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

Very Deep Convolutional Networks for Large-Scale Image Recognition

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Fully convolutional networks for semantic segmentation

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

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A Review of Deep Learning-Enhanced Imaging Methods for Microrobots

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DAU-GAN: Unsupervised Object Transfiguration via Deep Attention Unit

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Parsing Objects at a Finer Granularity: A Survey

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BIRD SPECIES IDENTIFICATION USING DEEP LEARNING

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Progressive Multi-granularity Analysis for Video Prediction

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Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

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Nonparametric learning approach based on infinite flexible mixture model and its application to medical data analysis

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Learning Two-level Features for Fine-grained Image Classification

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Un système intelligent pour le classement du café et l'identification des maladies

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A Survey on Fine-Grained Multimodal Large Language Models

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Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset

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Towards Discriminative Class-Aware Domain Alignment via Coding Rate Reduction for Unsupervised Adversarial Domain Adaptation

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Optimization Research and Defect Object Detection of Aeroengine Blade Boss Based on YOLOv4

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Classification of plumage images for identifying bird species

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Food image classification based on the Swin Transformer model

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Two Stage Semantic Segmentation by SEEDS and Fork Net

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Automated design of accurate and robust image classifiers with brain programming

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LE-CapsNet: A Light and Enhanced Capsule Network

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Multiarray nanopatterned (top-down nanolithography) e-nose

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FPGA-Based Optical Surface Inspection of Wind Turbine Rotor Blades Using Quantized Neural Networks

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Toward an Augmented and Explainable Machine Learning Approach for Classification of Defective Nanomaterial Patches

Ieracitano, C., Mammone, N., Paviglianiti, A., & Morabito, F. C. (2021) · Proceedings of the international neural networks society · doi:10.1007/978-3-030-80568-5_21 · Cited by 4

Eyebirds: Enabling the Public to Recognize Water Birds at Hand

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Deep learning-based fine-grained car make/model classification for visual surveillance

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Image Processing Algorithms

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Hexagonal Image Processing for Computer Vision With Hexnet: A Hexagonal Image Processing Data Set and Generator

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Multi-object Detection: A Social Distancing Monitoring System

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Phased progressive learning with coupling-regulation-imbalance loss for imbalanced data classification

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Fusion of GoogleNet and Capsule Neural Network models for improving bone tumor classification

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Exploration of a shape-focused autoencoder for improved ear biometrics

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Supervised Augmentation: Leverage Strong Annotation for Limited Data

Zheng, H., Shang, H., Sun, Z., Fu, X., Yao, J., & Huang, J. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098607 · Cited by 3

Deep learning object detection as an assistance system for complex image labeling tasks

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A Deep Learning-Based Transfer Learning Approach for the Bird Species Classification

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High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble

Kim, Y., Byun, Y.-C., Han, D. S., Dominic, D., & Cyriac, S. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.028632 · Cited by 3

Multi-Class Lane Semantic Segmentation of Expressway Dataset Based on Aerial View

Fan, Y., Wang, Z., Chen, C., Zhang, X., & Lu, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15934-3_17 · Cited by 3

A Review of Recent Advancements in Appearance-based Object Recognition

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Computer vision algorithm for identifying the post-irradiated nuclear fuel in a hotcell

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Semantic Segmentation of Street View Image Based on Fully Convolutional Neural Networks

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A CAD System for Lung Cancer Detection Using Chest X-ray: A Review

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Automated recognition of individual performers from de-identified video sequences

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CNNs, LSTMs, and Attention Networks for Pathology Detection in Medical DataPreprint

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Latest trends in land use and land cover monitoring using deep learning

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Achieving environmental sustainability via an integrated shampoo optimized BiLSTM-Transformer model for enhanced time-series forecasting

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Disentangling Convolutional Neural Network towards an explainable Vehicle Classifier

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Facial Emotion Detection Using CNN-Based Neural Network

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High‐precision real‐time urine crystallization recognition based on dilated bilinear space pyramid ConvNext

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Visual Measurement of Grinding Surface Roughness Based on GE-MobileNet

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High Performance Visual Inspection Service Architecture - Squeezing the Most Out of Commodity Servers

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Convergence of multiple deep neural networks for classification with fewer labeled data

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Incorporating Domain Knowledge in Machine Learning for Satellite Image Processing

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Deep Neural Network with Extracted Features for Social Group Detection

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A New Infrared and Visible Image Fusion Method Based on Generative Adversarial Networks and Attention Mechanism

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Research on the air quality prediction model of Wuhai mining area based on deep learning

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A Review of Remote Sensing Applications on Very High-Resolution Imagery Using Deep Learning-Based Semantic Segmentation Techniques

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Application Optimization of Fine-grained Vehicle Classification based on Backbone Network

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Research Progress of Fine-grained Visual Classification: Basic Framework, Challenges, and Future Development

