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

Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680

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

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891

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

Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

Yang, C., Liu, F., Huang, J.-H., Tian, M., Lin, M., Liu, Y. C., Morikawa, H., Yang, H.-H., & Tegner, J. N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21074-8_28 · Cited by 10

Deep Semantic Segmentation Neural Networks of Railway Scene

He, Z., Tang, P., Jin, W., Hu, C., & Li, W. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8483877 · Cited by 10

Few-shot hypercolumn-based mitochondria segmentation in cardiac and outer hair cells in focused ion beam-scanning electron microscopy (FIB-SEM) data

Dietlmeier, J., McGuinness, K., Rugonyi, S., Wilson, T., Nuttall, A. L., & O'Connor, N. E. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.10.031 · Cited by 11

Learning rich features with hybrid loss for brain tumor segmentation

Huang, D., Wang, M., Zhang, L., Li, H., Ye, M., & Li, A. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01431-y · Cited by 10

SnipeDet: Attention-guided pyramidal prediction kernels for generic object detection

Chen, S., Cheng, Z., Zhang, L., & Zheng, Y. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.10.026 · Cited by 10

SkeletonNetV2: A Dense Channel Attention Blocks for Skeleton Extraction

Nathan, S., & Kansal, P. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00243 · Cited by 10

Self-Balanced R-CNN for instance segmentation

Rossi, L., Karimi, A., & Prati, A. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103595 · Cited by 10

Capturing the Geometry of Object Categories from Video Supervision

Novotný, D., Larlus, D., & Vedaldi, A. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2871117 · Cited by 10

Gradually Updated Neural Networks for Large-Scale Image RecognitionPreprint

Qiao, S., Zhang, Z., Shen, W., Wang, B., & Yuille, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.09280 · Cited by 11

FCSS: Fully Convolutional Self-Similarity for Dense Semantic CorrespondencePreprint

Kim, S., Min, D., Ham, B., Jeon, S., Lin, S. C.-F., & Sohn, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.00926 · Cited by 10

Image Matters: Visually modeling user behaviors using Advanced Model ServerPreprint

Ge, T., Zhao, L., Zhou, G., Chen, K., Liu, S., Yi, H., Hu, Z., Liu, B., Sun, P., Liu, H., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.06505 · Cited by 13

PatternNet: Visual Pattern Mining with Deep Neural NetworkPreprint

Li, H., Ellis, J. G., Zhang, L., & Chang, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06339 · Cited by 10

Detection of Thymoma Disease Using mRMR Feature Selection and Transformer Models

Ağar, M., Aydın, S., Çakmak, M., Koç, M., & Toğaçar, M. (2024) · Diagnostics · doi:10.3390/diagnostics14192169 · Cited by 9

Multi-layer feature fusion for high-accuracy solid waste classification using a hybrid deep learning model

Çelik, G. (2025) · The Visual Computer · doi:10.1007/s00371-025-04031-3 · Cited by 13

Flash-Split: 2D Reflection Removal with Flash Cues and Latent Diffusion Separation

Wang, T., Xie, M., Cai, H., Shah, S., & Metzler, C. A. (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/cvpr52734.2025.00534 · Cited by 9

Bi-Directional Dynamic Interaction Network for Cross-Modality Person Re-Identification

Zheng, A., Feng, M., Li, C., Tang, J., & Luo, B. (2023) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2023.19280 · Cited by 9

Scene-driven Retrieval in Edited Videos using Aesthetic and Semantic Deep Features

Baraldi, L., Grana, C., & Cucchiara, R. (2016) · International Conference on Multimedia Retrieval · doi:10.1145/2911996.2912012 · Cited by 9

Learning Semantic Part-Based Models from Google Images

Modolo, D., & Ferrari, V. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2724029 · Cited by 9

Crowd-Guided Ensembles

Kaspar, A., Patterson, G., Kim, C., Aksoy, Y., Matusik, W., & Elgharib, M. A. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173685 · Cited by 9

