Hypercolumns for object segmentation and fine-grained localization

Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

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Fully convolutional networks for semantic segmentation

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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

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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

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

Tong, X., Sun, B., Wei, J., Zuo, Z., & Su, S. (2021) · Remote Sensing · doi:10.3390/rs13163200 · Cited by 76

The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance MatchingPreprint

Yu, Q., Chang, X., Song, Y.-Z., Xiang, T., & Hospedales, T. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08106 · Cited by 91

Semantic segmentation using stride spatial pyramid pooling and dual attention decoder

Peng, C., & Ma, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107498 · Cited by 75

Deepblueberry: Quantification of Blueberries in the Wild Using Instance Segmentation

Gonzalez, S., Arellano, C., & Tapia, J. E. (2019) · IEEE Access · doi:10.1109/access.2019.2933062 · Cited by 78

Learning Video Object Segmentation from Static ImagesPreprint

Khoreva, A., Perazzi, F., Benenson, R., Schiele, B., & Sorkine‐Hornung, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.02646 · Cited by 75

A Survey on Object Instance Segmentation

Sharma, R., Saqib, M., Lin, C., & Blumenstein, M. (2022) · SN Computer Science · doi:10.1007/s42979-022-01407-3 · Cited by 83

Region-Based Semantic Segmentation with End-to-End Training

Caesar, H., Uijlings, J., & Ferrari, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_23 · Cited by 74

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition

Jianping Fan, B., Zhao, T., Kuang, Z., Zheng, Y., Zhang, J., Yu, J., & Peng, J. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2667405 · Cited by 76

Multiscale fully convolutional network with application to industrial inspection

Bian, X., Lim, S. N., & Zhou, N. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477595 · Cited by 72

Human Parsing with Contextualized Convolutional Neural Network

Liang, X., Xu, C., Shen, X., Yang, J., Tang, J., Lin, L., & Yan, S. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2537339 · Cited by 72

Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing

Shao, R., Lan, X., & Yuen, P. C. (2017) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272765 · Cited by 72

Optimal Transport in Reproducing Kernel Hilbert Spaces: Theory and Applications

Zhang, Z., Wang, M., & Nehorai, A. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2903050 · Cited by 72

Benchmarking the Robustness of Semantic Segmentation Models with Respect to Common Corruptions

Kamann, C., & Rother, C. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01383-2 · Cited by 78

Survey on 3D face reconstruction from uncalibrated images

Morales, A., Piella, G., & Sukno, F. M. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100400 · Cited by 80

Coarse-to-Fine Feature Mining for Video Semantic Segmentation

Sun, G., Liu, Y., Ding, H., Probst, T., & Gool, L. V. (2022) · 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/cvpr52688.2022.00313 · Cited by 72

Weakly Supervised Localization Using Deep Feature Maps

Bency, A. J., Kwon, H., Lee, H., Karthikeyan, S., & Manjunath, B. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_43 · Cited by 74

Graininess-Aware Deep Feature Learning for Robust Pedestrian Detection

Lin, C., Lu, J., Wang, G., & Zhou, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2966371 · Cited by 71

From Contexts to Locality: Ultra-high Resolution Image Segmentation via Locality-aware Contextual Correlation

Li, Q., Yang, W., Liu, W., Yu, Y., & He, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00716 · Cited by 71

Geometry-Aware Deep Network for Single-Image Novel View Synthesis

Liu, M., He, X., & Salzmann, M. (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.2018.00485 · Cited by 69

DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction

Cruz, R. S., Lebrat, L., Bourgeat, P., Fookes, C., Fripp, J., & Salvado, O. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00085 · Cited by 69

PANet: Patch-Aware Network for Light Field Salient Object Detection

Piao, Y., Jiang, Y., Zhang, M., Wang, J., & Lu, H. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3095512 · Cited by 69

Hierarchical Cellular Automata for Visual Saliency

Qin, Y., Feng, M., Lu, H., & Cottrell, G. W. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-017-1062-2 · Cited by 68

A multi-path adaptive fusion network for multimodal brain tumor segmentation

Ding, Y., Gong, L., Zhang, M., Li, C., & Qin, Z. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.078 · Cited by 68

Correcting rural building annotations in OpenStreetMap using convolutional neural networks

Vargas-Muñoz, J. E., Lobry, S., Falcão, A. X., & Tuia, D. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.11.010 · Cited by 67

Context-Reinforced Semantic Segmentation

Zhou, Y., Sun, X., Zha, Z.-J., & Zeng, W. (2019) · 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.2019.00417 · Cited by 67

