Hypercolumns for object segmentation and fine-grained localization

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

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

Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex

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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898

Backpropagation Applied to Handwritten Zip Code Recognition

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Object Detection with Discriminatively Trained Part-Based Models

Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853

Two-Stream Convolutional Networks for Action Recognition in Videos

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756

Learning Dynamic Hierarchical Models for Anytime Scene Labeling

Liu, B., & He, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_39 · Cited by 15

Aircraft Detection using Deep Convolutional Neural Network in Small Unmanned Aircraft Systems

Hwang, S., Lee, J.-H., Shin, H., Cho, S., & Shim, D. H. (2018) · AIAA Information Systems-AIAA Infotech @ Aerospace · doi:10.2514/6.2018-2137 · Cited by 15

Unsupervised Multiple-Change Detection in VHR Optical Images Using Deep Features

Saha, S., Bovolo, F., & Brurzone, L. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8519440 · Cited by 15

Adaptive Multi-Path Aggregation for Human DensePose Estimation in the Wild

Guo, Y., Gao, L., Song, J., Wang, P., Xie, W., & Shen, H. T. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350856 · Cited by 15

Small Object Detection Algorithm Based on Feature Pyramid‐Enhanced Fusion SSD

Li, H., Lin, K., Bai, J., Li, A., & Yu, J. (2019) · Complexity · doi:10.1155/2019/7297960 · Cited by 15

Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network

Zhang, B., Grant, J., Bruckman, L. S., Wodo, O., & Rai, R. (2019) · Scientific Reports · doi:10.1038/s41598-019-52550-6 · Cited by 15

A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei

Liu, Y., Nacewicz, B. M., Zhao, G., Adluru, N., Kirk, G. R., Ferrazzano, P. A., Styner, M. A., & Alexander, A. L. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00260 · Cited by 25

RelationNet2: Deep Comparison Network for Few-Shot Learning

Zhang, X., Qiang, Y., Sung, F., Yang, Y., & Hospedales, T. M. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206909 · Cited by 15

Efficient Attention Pyramid Network for Semantic Segmentation

Yang, Q., Ku, T., & Hu, K. (2021) · IEEE Access · doi:10.1109/access.2021.3053316 · Cited by 15

Robust semi-supervised classification based on data augmented online ELMs with deep features

Hu, X., Zeng, Y., Xu, X., Zhou, S., & Liu, L. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107307 · Cited by 15

Aggregating diverse deep attention networks for large-scale plant species identification

Zhang, H., Kuang, Z., Peng, X., He, G., Peng, J., & Jianping Fan, B. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.077 · Cited by 15

Small-sample learning with salient-region detection and center neighbor loss for insect recognition in real-world complex scenarios

Yang, Z., Yang, X., Li, M., & Li, W. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106122 · Cited by 16

Capturing Small Objects and Edges Information for Cross-Sensor and Cross-Region Land Cover Semantic Segmentation in Arid Areas

Yuan, P., Zhao, Q., Zheng, Y., Wang, X., & Hu, B. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3232409 · Cited by 15

FA-YOLOv9: Improved YOLOv9 Based on Feature Attention Block

Nguyen, T.-Q., Tran, H.-L., Tran, T.-K., Phan-Nguyen, H.-P., & Nguyen, T.-H. (2024) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr63514.2024.10661057 · Cited by 14

Object Boundary Guided Semantic Segmentation

Huang, Q., Xia, C., Zheng, W., Song, Y., Xu, H., & Kuo, C. ‐. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54181-5_13 · Cited by 14

PaI‐Net: A modified U‐Net of reducing semantic gap for surgical instrument segmentation

Wang, X., Wang, L., Zhong, X., Bai, C., Huang, X., Zhao, R., & Xia, M. (2021) · IET Image Processing · doi:10.1049/ipr2.12283 · Cited by 17

Dynamic evolution of microstructure morphology in thin-sample solidification: Deep learning assisted synchrotron X-ray radiography