A Self-Supervised Learning Manipulator Grasping Approach Based on Instance Segmentation

Shu, X., Liu, C., Li, T., Wang, C., & Chi, C. (2018) · IEEE Access · doi:10.1109/access.2018.2877796 · Cited by 9

GISCA: Gradient-Inductive Segmentation Network With Contextual Attention for Scene Text Detection

Cao, M., Zou, Y., Yang, D., & Liu, C. (2019) · IEEE Access · doi:10.1109/access.2019.2915513 · Cited by 9

An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

Gain, M., Rahman, M. A., Debnath, R., Alnfiai, M. M., Sheikh, A., Masud, M., & Bairagi, A. K. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.034809 · Cited by 9

Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques

Gundu, S., & Syed, H. (2023) · Sensors · doi:10.3390/s23052569 · Cited by 10

TCNet: A Transformer–CNN Hybrid Network for Marine Aquaculture Mapping from VHSR Images

Fu, Y., Zhang, W., Bi, X., Wang, P., & Gao, F. (2023) · Remote Sensing · doi:10.3390/rs15184406 · Cited by 11

An aspect representation for object manipulation based on convolutional neural networks

Ku, L. Y., Miller, E. G., & Grupen, R. A. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989098 · Cited by 9

Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification

Liu, Y., Yang, H., & Zhao, Q. (2019) · Applied Sciences · doi:10.3390/app9112255 · Cited by 9

MSSTResNet-TLD: A robust tracking method based on tracking-learning-detection framework by using multi-scale spatio-temporal residual network feature model

Liu, B., Liu, Q., Zhang, T., & Yang, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.024 · Cited by 9

Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly

Jiang, H., & Grauman, K. L. (2017) · 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.2017.366 · Cited by 9

Distributed and consistent multi-image feature matching via QuickMatch

Serlin, Z., Yang, G., Sookraj, B., Belta, C. A., & Tron, R. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920917465 · Cited by 9

A hierarchical coarse-to-fine perception for small-target categorization of butterflies under complex backgrounds

Lin, Z., Jia, J., Gao, W., & Huang, F. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-190747 · Cited by 8

Rotation-Invariant Latent Semantic Representation Learning for Object Detection in VHR Optical Remote Sensing Images

Yao, X., Feng, X., Cheng, G., Han, J., & Guo, L. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8899285 · Cited by 9

Image Colorization Using a Deep Transfer Learning

Kiani, L., Saeed, M., & Nezamabadi–pour, H. (2020) · Iranian Joint Congress on Fuzzy and Intelligent Systems · doi:10.1109/cfis49607.2020.9238737 · Cited by 9

Computation of CNN’s Sensitivity to Input Perturbation

Xiang, L., Zeng, X., Wu, S., Liu, Y., & Yuan, B. (2021) · Neural Processing Letters · doi:10.1007/s11063-020-10420-7 · Cited by 9

Detection-Oriented Backbone Trained from Near Scratch and Local Feature Refinement for Small Object Detection

Yan, Z., Zheng, H., Li, Y., & Chen, L. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10493-y · Cited by 10

Interweave features of Deep Convolutional Neural Networks for semantic segmentation

Bai, S., Gu, W., & Kong, L. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104587 · Cited by 9

Superdense-scale network for semantic segmentation

Li, Z., Jiang, J., Chen, X., Qi, H., Li, Q., Liu, J., Zheng, L., Liu, M., & Zhang, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.103 · Cited by 8

Enhanced FCN for farmland extraction from remote sensing image

Pan, J., Wei, Z., Zhao, Y., Zhou, Y., Lin, X., Zhang, W., & Tang, C. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12141-6 · Cited by 9

Do Semantic Parts Emerge in Convolutional Neural Networks?