Residual Learning for Salient Object Detection

Feng, M., Lu, H., & Yu, Y. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2975919 · Cited by 67

Multiscale multilevel context and multimodal fusion for RGB-D salient object detection

Wu, J., Zhou, W., Luo, T., Yu, L., & Lei, J. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107766 · Cited by 68

Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition

Bhunia, A. K., Sain, A., Kumar, A., Ghose, S., Chowdhury, P. N., & Song, Y.-Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01467 · Cited by 67

COCO-Stuff: Thing and Stuff Classes in ContextPreprint

Caesar, H., Uijlings, J., & Ferrari, V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03716 · Cited by 112

Embedding Attention and Residual Network for Accurate Salient Object Detection

Chen, S., Wang, B., Tan, X., & Hu, X. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2879859 · Cited by 67

ELASTIC: Improving CNNs With Dynamic Scaling Policies

Wang, H., Kembhavi, A., Farhadi, A., Yuille, A., & Rastegari, M. (2019) · 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.2019.00236 · Cited by 66

Bidirectional Graph Reasoning Network for Panoptic Segmentation

Wu, Y., Zhang, G., Gao, Y., Deng, X., Gong, K., Liang, X., & Lin, L. (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/cvpr42600.2020.00910 · Cited by 66

Defect Detection and Classification by Training a Generic Convolutional Neural Network Encoder

Dong, X., Taylor, C., & Cootes, T. F. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3031188 · Cited by 69

Real-Time Human Motion Capture with Multiple Depth Cameras

Shafaei, A., & Little, J. J. (2016) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2016.25 · Cited by 65

SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness

Gu, J., Zhao, H., Volker Tresp, C., & Torr, P. H. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19818-2_18 · Cited by 75

One-Shot Instance SegmentationPreprint

Michaelis, C., Ustyuzhaninov, I., Bethge, M., & Ecker, A. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.11507 · Cited by 99

AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching

Novotný, D., Larlus, D., & Vedaldi, A. (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.2017.306 · Cited by 65

Learning-Based Cloth Material Recovery from Video

Yang, S., Liang, J., & Lin, M. C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.470 · Cited by 65

S4Net: Single Stage Salient-Instance Segmentation

Fan, R., Cheng, M., Hou, Q., Mu, T., Wang, J., & Hu, S. (2019) · 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.2019.00626 · Cited by 64

A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving

Guo, Z., Huang, Y., Hu, X., Wei, H., & Zhao, B. (2021) · Electronics · doi:10.3390/electronics10040471 · Cited by 66

Sparsely Aggregated Convolutional Networks

Zhu, L., Deng, R., Maire, M., Deng, Z., Mori, G., & Tan, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_12 · Cited by 65

A Deep Learning Approach to Solar-Irradiance Forecasting in Sky-Videos

Siddiqui, T. A., Bharadwaj, S., & Kalyanaraman, S. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00234 · Cited by 65

Sparse-to-Dense Hypercolumn Matching for Long-Term Visual Localization

Germain, H., Bourmaud, G., & Lepetit, V. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00063 · Cited by 61

A Thorough Benchmark and a New Model for Light Field Saliency Detection

Gao, W., Fan, S., Li, G., & Lin, W. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3235415 · Cited by 62

Bilinear CNNs for Fine-grained Visual RecognitionPreprint

Lin, T., RoyChowdhury, A., & Maji, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.07889 · Cited by 62

Recurrent Neural Networks for Semantic Instance Segmentation

Salvador, A., Bellver, M., Campos, V., Baradad, M., Marques, F., Torres, J., & Giro-i-Nieto, X. (2017) · arXiv · doi:10.48550/arxiv.1712.00617 · Cited by 62

Cross Pixel Optical-Flow Similarity for Self-supervised Learning

Mahendran, A., Thewlis, J., & Vedaldi, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20873-8_7 · Cited by 59

Attention-guided siamese networks for change detection in high resolution remote sensing images

Yin, H., Weng, L., Li, Y., Xia, M., Hu, K., Lin, H., & Qian, M. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103206 · Cited by 65

Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks

Liao, F., Chen, X., Hu, X., & Song, S. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2778799 · Cited by 60

Progressive Hard-Mining Network for Monocular Depth Estimation

Zhang, Z., Xu, C., Yang, J. J., Gao, J., & Cui, Z. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2821979 · Cited by 64

