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

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565

Attention gated networks: learning to leverage salient regions in medical images

Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014

A Survey of Deep Learning-Based Object Detection

Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019) · IEEE Access · doi:10.1109/access.2019.2939201 · Cited by 1,454

Convolutional neural network: a review of models, methodologies and applications to object detection

Dhillon, A., & Verma, G. K. (2020) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00203-0 · Cited by 1,119

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Wang, R., Chong, Y., et al. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3065361 · Cited by 978

BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition

Zhou, B., Cui, Q., Wei, X.-S., & Chen, Z.-M. (2020) · 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/cvpr42600.2020.00974 · Cited by 972

A Brief Survey on Semantic Segmentation with Deep Learning

Hao, S., Zhou, Y., & Guo, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.118 · Cited by 638

Fine-Grained Image Analysis With Deep Learning: A Survey

Wei, X.-S., Song, Y.-Z., Aodha, O. M., Wu, J., Peng, Y., Tang, J., Yang, J., & Belongie, S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126648 · Cited by 526

Understanding Deep Learning Techniques for Image Segmentation

Ghosh, S., Das, N., Das, I., & Maulik, U. (2019) · ACM Computing Surveys · doi:10.1145/3329784 · Cited by 517

Object-Part Attention Model for Fine-Grained Image Classification

Peng, Y., He, X., & Zhao, J. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2774041 · Cited by 399

Electronic Nose and Its Applications: A Survey

Ulucan, D., Ulucan, O., & Türkan, M. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1212-9 · Cited by 377

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Ni, J., Chen, Y., Chen, Y., Zhu, J., Ali, D., & Cao, W. (2020) · Applied Sciences · doi:10.3390/app10082749 · Cited by 184

Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation

Ghali, R., Akhloufi, M. A., & Mseddi, W. S. (2022) · Sensors · doi:10.3390/s22051977 · Cited by 176

A review on lung boundary detection in chest X-rays

Candemir, S., & Antani, S. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-01917-1 · Cited by 170

ResDO-UNet: A deep residual network for accurate retinal vessel segmentation from fundus images

Liu, Y., Shen, J., Yang, L., Bian, G., & Yu, H. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104087 · Cited by 168

A survey of semi- and weakly supervised semantic segmentation of images

Zhang, M., Zhou, Y. Z., Zhao, J., Man, Y., Liu, B., & Yao, R. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09792-7 · Cited by 148

Deep Learning for Retail Product Recognition: Challenges and Techniques

Wei, Y.-C., Tran, S. N., Xu, S., Kang, B. H., & Springer, M. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8875910 · Cited by 153

An Overview of Contour Detection Approaches

Gong, X., Su, H., Xu, D., Zhang, Z.-T., Shen, F., & Yang, H.-B. (2018) · Machine Intelligence Research · doi:10.1007/s11633-018-1117-z · Cited by 143

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134

Forest fire surveillance systems: A review of deep learning methods

Saleh, A., Zulkifley, M. A., Harun, H. H., Gaudreault, F., Davison, I., & Spraggon, M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e23127 · Cited by 143

Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning

Bianchi, F. M., Espeseth, M. M., & Borch, N. T. (2020) · Remote Sensing · doi:10.3390/rs12142260 · Cited by 120

Semantics for Robotic Mapping, Perception and Interaction: A Survey

Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I. D., Gould, S. J., et al. (2020) · Foundations and Trends in Robotics · doi:10.1561/2300000059 · Cited by 122

Visual SLAM Integration With Semantic Segmentation and Deep Learning: A Review

Pu, H., Luo, J., Wang, G., Huang, T., Liu, H., & Luo, J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3306371 · Cited by 122

A Vision-Based Pipeline for Vehicle Counting, Speed Estimation, and Classification

Liu, C., Huynh, D. Q., Sun, Y., Reynolds, M. A., & Atkinson, S. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3004066 · Cited by 101

Object Detection with Transformers: A Review

Shehzadi, T., Hashmi, K. A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2025) · Sensors · doi:10.3390/s25196025 · Cited by 93

BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos

Bondi, E., Jain, R., Aggrawal, P., Anand, S., Hannaford, R., Kapoor, A., Piavis, J., Shah, S., Joppa, L., Dilkina, B., et al. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093284 · Cited by 103

Single-View 3D reconstruction: A Survey of deep learning methods

Fahim, G., Amin, K., & Zarif, S. (2021) · Computers & Graphics · doi:10.1016/j.cag.2020.12.004 · Cited by 102

Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder

Attia, M., Hossny, M., Nahavandi, S., & Asadi, H. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8123151 · Cited by 85

Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification

Suresh, V., & Ong, D. C. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.359 · Cited by 82

Bird Image Retrieval and Recognition Using a Deep Learning Platform

Huang, Y., & Basanta, H. (2019) · IEEE Access · doi:10.1109/access.2019.2918274 · Cited by 78

Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Zhang, T., Su, J., Liu, C., & Chen, W. (2018) · Machine Intelligence Research · doi:10.1007/s11633-018-1143-x · Cited by 76

Action Recognition With Spatial-Temporal Discriminative Filter Banks

Martínez, B., Modolo, D., Xiong, Y., & Tighe, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00558 · Cited by 75

Deep Learning for Fine-Grained Image Analysis: A SurveyPreprint

Wei, X.-S., Wu, J., & Cui, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03069 · Cited by 98

Biometric identification of sheep via a machine-vision system

Hitelman, A., Edan, Y., Godo, A., Berenstein, R., Lepar, J., & Halachmi, I. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106713 · Cited by 76

Improving the Performance of Deep Neural Networks Using Two Proposed Activation Functions

Alkhouly, A. A., Mohammed, A., & Hefny, H. A. (2021) · IEEE Access · doi:10.1109/access.2021.3085855 · Cited by 80

3D image recognition using new set of fractional-order Legendre moments and deep neural networks

Ogri, O. E., Karmouni, H., Sayyouri, M., & Qjidaa, H. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116410 · Cited by 71

An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection

Yang, L., Song, S., Fan, J., Huo, B., Li, E., & Liu, Y. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3127645 · Cited by 73

Deep learning serves traffic safety analysis: A forward‐looking review

Razi, A., Chen, X., Li, H., Wang, H., Russo, B. J., Chen, Y., & Yu, H. (2022) · IET Intelligent Transport Systems · doi:10.1049/itr2.12257 · Cited by 83

Localization and Classification of Rice-grain Images Using Region Proposals-based Convolutional Neural Network

Aukkapinyo, K., Sawangwong, S., Pooyoi, P., & Kusakunniran, W. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1207-6 · Cited by 70

A review of distributed solar forecasting with remote sensing and deep learning

Chu, Y., Wang, Y., Yang, D., Chen, S., & Li, M. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114391 · Cited by 87

Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey

Liu, S., Guo, B., Fang, C., Wang, Z., Luo, S., Zhou, Z., & Yu, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3319952 · Cited by 74

Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey

Altini, N., Prencipe, B., Cascarano, G. D., Brunetti, A., Brunetti, G., Triggiani, V., Carnimeo, L., Marino, F., Guerriero, A., Villani, L., et al. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.08.157 · Cited by 66

A Survey of Scene Understanding by Event Reasoning in Autonomous Driving

Xue, J., Fang, J., & Zhang, P. (2018) · Machine Intelligence Research · doi:10.1007/s11633-018-1126-y · Cited by 63

Pointview-GCN: 3D Shape Classification With Multi-View Point Clouds

Mohammadi, S. S., Wang, Y., & Bue, A. D. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506426 · Cited by 62

Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold

Wu, Z., Gao, Y., Li, L., Xue, J., & Li, Y. (2018) · Connection Science · doi:10.1080/09540091.2018.1510902 · Cited by 61

Accurate Gastric Cancer Segmentation in Digital Pathology Images Using Deformable Convolution and Multi-Scale Embedding Networks

Sun, M., Zhang, G., Dang, H., Qi, X., Zhou, X., & Chang, Q. H. (2019) · IEEE Access · doi:10.1109/access.2019.2918800 · Cited by 61

Deep learning-based object classification through multimode fiber via a CNN-architecture SpeckleNet

Wang, P., & Di, J. (2018) · Applied Optics · doi:10.1364/ao.57.008258 · Cited by 59

Fine-Grained Recognition With Learnable Semantic Data Augmentation

Pu, Y., Han, Y., Wang, Y., Feng, J., Deng, C., & Huang, G. M. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3364500 · Cited by 66

Gated Recurrent Multiattention Network for VHR Remote Sensing Image Classification

Li, B., Guo, Y., Yang, J., Wang, L., Wang, Y., & An, W. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3093914 · Cited by 59

Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation

Meletis, P., & Dubbelman, G. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500398 · Cited by 58

Fine-grained object detection method using attention mechanism and its application in coal–gangue detection

Lv, Z., Wang, W., Xu, Z., Zhang, K., Fan, Y., & Song, Y. S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107891 · Cited by 53

Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification

Yu, D., Guo, H., Xu, Q., Lu, J., Zhao, C., & Lin, Y. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3030257 · Cited by 52