Wang, N., Tang, Y., Wu, Y., Zhang, Y., Dai, Y., Zhang, J., Zhang, R., Xu, Y. X., & Sun, B. (2021) · Materials Characterization · doi:10.1016/j.matchar.2021.111451 · Cited by 14

Contrastive and Selective Hidden Embeddings for Medical Image Segmentation

Liu, Z., Li, Z., Hu, Z., Xia, Q., Xiong, R., Zhang, S., & Jiang, T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3186677 · Cited by 14

A Multi-Scale Edge Constraint Network for the Fine Extraction of Buildings from Remote Sensing Images

Wang, Z., Zhou, Y., Wang, F., Wang, S., Qin, G., Zou, W., & Zhu, J. (2023) · Remote Sensing · doi:10.3390/rs15040927 · Cited by 15

Deep Shape from a Low Number of Silhouettes

Di, X., Dahyot, R., & Prasad, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_21 · Cited by 14

Twin Deep Convolutional Neural Network for Example-Based Image Colorization

Varga, D., & Szirányi, T. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-64689-3_15 · Cited by 14

S3-Net: A Fast Scene Understanding Network by Single-Shot Segmentation for Autonomous Driving

Cheng, Y., Yang, Y., Chen, H., Wong, N., & Yu, H. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3470660 · Cited by 14

Objects as Context for Detecting Their Semantic Parts

González-García, A., Modolo, D., & Ferrari, V. (2018) · 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/cvpr.2018.00722 · Cited by 14

Texture segmentation with Fully Convolutional NetworksPreprint

Andrearczyk, V., & Whelan, P. F. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05230 · Cited by 24

DeepProposal: Hunting Objects by Cascading Deep Convolutional LayersPreprint

Ghodrati, A., Diba, A., Pedersoli, M., Tuytelaars, T., & Gool, L. V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.04445 · Cited by 14

2D Object Detection: A Survey

Malagoli, E., & Persio, L. D. (2025) · Mathematics · doi:10.3390/math13060893 · Cited by 19

On a Sparse Shortcut Topology of Artificial Neural Networks

Fan, F., Wang, D., Guo, H., Zhu, Q., Yan, P., Wang, G., & Yu, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3128132 · Cited by 22

Self-supervised Few-Shot Learning for Semantic Segmentation: An Annotation-Free Approach

Karimijafarbigloo, S., Azad, R., & Merhof, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46005-0_14 · Cited by 11

Edge gradient feature and long distance dependency for image semantic segmentation

Zhou, H., Han, A., Yang, H., & Zhang, J. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5035 · Cited by 13

Multi-level feature fusion model-based real-time person re-identification for forensics

Wang, S., Xu, X., Liu, L., & Tian, J. (2019) · Journal of Real-Time Image Processing · doi:10.1007/s11554-019-00908-4 · Cited by 13

Attentive boundary aware network for multi-scale skin lesion segmentation with adversarial training

Wei, Z., Shi, F., Song, H., Ji, W., & Han, G. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09334-2 · Cited by 13

Deep Feature Representations for Variable-Sized Regions of Interest in Breast Histopathology

Mercan, C., Aygüneş, B., Aksoy, S., Mercan, E., Shapiro, L. G., Weaver, D. L., & Elmore, J. G. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3036734 · Cited by 13

Attention Fusion for Audio-Visual Person Verification Using Multi-Scale Features

Hörmann, S., Moiz, A., Knoche, M., & Rigoll, G. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00074 · Cited by 13

Analytical review and study on object detection techniques in the image

Sriram, K., & Havaldar, R. H. (2021) · Advances in Complex Systems · doi:10.1142/s1793962321500318 · Cited by 13

Head Detection Based on DR Feature Extraction Network and Mixed Dilated Convolution Module

Liu, J., Zhang, Y., Xie, J., Yan, W., Wang, Z., & Niu, M. (2021) · Electronics · doi:10.3390/electronics10131565 · Cited by 13

Dynamic Parallel Pyramid Networks for Scene Recognition

Liu, K., & Moon, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3129227 · Cited by 13