Learning Semantic Deformation Flows with 3D Convolutional Networks

Yumer, M. E., & Mitra, N. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_18 · Cited by 60

Learning Category-Specific Deformable 3D Models for Object Reconstruction

Tulsiani, S., Kar, A., Carreira, J., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2574713 · Cited by 65

Deep features class activation map for thermal face detection and tracking

Kwaśniewska, A., Rumiński, J., & Rad, P. (2017) · Conference on Human System Interactions · doi:10.1109/hsi.2017.8004993 · Cited by 60

Deep hierarchical guidance and regularization learning for end-to-end depth estimation

Zhang, Z., Xu, C., Yang, J. J., Tai, Y. Y., & Chen, L. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.016 · Cited by 58

Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning

Walambe, R. A., Marathe, A., & Kotecha, K. V. (2021) · Drones · doi:10.3390/drones5030066 · Cited by 62

Learning to Compose Hypercolumns for Visual Correspondence

Min, J., Lee, J., Ponce, J., & Cho, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_21 · Cited by 61

GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things

Yu, J., Xue, H., Liu, B., Wang, Y., Zhu, S., & Ding, M. (2020) · Sensors · doi:10.3390/s21010058 · Cited by 61

Context-aware Self-Attention Networks for Natural Language Processing

Yang, B., Wang, L., Wong, D. F., Shi, S., & Tu, Z. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.06.009 · Cited by 62

Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes

Chen, Y., Jin, D., Guo, B., & Bai, X. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3186731 · Cited by 59

Accurate Glass Insulators Defect Detection in Power Transmission Grids Using Aerial Image Augmentation

Cao, Y. J., Xu, H., Su, C., & Yang, Q. (2022) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2022.3202958 · Cited by 60

Small-Object Sensitive Segmentation Using Across Feature Map Attention

Sang, S., Zhou, Y., Islam, M. T., & Xing, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3211171 · Cited by 60

Semantic Image Segmentation: Two Decades of Research

Csurka, G., Volpi, R., & Chidlovskii, B. (2022) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000095 · Cited by 84

Pose-Guided Human Parsing by an AND/OR Graph Using Pose-Context Features

Xia, F., Zhu, J., Wang, P., & Yuille, A. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10460 · Cited by 58

A Unified Model for Near and Remote Sensing

Workman, S., Zhai, M., Crandall, D., & Jacobs, N. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.293 · Cited by 58

Traffic sign detection and recognition based on pyramidal convolutional networks

Liang, Z., Shao, J., Zhang, D., & Gao, L. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04086-z · Cited by 58

CTIF-Net: A CNN-Transformer Iterative Fusion Network for Salient Object Detection

Yuan, J., Zhu, A., Xu, Q., Wattanachote, K., & Gong, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3321190 · Cited by 58

Single Image Reflection Separation via Component Synergy

Hu, Q., & Guo, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01208 · Cited by 57

Teaching Compositionality to CNNs

Stone, A., Wang, H., Stark, M., Liu, Y., Phoenix, D. S., & George, D. (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.2017.85 · Cited by 57

Graph‐convolutional‐network‐based interactive prostate segmentation in MR images

Tian, Z., Li, X., Zheng, Y., Chen, Z., Zhong, S. S., Liu, L., & Fei, B. (2020) · Medical Physics · doi:10.1002/mp.14327 · Cited by 57

Boosting Salient Object Detection With Transformer-Based Asymmetric Bilateral U-Net

Qiu, Y., Liu, Y., Zhang, L., Lu, H.-T., & Xu, J. (2021) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2023.3307693 · Cited by 58

Modular Lightweight Network for Road Object Detection Using a Feature Fusion Approach

Liu, Y., Cao, S., Lasang, P., & Shen, S. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2019.2945053 · Cited by 56

Boosting vision transformers for image retrieval

Song, C. H., Yoon, J.-Y., Choi, S., & Avrithis, Y. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00019 · Cited by 59

A CONVOLUTIONAL NETWORK FOR SEMANTIC FACADE SEGMENTATION AND INTERPRETATION

Schmitz, M., & Mayer, H. (2016) · 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-xli-b3-709-2016 · Cited by 56

Automatic triage for a photo series

Chang, H., Yu, F., Wang, J., Ashley, D., & Finkelstein, A. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925908 · Cited by 58

Automatic Building Extraction in Aerial Scenes Using Convolutional NetworksPreprint

Yuan, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.06564 · Cited by 76

Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection

Hsu, C.-Y., Li, W., & Wang, S. (2021) · Remote Sensing · doi:10.3390/rs13112116 · Cited by 53