González-García, A., Modolo, D., & Ferrari, V. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1048-0 · Cited by 9

Robust person head detection based on multi-scale representation fusion of deep convolution neural network

Wang, Y., Yin, Y., Wu, W., Sun, S., & Wang, X. (2017) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2017.8324433 · Cited by 9

Ensemble-based Adaptive Single-shot Multi-box Detector

Thakar, V., Ahmed, W., Soltani, M. M., & Yu, J. Y. (2018) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc.2018.8530893 · Cited by 9

Exploiting hierarchical visual features for visual question answering

Hong, J. K., Fu, J., Uh, Y., Mei, T., & Byun, H. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.035 · Cited by 10

Increasingly Specialized Perception Network for Fine-Grained Visual Categorization of Butterfly Specimens

Lin, Z., Jia, J., Gao, W., & Huang, F. (2019) · IEEE Access · doi:10.1109/access.2019.2938537 · Cited by 9

A New Localization Objective for Accurate Fine-Grained Affordance Segmentation Under High-Scale Variations

Hassanin, M., Khan, S. H., & Tahtalı, M. (2019) · IEEE Access · doi:10.1109/access.2019.2958608 · Cited by 9

Application of wearable inertial sensor in optimization of basketball player’s human motion tracking method

Ren, H., & Wang, X. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03110-2 · Cited by 9

Biologically inspired visual computing: the state of the art

Hao, W., Andolina, I. M., Wang, W., & Zhang, Z. (2020) · Frontiers of Computer Science · doi:10.1007/s11704-020-9001-8 · Cited by 9

Lesion Classification of Wireless Capsule Endoscopy Images

Yang, W., Cao, Y., Zhao, Q., Ren, Y., & Liao, Q. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759577 · Cited by 10

Pose-Guided Human Parsing with Deep Learned FeaturesPreprint

Xia, F., Zhu, J., Wang, P., & Yuille, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.03881 · Cited by 9

Towards Lifelong Self-Supervision: A Deep Learning Direction for RoboticsPreprint

Wong, J. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.00201 · Cited by 12

DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object DetectionPreprint

Lang, S., Ventola, F., & Kersting, K. (2021) · arXiv (Cornell University) · Cited by 10

Weakly Supervised Co-training with Swapping Assignments for Semantic Segmentation

Yang, X., Rahmani, H., Black, S., & Williams, B. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72992-8_26 · Cited by 8

Class Balanced PixelNet for Neurological Image Segmentation

Islam, M., & Ren, H. (2018) · International Conference on Bioinformatics and Computational Biology (ICBCB) · doi:10.1145/3194480.3194494 · Cited by 8

Associating grasp configurations with hierarchical features in convolutional neural networks

Ku, L. Y., Miller, E. G., & Grupen, R. A. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206059 · Cited by 8

Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks

Salberg, A.-B., Trier, Ø. D., & Kampffmeyer, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59129-2_17 · Cited by 8

SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks

Sarvadevabhatla, R. K., Dwivedi, I., Biswas, A., Manocha, S., & Babu, R. V. (2017) · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · doi:10.48550/arxiv.1709.01295 · Cited by 8

Dense small face detection based on regional cascade multi‐scale method

Ke, X., Li, J., & Guo, W. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.6571 · Cited by 8

Robust appearance feature learning using pixel‐wise discrimination for visual tracking

Kim, M., & Kim, S. (2019) · ETRI Journal · doi:10.4218/etrij.2018-0486 · Cited by 8

Convolution-deconvolution architecture with the pyramid pooling module for semantic segmentation

Malekijoo, A., & Fadaeieslam, M. J. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07990-7 · Cited by 8

DSFMA: deeply supervised fully convolutional neural networks based on multi-level aggregation for saliency detection

Ullah, I., Jian, M., Hussain, S., Guo, J., Li, L., Yu, H. L., Shaheed, K., & Yin, Y. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10111-4 · Cited by 8

Vision-Based Unmarked Road Detection with Semantic Segmentation using Mask R-CNN for Lane Departure Warning System