Automatic White Blood Cell Classification Based on Whole-Slide Images with a Deeply Aggregated Neural Network

Zhai, Q., Fan, B., Zhang, B., Li, J., & Liu, J. (2022) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-022-00683-x · Cited by 13

RATNet: A deep learning model for Bengali handwritten characters recognition

Islam, M. S., Rahman, M. M., Rahman, M. H., Rivolta, M. W., & Aktaruzzaman, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12070-4 · Cited by 15

Depth measurement based on a convolutional neural network and structured light

Jia, T., Liu, Y., Yuan, X., Li, W., Chen, D., & Zhang, Y. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac329d · Cited by 13

Improved 3D Semantic Segmentation Model Based on RGB Image and LiDAR Point Cloud Fusion for Automantic Driving

Du, J., Huang, X., Xing, M., & Zhang, T. (2023) · International Journal of Automotive Technology · doi:10.1007/s12239-023-0065-y · Cited by 13

PAR2Net: End-to-End Panoramic Image Reflection Removal

Hong, Y., Zheng, Q., Zhao, L., Jiang, X., Kot, A. C., & Shi, B. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3286429 · Cited by 13

Improving OCT Image Segmentation of Retinal Layers by Utilizing a Machine Learning Based Multistage System of Stacked Multiscale Encoders and Decoders

Kumar, A. S., Schlosser, T., Langner, H., Ritter, M., & Kowerko, D. (2023) · Bioengineering · doi:10.3390/bioengineering10101177 · Cited by 12

Pixel-Level Reconstruction and Classification for Noisy Handwritten Bangla Characters

Karki, M., Liu, Q., DiBiano, R., Basu, S., & Mukhopadhyay, S. (2018) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr-2018.2018.00095 · Cited by 13

Multi-scale pedestrian detection using skip pooling and recurrent convolution

Zhang, C. G., & Kim, J. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6240-x · Cited by 11

A Deep Network Approach to Multitemporal Cloud Detection

Tuia, D., Kellenberger, B., Pérez-Suay, A., & Camps‐Valls, G. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8517312 · Cited by 13

Geometric Image Correspondence Verification by Dense Pixel Matching

Laskar, Z., Melekhov, I., Tavakoli, H. R., & Ylioinas, J. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093482 · Cited by 13

Alignment of Local and Global Features from Multiple Layers of Convolutional Neural Network for Image Classification

dos Santos, F. P., & Ponti, M. A. (2019) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2019.00040 · Cited by 13

A Method of Hierarchical Image Retrieval for Real-Time Photogrammetry Based on Multiple Features

Zhan, Z., Zhou, G., & Yang, X. D. (2020) · IEEE Access · doi:10.1109/access.2020.2969287 · Cited by 13

Attention-based bi-directional refinement network for salient object detection

Yuan, J., Wei, J., Wattanachote, K., Zeng, K., Luo, X., Xu, Q., & Gong, Y. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03040-8 · Cited by 13

Fully extracting feature correlation between and within stages for semantic segmentation

Yuan, F., Li, K., Wang, C., Shi, J., & Zhu, Y. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103578 · Cited by 13

Deep learning methods for autonomous driving scene understanding tasks: A review

Awan, M., Whangbo, T. K., & Shin, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128098 · Cited by 17

Automated defect detection of insulated gate bipolar transistor based on computed laminography imaging

Li, Y., Liu, S., Li, C., Zheng, Y., Wei, C., Liu, B., & Yang, Y. (2020) · Microelectronics Reliability · doi:10.1016/j.microrel.2020.113966 · Cited by 12

Salt interpretation with U-SaltNet

Zhou, H.-B., Xu, S. H., Ionescu, G., Laomana, M., & Weber, N. (2020) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/segam2020-3423283.1 · Cited by 13

Coordinate-based anchor-free module for object detection

Tang, Z., Yang, J., Pei, Z., & Song, X. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02373-8 · Cited by 12

Deep Learning-based Model for Automatic Salt Rock Segmentation

Li, H., Hu, Q., Mao, Y., Niu, F., & Liu, C. (2021) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-021-02673-y · Cited by 13

