M., Saavedra, J. M., Recabarren, M., & Delpiano, J. F. (2024) · Urban forestry & urban greening · doi:10.1016/j.ufug.2024.128316 · Cited by 15

Multimodal fine-grained grocery product recognition using image and OCR text

Pettersson, T., Riveiro, M., & Löfström, T. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01549-9 · Cited by 22

A lightweight network architecture for traffic sign recognition based on enhanced LeNet-5 network

An, Y., Yang, C., & Zhang, S. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1431033 · Cited by 17

Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches

Ashimgaliyev, M., Matkarimov, B. T., Barlybayev, A., Li, R. Y. M., & Zhumadillayeva, А. (2024) · Applied Sciences · doi:10.3390/app14167281 · Cited by 13

Accelerator-Aware Fast Spatial Feature Network for Real-Time Semantic Segmentation

Kim, M., Park, B., & Chi, S. (2020) · IEEE Access · doi:10.1109/access.2020.3045147 · Cited by 14

From Sensors to Sustainability: Integrating Welfare, Management, and Climate Resilience in Small Ruminant Farm Systems

Ciliberti, M. G., Albenzio, M., & Sevi, A. (2025) · Animals · doi:10.3390/ani15223240 · Cited by 16

A 3D POINT CLOUD DEEP LEARNING APPROACH USING LIDAR TO IDENTIFY ANCIENT MAYA ARCHAEOLOGICAL SITES

Richards-Rissetto, H., Newton, D., & Zadjali, A. A. (2021) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-viii-m-1-2021-133-2021 · Cited by 12

A Diverse Space Target Dataset With Multidebris and Realistic On-Orbit Environment

Zhang, Z., Deng, C., & Deng, Z. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3203042 · Cited by 12

Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications

Capogrosso, L., Cunico, F., Lora, M., Cristani, M., Fummi, F., & Quaglia, D. (2023) · IEEE International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS) · doi:10.1109/ddecs57882.2023.10139711 · Cited by 12

An Environmental Perception and Navigational Assistance System for Visually Impaired Persons Based on Semantic Stixels and Sound Interaction

Wang, J., Yang, K., Hu, W., & Wang, K. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00332 · Cited by 12

The Impact of a Number of Samples on Unsupervised Feature Extraction, Based on Deep Learning for Detection Defects in Printed Circuit Boards

Volkau, I., Mujeeb, A., Dai, W., Erdt, M., & Sourin, A. (2021) · Future Internet · doi:10.3390/fi14010008 · Cited by 11

Informative Data Mining for One-shot Cross-Domain Semantic Segmentation

Wang, Y., Liang, J., Xiao, J., Mei, S., Yang, Y., & Zhang, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00104 · Cited by 12

Understanding temporal structure for video captioning

Sah, S., Nguyen, T., & Ptucha, R. (2019) · Pattern Analysis and Applications · doi:10.1007/s10044-018-00770-3 · Cited by 12

Unsupervised Object Transfiguration with Attention

Ye, Z., Lyu, F., Li, L., Sun, Y., Fu, Q., & Hu, F. (2019) · Cognitive Computation · doi:10.1007/s12559-019-09633-3 · Cited by 11

Indoor scene understanding via RGB-D image segmentation employing depth-based CNN and CRFs

Li, W., Gu, J., Dong, Y., Dong, Y., & Han, J. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07882-w · Cited by 14

Adversarial erasing attention for fine-grained image classification

Ji, J., Jiang, L., Zhang, T., Zhong, W., & Xiong, H. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08666-3 · Cited by 12

Remotely piloted aircraft imagery for automatic tree counting in forest restoration areas: a case study in the Amazon

Albuquerque, R. W., Costa, M. O., Ferreira, M. E., Carrero, G. C., & Grohmann, C. H. (2020) · Journal of Unmanned Vehicle Systems · doi:10.1139/juvs-2019-0024 · Cited by 13

Smart guiding glasses with descriptive video service and spoken dialogue system for visually impaired

Chen, C.-H., & Shiu, M.-F. (2020) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-taiwan49838.2020.9258014 · Cited by 12

On the performance evaluation of object classification models in low altitude aerial data

Mittal, P., Sharma, A., Singh, R., & Sangaiah, A. K. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04469-5 · Cited by 12