Ma, C., & Pu, Y. (2021) · International Academic Exchange Conference on Science and Technology Innovation (IAECST) · doi:10.1109/iaecst54258.2021.9695701 · Cited by 2

A specific fine-grained identification model for plasma-treated rice growth using multiscale shortcut convolutional neural network

Chen, W., Wang, Y., Tang, X., Yan, P., Liu, X., Lin, L., Shi, G., Robert, É., & Huang, F. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023448 · Cited by 2

Performance Evaluation of Quantum and Classical Machine-Learning Models for Breast Cancer Diagnosis

Shah, M. R., Sarraf, J., & Pattnaik, P. K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-0573-3_33 · Cited by 1

A Comparative Analysis on Predicting Emotions from Images And Videos Based on Colors Using Machine Learning

Pydala, B., Samatha, P., Subramanyam, P., Prabhas, T. L. V., Panchajanya, S., & Jyothsna, V. (2024) · Advances in computer science research · doi:10.2991/978-94-6463-471-6_26 · Cited by 1

Forecasting Future Corporate Value Including Information on Firm Environmental Activities: Using Machine Learning Techniques

Park, J., Kim, H., & Na, H. J. (2025) · IEEE Access · doi:10.1109/access.2025.3595456 · Cited by 1

Head-Body Guided Deep Learning Framework for Dog Breed Recognition

Khan, N., Lee, M. Y., & Min, J. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.069058 · Cited by 1

A Light‐Weight and Controllable Attention Mechanism for Interleaved Signal Recognition with High Similarity

Meng, H., Lei, Y., Teng, F., Wang, J., Feng, H., Sun, Y., & Yu, H. (2025) · IET Communications · doi:10.1049/cmu2.70086 · Cited by 1

A survey on deep learning techniques for image and video feature aggregation

Islam, M. M., Rao, S. P. R., & He, S. (2026) · CAAI Artificial Intelligence Research · doi:10.26599/air.2025.9150054 · Cited by 1

SCS-SupCon: Sigmoid-based common and style supervised contrastive learning with adaptive decision boundaries

Wang, B., Dornaika, F., & Charafeddine, J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131308 · Cited by 1

A proxy-enhanced and margin-adaptive contrastive learning method for skin lesion diagnosis

Xin, D., Yao, P., Zhang, C., Shen, S., Wang, Z., Han, Z., Shi, Y., Shao, P., Sun, M., & Xu, R. X. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109907 · Cited by 1

The U-Net Architecture for MRI Brain Tumor Segmentation, Detection, and Classification: A Survey

Benchari, M., Totaro, M. W., Bayoumi, M., & Mokhtari, S. (2026) · Biomedical Materials & Devices · doi:10.1007/s44174-026-00660-x · Cited by 1

Classify fine-grained images in hyperbolic space

Yu, Z., Lai, H., & Liu, Y. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113348 · Cited by 1

Semantic Segmentation using Generative Adversarial Network

Chen, W., Zhang, T., & Zhao, X. (2021) · Chinese Control Conference (CCC) · doi:10.23919/ccc52363.2021.9549798 · Cited by 1

WCANet: Wavelet Channel Attention Network for Citrus Variety Identification

Zhang, F., Jin, X., Jiang, J. S., An, S., & Lyu, Q. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222588 · Cited by 1

Meta-HRNet: A High Resolution Network for Coarse-to-Fine Few-Shot Classification

Li, Z., & Mu, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43415-0_28 · Cited by 1

A Lightweight Multimodal Footprint Recognition Network Based on Progressive Multi-Granularity Feature Fusion

Cao, R., Li, L., Zhang, Y., Wu, J., & Zhao, X. (2023) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001423570124 · Cited by 1

The Improved Inception Networks based on Attention Mechanism

Yu, Y., Sun, Y., & Cao, Y. (2023) · doi:10.1145/3635175.3635177 · Cited by 1

The Research on Traffic Flow Statistics Based on YOLOv5 and Attention Mechanism

Yu, Y., Sun, Y., & Cao, Y. (2023) · doi:10.1145/3635175.3635187 · Cited by 1

A Power Monitoring Network Attack Detection Mechanism based on Graph Convolutional Neural Network

Lv, Z., Wang, M., Chang, H., Zhang, Z., Chen, C., & Cai, J. (2023) · International Conference on Networks, Communications and Intelligent Computing (NCIC) · doi:10.1109/ncic61838.2023.00025 · Cited by 1

Standardizing canine breed data in veterinary records is challenging, but computer vision offers an alternative perspective on breed assignment

Perez, G., He, Y., Lyu, Z., Chen, Y., Howe, N. R., & Rando, H. M. (2025) · American Journal of Veterinary Research · doi:10.2460/ajvr.24.10.0315 · Cited by 2

An Interpretable Galaxy Morphology Classification Approach Using Modified SqueezeNet and Local Interpretable Model-agnostic Explanation