A 3D CNN APPROACH FOR CHANGE DETECTION IN HR SATELLITE IMAGE TIME SERIES BASED ON A PRETRAINED 2D CNN

Meshkini, K., Bovolo, F., & Bruzzone, L. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b3-2022-143-2022 · Cited by 12

Automated Defect Analysis of Additively Fabricated Metallic Parts Using Deep Convolutional Neural Networks

Nemati, S., Ghadimi, H., Li, X. S., Butler, L. G., Wen, H., & Guo, S. (2022) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp6060141 · Cited by 12

Low-pass U-Net: a segmentation method to improve strip steel defect detection

Liu, B., Yang, B., Zhao, Y., & Li, J. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/aca34a · Cited by 13

Bolt Positioning Detection Based on Improved YOLOv5 for Bridge Structural Health Monitoring

Wang, D., Zhang, M., Sheng, D., & Chen, W. (2022) · Sensors · doi:10.3390/s23010396 · Cited by 12

Vision UFormer: Long-range monocular absolute depth estimation

Polasek, T., Čadík, M., Keller, Y., & Beneš, B. (2022) · Computers & Graphics · doi:10.1016/j.cag.2023.02.003 · Cited by 12

Organ Segmentation in Poultry Viscera Using RGB-D

Philipsen, M. P., Dueholm, J. V., Jørgensen, A., Escalera, S., & Moeslund, T. B. (2018) · Sensors · doi:10.3390/s18010117 · Cited by 12

Learning deep representations for semantic image parsing: a comprehensive overview

Huang, L., Peng, J., Zhang, R., Li, G., & Lin, L. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-018-7195-8 · Cited by 12

A Densenet Based Robust Face Detection Framework

Nandy, A. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00229 · Cited by 12

Monocular Visual-IMU Odometry: A Comparative Evaluation of Detector–Descriptor-Based Methods

Dong, X., Dong, X., Dong, J., & Zhou, H. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2919003 · Cited by 12

Data Augmentation for Improving Deep Learning Models in Building Inspections or Postdisaster Evaluation

Leach, S., Xue, Y., Sridhar, R., Paal, S. G., Wang, Z., & Murphy, R. R. (2021) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0001594 · Cited by 12

Multimedia ontology population through semantic analysis and hierarchical deep features extraction techniques

Muscetti, M., Rinaldi, A. M., Russo, C., & Tommasino, C. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01669-6 · Cited by 12

Intelligent Detection Using Convolutional Neural Network (ID-CNN)

Kapoor, A. J., Fan, H., & Sardar, M. S. (2019) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/234/1/012061 · Cited by 12

Object Detection Free Instance Segmentation With Labeling TransformationsPreprint

Jin, L., Chen, Z., & Zhuowen Tu, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.08991 · Cited by 12

Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers

Miech, A., Alayrac, J.-B., Laptev, I., Sivic, J., & Zisserman, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00970 · Cited by 149

MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion

Ma, Y., Huang, Z., Yang, Y., Chen, Z., Dong, Q., Zhang, S., & Li, Y. (2025) · Biomimetics · doi:10.3390/biomimetics10030178 · Cited by 13

Review of deep learning-based segmentation methods: Popular approaches, literature gaps, and opportunities

ÇELİK, M., & İni̇k, Ö. (2025) · Displays · doi:10.1016/j.displa.2025.103225 · Cited by 15

Core Sampling Framework for Pixel Classification

Karki, M., DiBiano, R., Basu, S., & Mukhopadhyay, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68612-7_70 · Cited by 11

Correcting Misaligned Rural Building Annotations in Open Street Map Using Convolutional Neural Networks Evidence

Vargas-Muñoz, J. E., Marcos, D., Lobry, S., dos Santos, J. A., Falcão, A. X., & Tuia, D. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8518711 · Cited by 11

Pyramid Ensemble Convolutional Neural Network for Virtual Computed Tomography Image Prediction in a Selective Laser Melting Process