Disease prediction based retinal segmentation using bi-directional ConvLSTMU-Net

Rani, B., Vallabhuni, R. R., Srinivasan, V. P., Thenmalar, S., & Kanimozhi, R. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03017-y · Cited by 51

A Comprehensive Review of Group Activity Recognition in Videos

Wu, L., Wang, Q., Jian, M., Qiao, Y., & Zhao, B.-X. (2021) · Machine Intelligence Research · doi:10.1007/s11633-020-1258-8 · Cited by 51

Diagnostic Accuracy of Artificial Intelligence in Glaucoma Screening and Clinical Practice

Chaurasia, A. K., Greatbatch, C. J., & Hewitt, A. W. (2022) · Journal of Glaucoma · doi:10.1097/ijg.0000000000002015 · Cited by 48

Deep Listwise Triplet Hashing for Fine-Grained Image Retrieval

Liang, Y., Pan, Y., Lai, H., Liu, W., & Yin, J. P. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3137653 · Cited by 47

Detection of unwanted traffic congestion based on existing surveillance system using in freeway via a CNN-architecture trafficnet

Wang, P., Li, L., Jin, Y., & Wang, G. (2018) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2018.8397881 · Cited by 45

Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery

Rodríguez, P., Gonfaus, J. M., Cucurull, G., Roca, F. X., & Gonzàlez, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_22 · Cited by 45

A Triple-Stream Network With Cross-Stage Feature Fusion for High-Resolution Image Change Detection

Zhao, Y., Chen, P., Chen, Z., Bai, Y., Zhao, Z., & Yang, X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3233849 · Cited by 45

Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images

Feng, Y., Diao, W., Sun, X., Yan, M., & Gao, X. (2019) · Remote Sensing · doi:10.3390/rs11161901 · Cited by 42

Adaptive Mutual Learning for Unsupervised Domain Adaptation

Zhou, L., Xiao, S., Ye, M., Zhu, X., & Li, S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3265853 · Cited by 40

Ship Classification in SAR Images With Geometric Transfer Metric Learning

Xu, Y., & Lang, H. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3026387 · Cited by 40

3D analysis of the whole subcutaneous adipose tissue reveals a complex spatial network of interconnected lobules with heterogeneous browning ability

Dichamp, J., Barreau, C., Guissard, C., Carrière, A., Martinez, Y., Descombes, X., Pénicaud, L., Rouquette, J., Casteilla, L., Plouraboué, F., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-43130-9 · Cited by 36

Physiognomy: Personality traits prediction by learning

Zhang, T., Qin, R., Dong, Q., Gao, W., Xu, H., & Hu, Z. (2017) · Machine Intelligence Research · doi:10.1007/s11633-017-1085-8 · Cited by 40

Fine grained food image recognition based on swin transformer

Xiao, Z., Diao, G., & Deng, Z. (2024) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2024.112134 · Cited by 35

Semi-supervised Ladder Networks for Speech Emotion Recognition

Tao, J.-H., Huang, J., Li, Y., Lian, Z., & Niu, M. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1175-x · Cited by 33

Distribution Shift Metric Learning for Fine-Grained Ship Classification in SAR Images

Xu, Y., & Lang, H. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2991784 · Cited by 33

CGAN-plankton: Towards large-scale imbalanced class generation and fine-grained classification

Wang, C., Yu, Z., Zheng, H., Wang, N., & Zheng, B. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296402 · Cited by 33

Addressing significant challenges for animal detection in camera trap images: a novel deep learning-based approach

Mulero‐Pázmány, M., Hurtado, S., Barba-González, C., Antequera‐Gómez, M. L., Díaz‐Ruiz, F., Real, R., Navas‐Delgado, I., & Aldana-Montes, J. F. (2025) · Scientific Reports · doi:10.1038/s41598-025-90249-z · Cited by 41

Focal Dice Loss-Based V-Net for Liver Segments Classification

Prencipe, B., Altini, N., Cascarano, G. D., Brunetti, A., Guerriero, A., & Bevilacqua, V. (2022) · Applied Sciences · doi:10.3390/app12073247 · Cited by 34

Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning

Borwarnginn, P., Kusakunniran, W., Karnjanapreechakorn, S., & Thongkanchorn, K. (2020) · Machine Intelligence Research · doi:10.1007/s11633-020-1261-0 · Cited by 34

Deep LF-Net: Semantic lung segmentation from Indian chest radiographs including severely unhealthy images

Singh, A., Lall, B., Panigrahi, B. K., Agrawal, A., Agrawal, A., Thangakunam, B., & Christopher, D. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102666 · Cited by 31

Machine learning models based on hyperspectral imaging for pre-harvest tomato fruit quality monitoring