Undit, H. J. A., Hassan, M. F. A., & Zin, Z. M. (2021) · International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR) · doi:10.1109/isamsr53229.2021.9567892 · Cited by 8

Segmentation-based multi-scale attention model for KRAS mutation prediction in rectal cancer

Song, K., Zhao, Z., Wang, J., Qiang, Y., Zhao, J., & Zia, M. B. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01447-w · Cited by 8

AMANet: Adaptive Multi-Path Aggregation for Learning Human 2D-3D Correspondences

Wang, X., Guo, Y., Song, J., Gao, L., & Shen, H. T. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3135145 · Cited by 8

Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images

Du, X., He, S., Yang, H., & Wang, C. (2022) · Remote Sensing · doi:10.3390/rs14225830 · Cited by 7

Evaluating contour segment descriptors

Yang, C., Tiebe, O., Shirahama, K., Łukasik, E., & Grzegorzek, M. (2017) · Machine Vision and Applications · doi:10.1007/s00138-017-0823-9 · Cited by 8

Enlarging Effective Receptive Field of Convolutional Neural Networks for Better Semantic Segmentation

Gu, Y., Zhong, Z., Wu, S., & Xu, Y. D. (2017) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2017.7 · Cited by 8

Joint motion boundary detection and CNN-based feature visualization for video object segmentation

Kamranian, Z., Nilchi, A. R. N., Sadeghian, H., Tombari, F., & Navab, N. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04448-7 · Cited by 8

Deep Convolutional Neural Network with Segmentation Techniques for Chest X-Ray Analysis

Wang, B., Wu, Z., Khan, Z. U., Liu, C., & Zhu, M. (2019) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2019.8834117 · Cited by 8

DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide imagesPreprint

Narayanan, P., Raza, S. E. A., Dodson, A. R., Gusterson, B. A., Dowsett, M., & Yuan, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10850 · Cited by 8

Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet TransformPreprint

Ma, L., Stückler, J., Wu, T., & Cremers, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.01834 · Cited by 8

Practical Self-Driving Cars: Survey of the State-of-the-ArtPreprint

Saha, D., & De, S. (2022) · Preprints.org · doi:10.20944/preprints202202.0123.v1 · Cited by 8

Evaluating model generalization for cow detection in free-stall barn settings: Insights from the COw LOcalization (COLO) dataset

Das, M., Ferreira, G., & Chen, C. J. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101054 · Cited by 7

Semantic segmentation with hybrid pyramid pooling and stacked pyramid structure

Lian, X., Pang, Y., Han, J., & Pan, J. S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.126 · Cited by 7

Scale-pyramid dynamic atrous convolution for pixel-level labeling

Li, Z., Jiang, J., Chen, X., Zhang, M., Wang, Y., Li, Q., Qi, H., Liu, M., & Laganière, R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122695 · Cited by 7

Flash-Splat: 3D Reflection Removal with Flash Cues and Gaussian Splats

Xie, M., Cai, H., Shah, S., Xu, Y., Feng, B. Y., Huang, J., & Metzler, C. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73007-8_8 · Cited by 7

MASFNet: Multi-level attention and spatial sampling fusion network for pine wilt disease trees detection

Ren, D., Li, M., Hong, Z., Liu, L., Huang, J., Sun, H., Ren, S., Sao, P., Wang, W., & Zhang, J. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113073 · Cited by 7

Stacked Learning to Search for Scene Labeling

Cheng, F., He, X., & Zhang, H. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2668218 · Cited by 7

PASCAL Boundaries: A Semantic Boundary Dataset with a Deep Semantic Boundary Detector

Premachandran, V., Bonev, B., Lian, X., & Yuille, A. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.16 · Cited by 7

Two-dimensional multi-scale perceptive context for scene text recognition

Li, H., Yang, D., Huang, S., Lam, K., Jin, L., & Zhuang, Z. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.071 · Cited by 7

Computer Vision based Polyethylene Terephthalate (PET) Sorting for Waste Recycling