Wang, L., Chen, X., Henkel, D., & Jin, R. (2021) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4051077 · Cited by 11

A Unified Two-Stage Model for Separating Superimposed Images

Duan, H., Min, X., Shen, W., & Zhai, G. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746606 · Cited by 11

Cinematographic Shot Classification with Deep Ensemble Learning

Vacchetti, B., & Cerquitelli, T. (2022) · Electronics · doi:10.3390/electronics11101570 · Cited by 11

Bidirectional Flow Decision Tree for Reliable Remote Sensing Image Scene Classification

Feng, J., Wang, D., & Gu, Z. (2022) · Remote Sensing · doi:10.3390/rs14163943 · Cited by 11

Semantic-Aware Fine-Grained Correspondence

Hu, Y., Wang, R., Zhang, K., & Gao, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_6 · Cited by 11

GANmouflage: 3D Object Nondetection with Texture Fields

Guo, R., Collins, J., de Lima, O., & Owens, A. (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/cvpr52729.2023.00456 · Cited by 11

Environmental Perception for Intelligent Vehicles

Armingol, J. M., Kurano, J. A., Aliane, N., Clavijo, M., Campos‐Cordobés, S., de la Escalera, A., Ser, J. D., Andrés, J. F., García, F., Jiménez, F., et al. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812800-8.00002-3 · Cited by 11

Skin Lesion Segmentation using Deep Hypercolumn Descriptors

Ramachandram, D., & Taylor, G. W. (2017) · Journal of Computational Vision and Imaging Systems · doi:10.15353/vsnl.v3i1.173 · Cited by 11

RC-CNN: Reverse Connected Convolutional Neural Network for Accurate Player Detection

Zhang, L., Lu, Y., Song, G., & Zheng, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-97310-4_50 · Cited by 11

Capsule Endoscopy Image Classification with Deep Convolutional Neural Networks

Cao, Y., Yang, W., Chen, K., Ren, Y., & Liao, Q. (2018) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2018.8780859 · Cited by 12

Facial Expression Recognition with Skip-Connection to Leverage Low-Level Features

Verma, M., Kobori, H., Nakashima, Y., Takemura, N., & Nagahara, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803396 · Cited by 11

APLNet: Attention-enhanced progressive learning network

Zhang, H., Kang, D., He, H., & Wang, F. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.086 · Cited by 11

Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks

He, Y., Qian, J., Le, C. X., Hetang, C., Lyu, Q., Wang, W., & Yue, T. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1197 · Cited by 16

Multi-scale and multi-branch feature representation for person re-identification

Jiao, S., Pan, Z., Hu, G., Shen, Q., Du, L., Chen, Y., & Wang, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.074 · Cited by 11

On Equivariant and Invariant Learning of Object Landmark Representations

Cheng, Z., Su, J.-C., & Maji, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00975 · Cited by 12

Y-Net: Dual-branch Joint Network for Semantic Segmentation

Chen, Y., & Hu, H. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3460940 · Cited by 11

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID‐19 Detection

Valizadeh, A., & Shariatee, M. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/7265644 · Cited by 13

A Learned Representation of Artist-Specific Colourisation

van Noord, N., & Postma, E. O. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.343 · Cited by 10

The 3D-Pitoti Dataset

Poier, G., Seidl, M., Zeppelzauer, M., Reinbacher, C., Schaich, M., Bellandi, G., Marretta, A., & Bischof, H. (2017) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3095713.3095719 · Cited by 17

Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour DetectionPreprint

Zhang, Z., Xing, F., Su, H., Shi, X., & Yang, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.07281 · Cited by 14

PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature EmbeddingPreprint

Chen, X., Wang, J., & Hebert, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00192 · Cited by 17

A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers

Anwar, S., Barnes, N. M., & Petersson, L. (2023) · Electronics · doi:10.3390/electronics12234877 · Cited by 14

Emerging Trends in Wildfire Detection Through the Lens of Computer Vision and Wildfire Emission Quantification: A Comprehensive Survey

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Improving semantic segmentation with knowledge reasoning network

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