Determining the Rice Seeds Quality Using Convolutional Neural Network

Hidayat, S. S., Rahmawati, D. I., Prabowo, M. C. A., Triyono, L., & Putri, F. T. (2023) · JOIV International Journal on Informatics Visualization · doi:10.30630/joiv.7.2.1175 · Cited by 11

Segmentation and Connectivity Reconstruction of Urban Rivers from Sentinel-2 Multi-Spectral Imagery by the WaterSCNet Deep Learning Model

Dui, Z., Huang, Y., Wang, M., Jin, J., & Gu, Q. (2023) · Remote Sensing · doi:10.3390/rs15194875 · Cited by 11

MRASFusion: A multi-scale residual attention infrared and visible image fusion network based on semantic segmentation guidance

An, R., Liu, G., Qian, Y., Xing, M., & Tang, H. (2024) · Infrared Physics & Technology · doi:10.1016/j.infrared.2024.105343 · Cited by 11

Convolutional neural network for color images classification

Mohammed, N. A., Abed, M. H., & Albu-Salih, A. T. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i3.3730 · Cited by 11

Review on self-driving cars using neural network architectures

Srinivas Rao, P., Gudla, R., Telidevulapalli, V. S., Kota, J. S., & Mandha, G. (2022) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2022.16.2.1240 · Cited by 11

Attention‐enhanced Alexnet for improved radar micro‐Doppler signature classification

Vishwakarma, S., Li, W., Tang, C., Woodbridge, K., Adve, R., & Chetty, K. (2022) · IET Radar Sonar & Navigation · doi:10.1049/rsn2.12369 · Cited by 11

Bird Species Identification using Deep Fuzzy Neural Network

Triveni, G. (2020) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2020.5193 · Cited by 11

Deep Learning in Age-invariant Face Recognition: A Comparative Study

Sajid, M., Ali, N., Ratyal, N. I., Usman, M., Butt, F. M., Riaz, I., Mussadiq, U., Baig, M. J. A., Baig, S., & Salaria, U. A. (2020) · The Computer Journal · doi:10.1093/comjnl/bxaa134 · Cited by 11

A fine-grained detection method for gate-level hardware Trojan based on bidirectional Graph Neural Networks

Cheng, D., Dong, C., He, W., Chen, Z., Liu, X., & Zhang, H. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101822 · Cited by 10

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Bourouis, S., Alharbi, A., & Bouguila, N. (2021) · Journal of Imaging · doi:10.3390/jimaging7010007 · Cited by 10

Lifelong ensemble learning based on multiple representations for few-shot object recognition

Kasaei, H., & Xiong, S. (2024) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104615 · Cited by 11

Simultaneous, vision-based fish instance segmentation, species classification and size regression

Climent-Pérez, P., Galán-Cuenca, A., García-D’Urso, N., Saval-Calvo, M., Azorín-López, J., & Fuster-Guilló, A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1770 · Cited by 10

Fuzzy Least Squares Support Vector Machine With Adaptive Membership for Object Tracking

Zhang, S., Zhang, L., & Hauptmann, A. G. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2952252 · Cited by 10

Selfie Segmentation in Video Using N-Frames Ensemble

Kim, Y., Byun, Y.-C., Krishna, A. V. N., & Balachandran, K. (2021) · IEEE Access · doi:10.1109/access.2021.3133276 · Cited by 10

Optimization of U-shaped pure transformer medical image segmentation network

Dan, Y., Jin, W., Wang, Z., & Sun, C. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1515 · Cited by 9

HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation

Lyu, C., Hu, G., & Wang, D. (2020) · IEEE Access · doi:10.1109/access.2020.2975983 · Cited by 9

Semantic Image Segmentation Using Scant Pixel Annotations

Chakravarthy, A. D., Abeyrathna, D. L. B., Subramaniam, M., Chundi, P., & Gadhamshetty, V. R. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4030029 · Cited by 9

Interaction Control of a Robotic Manipulator With the Surface of Deformable Object

Dometios, A. C., & Tzafestas, C. S. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3226143 · Cited by 9

Efficient Vision Transformer With Token-Selective and Merging Strategies for Autonomous Underwater Vehicles

Jiang, Y. L., Zhang, Y., Wang, Y., Guo, Q., Zhao, M., & Qin, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3422389 · Cited by 8