Bchir, O., Alghannam, S., Alsadhan, N. M., Alsumairy, R., Albelahid, R., & Almotlaq, M. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0121069 · Cited by 7

AdjMix: simplifying and attending graph convolutional networks

Liu, X., Lei, F., Xia, G., Zhang, Y., & Wei, W. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00567-8 · Cited by 7

Identifying risk factors of intracerebral hemorrhage stability using explainable attention model

Rangaraj, S., Islam, M., Vibashan, V. S., Wijethilake, N., Uppal, U., See, A. A. Q., Chan, J., James, M. L., King, N. K. K., & Ren, H. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02459-y · Cited by 7

Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation

Martinez-Sanchez, L., Borio, D., d’Andrimont, R., & van der Velde, M. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101757 · Cited by 7

Instance segmentation algorithm based on fine-grained feature perception and cross-path aggregation

Ma, J., Gu, S., Deng, Y., & Ao, T. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110754 · Cited by 7

PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization

Nagendra, S., & Kifer, D. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00139 · Cited by 7

Automatic Colorization with Improved Spatial Coherence and Boundary Localization

Zhang, W., Fang, C., & Li, G. (2017) · Journal of Computer Science and Technology · doi:10.1007/s11390-017-1739-6 · Cited by 7

Keyframes and Shot Boundaries: The Attributes of Scene Segmentation and Classification

Kumar, N., & Sukavanam, N. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-0761-4_74 · Cited by 7

Texture Segmentation using Siamese Network and Hierarchical Region Merging

Yamada, R., Ide, H., Yudistira, N., & Kurita, T. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545348 · Cited by 7

Widening residual refine edge reserved neural network for semantic segmentation

Su, W., & Wang, Z. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-018-7121-z · Cited by 7

BallTrack: Football ball tracking for real-time CCTV systems

Komorowski, J., Kurzejamski, G., & Sarwas, G. (2019) · Machine Vision Applications · doi:10.23919/mva.2019.8757880 · Cited by 7

Logistic Regression Region Weighting for Weakly Supervised Object Localization

Wang, L.-T., Wang, T., & Hu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2935011 · Cited by 7

Semantically Coherent 4D Scene Flow of Dynamic Scenes

Mustafa, A., & Hilton, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01241-w · Cited by 7

Weakly Supervised Fine-Grained Image Classification via Salient Region Localization and Different Layer Feature Fusion

Chen, F., Huang, G., Lan, J., Wu, Y., Pun, C., Ling, B. W., & Cheng, L. (2020) · Applied Sciences · doi:10.3390/app10134652 · Cited by 7

Efficient Residual Neural Network for Semantic Segmentation

Li, B., Zang, J., & Cao, J. (2021) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661821020103 · Cited by 7

Semantic segmentation of food images for automatic dietary monitoring

Aslan, S., Ciocca, G., & Schettini, R. (2018) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2018.8404824 · Cited by 7

Learning High-level Prior with Convolutional Neural Networks for Semantic SegmentationPreprint

Zheng, H., Liu, Y., Ji, M., Wu, F., & Fang, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06988 · Cited by 7

Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection DatasetPreprint

Pezzementi, Z. A., Tabor, T., Hu, P., Chang, J., Ramanan, D., Wellington, C., Babu, B. P. W., & Herman, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.07169 · Cited by 7

ShelfNet for Real-time Semantic SegmentationPreprint

Zhuang, J., & Yang, J. (2018) · arXiv (Cornell University) · Cited by 7

Boundary-aware Instance SegmentationPreprint

Hayder, Z., He, X., & Salzmann, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03129 · Cited by 7

SVS-VPR: A Semantic Visual and Spatial Information-Based Hierarchical Visual Place Recognition for Autonomous Navigation in Challenging Environmental Conditions

Arshad, S., & Park, T.-H. (2024) · Sensors · doi:10.3390/s24030906 · Cited by 6

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