Goh, K. M., Lim, D. H. Y., Sham, Z. D., & Prakash, K. B. (2025) · Research in Astronomy and Astrophysics · doi:10.1088/1674-4527/adce8f · Cited by 1

Vegetation density estimation in the wild

Mihail, R. P., Cook, W. I., Griffin, B. M., Uyeno, T. A., & Anderson, C. D. (2018) · doi:10.1145/3190645.3190690 · Cited by 1

Computational and Performance Aspects of Face Recognition Method (HOPFALRB)

Jain, N., Singh, H., Sharma, V., & Chaturvedi, R. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-6014-9_76 · Cited by 1

Recent Applications of Deep Learning Algorithms in Medical Image Analysis

Sarraf, A., Jalali, A., & Ghaffari, J. (2020) · American Scientific Research Journal for Engineering, Technology, and Sciences · Cited by 1

Multi-branch Recurrent Attention Convolutional Neural Network with Evidence Theory for Fine-Grained Image Classification

Xu, Z., Zhang, B., Fu, H., Yue, X., & Lv, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88601-1_18 · Cited by 1

Impact of Sample Quality to Deep Learning Classification Model of Multiple Crop Types on UAV Remotely Sensed Images

Li, X., Cui, Y. D., Zhou, X., & Liu, S. (2022) · Advances in transdisciplinary engineering · doi:10.3233/atde220048 · Cited by 1

Fusion of handcrafted and deep transfer learning features to improve performance of breast lesion classification

Jones, M. A., Pham, H., Gai, T., & Zheng, B. (2022) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2611607 · Cited by 1

Deep Learning in Cybersecurity

İmamverdiyev, Y., & Abdullayeva, F. J. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-7705-9.ch095 · Cited by 1

Semantic Generation in Vision Data Link

Hui, G., Chen, C., Li, F., Hu, J., & Guo, P. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-6052-9_58 · Cited by 1

Context-Dependent Deep Learning for Affective Computing

Suresh, V. (2022) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw57231.2022.10085993 · Cited by 1

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

Suresh, V., & Ong, D. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05427 · Cited by 20

Aplicação de aeronaves remotamente pilotadas e fotogrametria para avaliação e monitoramento de áreas em processo de restauração florestal

de Albuquerque, R. W. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.106.2022.tde-27072022-144958 · Cited by 1

Sparse Feature Convolutional Neural Network with Cluster Max Extraction for Fast Object Classification

Kim, S. H., Pae, D.-S., Kang, T., Kim, D.-W., & Lim, M. T. (2018) · doi:10.5370/jeet.2018.13.6.2468

Risk-Aware Reasoning for Autonomous VehiclesPreprint

Khonji, M., Dias, J. M. M., & Seneviratne, L. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.02461

Adversarial detection games in network security applications with imperfect and incomplete information

Moral, J. P. (2020) · doi:10.20868/upm.thesis.57986

Convolutional Attention Network with Maximizing Mutual Information for Fine-Grained Image Classification

Wang, F., Zhou, H., Li, S., Lei, J. W., & Zhang, J. (2020) · Symmetry · doi:10.3390/sym12091511

Two-Stage Classification Model for Feather Images Identification

Belko, A., Dobratulin, K., & Andrey, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68821-9_17

Exploring Category-Shared and Category-Specific Features for Fine-Grained Image Classification

Wang, H., Chang, D., Liu, W., Xiao, B., Ma, Z., Guo, J. H., & Chang, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88004-0_15

Peer Review #2 of "A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images (v0.1)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.806v0.1/reviews/2

Peer Review #1 of "A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images (v0.1)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.806v0.1/reviews/1

Peer Review #1 of "A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images (v0.2)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.806v0.2/reviews/1

Peer Review #2 of "A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images (v0.2)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.806v0.2/reviews/2

An Intelligent Visual Analysis Scheme for Automatic Disassembly Processes in the Recycling Industry

Yildiz, E. (2021) · doi:10.53846/goediss-8573

Coordinate Transformer Network for Prediction of Pseudomonas Aeruginosa’s Drug Resistance

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Research on power monitoring network attack detection technology based on deep learning

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Fine-grained classification of natural scene grape images based on deep convolution networks

Jia, M., Hu, T., Li, J., Jia, S., & Luo, J. T. (2022) · International Conference on Digital Image Processing · doi:10.1117/12.2644241

A Survey of Fine-Grained Image Classification Based on Deep Learning

Guo, C., Cui, H., Yu, K., & Xin-ping, M. O. (2019) · DEStech Transactions on Computer Science and Engineering · doi:10.12783/dtcse/ica2019/30636 · Cited by 1

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

Deep Learning Based Fine Grained Image Classification

Vaidya, P., & Kamalapur, S. M. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i7s.7532

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

Fine-grained image classification using deep learning

Sun, D. (2021) · DR-NTU (Nanyang Technological University)