Fass, E., Shlomi, E., Ziv, C., Glickman, O., & Helman, D. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109788 · Cited by 30

Deep Learning in Cybersecurity

İmamverdiyev, Y., & Abdullayeva, F. J. (2020) · International Journal of Cyber Warfare and Terrorism · doi:10.4018/ijcwt.2020040105 · Cited by 29

Dates Fruit Recognition: From Classical Fusion to Deep Learning

Alresheedi, K. M., Aladhadh, S., Khan, R. U., & Qamar, A. M. (2021) · Computer Systems Science and Engineering · doi:10.32604/csse.2022.017931 · Cited by 34

Reinforced knowledge distillation: Multi-class imbalanced classifier based on policy gradient reinforcement learning

Fan, S., Zhang, X., & Song, Z. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.040 · Cited by 29

Visually interpretable deep network for diagnosis of breast masses on mammograms

Kim, S. T., Lee, J.-H., Lee, H., & Ro, Y. M. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aaef0a · Cited by 29

Detecting helicobacter pylori in whole slide images via weakly supervised multi-task learning

Yang, Y., Yang, Y., Yuan, Y., Zheng, J., & Zheng, Z. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09185-x · Cited by 29

Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features

Jones, M. A., Faiz, R., Qiu, Y., & Zheng, B. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac5297 · Cited by 34

Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence

Albuquerque, R. W., Vieira, D. L. M., Ferreira, M. E., Soares, L. P., Olsen, S. I., de Araujo, L. S., Vicente, L. E., Tymus, J. R. C., Balieiro, C. P., Matsumoto, M. H., et al. (2022) · Remote Sensing · doi:10.3390/rs14040830 · Cited by 33

A survey on deep learning-based precise boundary recovery of semantic segmentation for images and point clouds

Zhang, R., Li, G., Wunderlich, T. A., & Wang, L. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102411 · Cited by 27

New deep learning method for efficient extraction of small water from remote sensing images

Luo, Y., Feng, A., Li, H., Li, D., Wu, X., Liao, J., Zhang, C., Zheng, X., & Pu, H. (2022) · PLoS ONE · doi:10.1371/journal.pone.0272317 · Cited by 26

Medical Ultrasound Image Quality Assessment for Autonomous Robotic Screening

Song, Y., Zhong, Z., Zhao, B., Zhang, P., Wang, Q., Wang, Z., Yao, L., Lv, F., & Hu, Y. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3170209 · Cited by 28

Learning Multi-Subset of Classes for Fine-Grained Food Recognition

Ródenas, J., Nagarajan, B., Bolaños, M., & Radeva, P. I. (2022) · doi:10.1145/3552484.3555754 · Cited by 24

Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models

Kim, Y., Byun, Y.-C., & Krishna, A. V. N. (2021) · Entropy · doi:10.3390/e23020197 · Cited by 23

MEP-3M: A large-scale multi-modal E-commerce product dataset

Liu, F., Chen, D., Du, X., Gao, R., & Xu, F. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109519 · Cited by 23

A Robust Face Recognition Method Combining LBP with Multi-mirror Symmetry for Images with Various Face Interferences

Tong, S.-G., Huang, Y., & Tong, Z. (2018) · Machine Intelligence Research · doi:10.1007/s11633-018-1153-8 · Cited by 23

Deep reinforcement learning in edge networks: Challenges and future directions

Hazra, A., Tummala, V. M. R., Mazumdar, N., Sah, D. K., & Adhikari, M. (2024) · Physical Communication · doi:10.1016/j.phycom.2024.102460 · Cited by 24

SILIC: A cross database framework for automatically extracting robust biodiversity information from soundscape recordings based on object detection and a tiny training dataset

Wu, S., Chang, H., Lin, R., & Tuanmu, M. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101534 · Cited by 21

GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning

Li, W., Wang, S., Arundel, S. T., & Hsu, C.-Y. (2022) · GeoInformatica · doi:10.1007/s10707-022-00476-z · Cited by 22

Few-shot fine-grained fish species classification via sandwich attention CovaMNet

Zhai, J., Han, L., Xiao, Y., Yan, M., Wang, Y., & Wang, X. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1149186 · Cited by 22

An improved GrabCut on multiscale features

He, K., Wang, D., Tong, M., & Zhu, Z. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107292 · Cited by 22

Computing Valid P-Values for Image Segmentation by Selective Inference

Tanizaki, K., Hashimoto, N., Inatsu, Y., Hontani, H., & Takeuchi, I. (2020) · 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/cvpr42600.2020.00957 · Cited by 22

Towards Multi-source Adaptive Semantic Segmentation

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