Video Emotion Recognition with Transferred Deep Feature Encodings

Xu, B., Fu, Y., Jiang, Y.-G., Li, B., & Sigal, L. (2016) · Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval · doi:10.1145/2911996.2912006 · Cited by 53

Reversible Recursive Instance-Level Object Segmentation

Liang, X., Wei, Y., Shen, X., Jie, Z., Feng, J., Lin, L., & Yan, S. (2016) · 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.2016.75 · Cited by 53

Multiscale Representation Learning for Image Classification: A Survey

Jiao, L., Gao, J., Liu, X., Liu, F., Yang, S., & Hou, B. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3135248 · Cited by 60

Robust Perception and Precise Segmentation for Scribble-Supervised RGB-D Saliency Detection

Li, L., Han, J., Liu, N., Khan, S. H., Cholakkal, H., Anwer, R. M., & Khan, F. S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3324807 · Cited by 53

Hallucinating Agnostic Images to Generalize Across Domains

Carlucci, F. M., Russo, P., Tommasi, T., & Barbara Caputo, B. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00403 · Cited by 57

An Efficient Network for Surface Defect Detection

Lin, Z., Ye, H., Zhan, B., & Huang, X. (2020) · Applied Sciences · doi:10.3390/app10176085 · Cited by 53

CANet: Centerness-Aware Network for Object Detection in Remote Sensing Images

Shi, L., Kuang, L., Xu, X., Pan, B., & Shi, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3068970 · Cited by 53

Task-Driven Semantic Coding via Reinforcement Learning

Li, X., Shi, J., & Chen, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3091909 · Cited by 55

Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences

Fathy, M. E., Tran, Q.-H., Zia, M. Z., Vernaza, P., & Chandraker, M. K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_49 · Cited by 52

Automatic segmentation tool for 3D digital rocks by deep learning

Phan, J., Ruspini, L. C., & Lindseth, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-98697-z · Cited by 52

Fast Multi-image Matching via Density-Based Clustering

Tron, R., Zhou, X., Esteves, C., & Daniilidis, K. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.437 · Cited by 49

Real-Time Document Localization in Natural Images by Recursive Application of a CNN

Javed, K., & Faisal Shafait, B. (2017) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2017.26 · Cited by 51

Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning

Han, Z., Wei, B., Leung, S., Nachum, I. B., Laidley, D. T., & Li, S. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9365-1 · Cited by 51

Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework

Yan, Z., Yang, X. Y., & Cheng, K.-T. T. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2966594 · Cited by 51

PixelNet: Towards a General Pixel-level ArchitecturePreprint

Bansal, A., Chen, X., Russell, B. C., Gupta, A. K., & Ramanan, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.06694 · Cited by 51

Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

Eslami, E., & Yun, H. (2021) · Sensors · doi:10.3390/s21155137 · Cited by 50

Decoder Network over Lightweight Reconstructed Feature for Fast Semantic Style Transfer

Lu, M., Zhao, H., Yao, A., Xu, F., Chen, Y., & Zhang, L. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.270 · Cited by 50

A survey on indoor RGB-D semantic segmentation: from hand-crafted features to deep convolutional neural networks

Fooladgar, F., & Kasaei, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7684-3 · Cited by 54

Deep Attention and Multi-Scale Networks for Accurate Remote Sensing Image Segmentation

Qi, X., Li, K., Liu, P., Zhou, X., & Sun, M. (2020) · IEEE Access · doi:10.1109/access.2020.3015587 · Cited by 50

A transfer learning approach for improved classification of carbon nanomaterials from TEM images

Luo, Q., Holm, E. A., & Wang, C. (2020) · Nanoscale Advances · doi:10.1039/d0na00634c · Cited by 51

End-to-End Instance Segmentation and Counting with Recurrent Attention.Preprint

Ren, M., & Zemel, R. S. (2016) · arXiv (Cornell University) · Cited by 50

Quaternion-Valued Correlation Learning for Few-Shot Semantic Segmentation

Zheng, Z., Huang, G., Yuan, X., Pun, C., Liu, H., & Ling, B. W. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3223150 · Cited by 50

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Approaching the Real-World: Supporting Activity Recognition Training with Virtual IMU Data

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Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns

Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2578328 · Cited by 102

Universidad de los Andes BogotaPreprint

Hariharan, B., Girshick, R., & Malik, J. (2016)

THEME 3Hyperfeatures — Multilevel Local Coding for Visual Recognition

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