A survey on hyper basis function neural networks

Zhou, Y., Mu, T., Pang, Z., & Zheng, C. (2019) · Systems Science & Control Engineering · doi:10.1080/21642583.2019.1699474 · Cited by 9

A Method for Identifying and Classifying Resistors and Capacitors Based on YOLO Network

Huang, J., & Lu, Y. (2019) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2019.8868519 · Cited by 8

Classification and Diagnosis of Thyroid Carcinoma Using Reinforcement Residual Network with Visual Attention Mechanisms in Ultrasound Images

Zhang, Y. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1448-5 · Cited by 9

Progressive learning for weakly supervised fine-grained classification

Yan, T., Wang, S., Wang, Z., Li, H., & Luo, Z. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107519 · Cited by 8

Deep truth discovery for pattern-based fact extraction

Ye, C., Wang, H., Lu, W., Gao, J., & Dai, G. (2021) · Information Sciences · doi:10.1016/j.ins.2021.08.084 · Cited by 8

Deep Learning for Fine-Grained Image Recognition: A Comprehensive Study

Yu-hua, K., Chao, G., Hu, X. Y., Tu, Z., & Chu, D. (2022) · Asia Pacific Information Technology Conference (APIT) · doi:10.1145/3512353.3512359 · Cited by 8

Intelligent diagnosis of coronavirus with computed tomography images using a deep learning model

Šarac, M., Mravik, M., Jovanovic, D., Strumberger, I., Zivkovic, M. Z., & Bačanin, N. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.2.021406 · Cited by 11

Information Retrieval and Artificial Intelligence

Boughanem, M., Akermi, I., Pasi, G., & Abdulahhad, K. (2020) · Springer eBooks · doi:10.1007/978-3-030-06170-8_5 · Cited by 8

Deep Expectation-Maximization for Semi-Supervised Lung Cancer ScreeningPreprint

Menon, S., Chapman, D., Nguyen, P., Yesha, Y., Morris, M. A., & Saboury, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01173 · Cited by 8

Fine-Grained Image Analysis with Deep Learning: A SurveyPreprint

Wei, X.-S., Song, Y.-Z., Aodha, O. M., Wu, J., Peng, Y., Tang, J., Yang, J. J., & Belongie, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.06119 · Cited by 8

Deep Learning Based Fine‐Grained Image Classification: Recent Advances, Applications and Future Outlook

Liu, Y., Zhang, H., Che, X., Zhang, W., & Lu, G. (2025) · IET Image Processing · doi:10.1049/ipr2.70243 · Cited by 8

Primary Cognitive Categories Are Determined by Their Invariances

Gärdenfors, P. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.584017 · Cited by 11

SplitStream: Distributed and workload-adaptive video analytics at the edge

Liang, Y., Zhang, S., & Wu, J. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103866 · Cited by 7

Contrastive Self-supervised Representation Learning Using Synthetic Data

She, D., & Xu, K. (2021) · Machine Intelligence Research · doi:10.1007/s11633-021-1297-9 · Cited by 7

Single-Loss Multi-task Learning For Improving Semantic Segmentation Using Super-Resolution

Aakerberg, A., Johansen, A. S., Nasrollahi, K., & Moeslund, T. B. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89131-2_37 · Cited by 7

IU-Module: Intersection and Union Module for Fine-Grained Visual Classification

Zheng, Y., Chang, D., Xie, J., & Ma, Z. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102839 · Cited by 7

Fine-grained recognition of citrus varieties via wavelet channel attention network

Zhang, F., Jin, X., Jiang, J. S., Lin, G., Wang, M., An, S., & Lyu, Q. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113128 · Cited by 6

Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning

Yang, F., Cheng, J., Liu, H., Dong, Y., Jia, Y., & Hou, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709276 · Cited by 9

Fault diagnosis of axial piston pump based on multi-source subdomain adaptation and sensor data fusion

Tang, H., Gong, Y., & Zhou, J. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad42c3 · Cited by 6

Vehicles distance estimation using detection of vanishing point

Bougharriou, S., Hamdaoui, F., & Mtibaa, A. (2019) · Engineering Computations · doi:10.1108/ec-09-2018-0416 · Cited by 6

Enhancing Fine-Grained Classification for Low Resolution Images

Singh, M., Nagpal, S., Mayank Vatsa, A., & Singh, R. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534026 · Cited by 7

Self-supervised Representation Learning for the Object Detection of Marine Radar

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