Object Recognition Datasets and Challenges: A Review | Paper Citation Record

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This paper cites IEEE Xplore Full-Text PDF:.

Object Recognition Datasets and Challenges: A Review IEEE Xplore Full-Text PDF:

Reference 1

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This paper cites 2D and 3D face recognition: A survey.

Object Recognition Datasets and Challenges: A Review 2D and 3D face recognition: A survey

Reference 2

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This paper cites Endoscopy artifact detection (EAD 2019) challenge dataset , 1–13doi: 10.17632/C7FJBXCGJ9.1.

Object Recognition Datasets and Challenges: A Review Endoscopy artifact detection (EAD 2019) challenge dataset , 1–13doi: 10.17632/C7FJBXCGJ9.1

Reference 4

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This paper cites EAD 2019.

Object Recognition Datasets and Challenges: A Review EAD 2019

Reference 5

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This paper cites Overview of artificial intelligence in Medicine.

Object Recognition Datasets and Challenges: A Review Overview of artificial intelligence in Medicine

Reference 6

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This paper cites CVPR 2019 W AD Beyond Single-frame Perception Challenge.

Object Recognition Datasets and Challenges: A Review CVPR 2019 W AD Beyond Single-frame Perception Challenge

Reference 7

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This paper cites ICIAR 2018.

Object Recognition Datasets and Challenges: A Review ICIAR 2018

Reference 8

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This paper cites BACH: Grand challenge on breast cancer histology images.

Object Recognition Datasets and Challenges: A Review BACH: Grand challenge on breast cancer histology images

Reference 9

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This paper cites The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans.

Object Recognition Datasets and Challenges: A Review The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans

Reference 10

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This paper cites UMDFaces: An Annotated Face Dataset for Training Deep Networks.

Object Recognition Datasets and Challenges: A Review UMDFaces: An Annotated Face Dataset for Training Deep Networks

Reference 11

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This paper cites ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.

Object Recognition Datasets and Challenges: A Review ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

Reference 12

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This paper cites Surf: Speeded up robust features , 404–417.

Object Recognition Datasets and Challenges: A Review Surf: Speeded up robust features , 404–417

Reference 13

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This paper cites SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences.

Object Recognition Datasets and Challenges: A Review SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Reference 14

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This paper cites OPENSURF ACES: A richly annotated catalog of surface appearance.

Object Recognition Datasets and Challenges: A Review OPENSURF ACES: A richly annotated catalog of surface appearance

Reference 15

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This paper cites Greedy layer-wise training of deep networks, in: Advances in neural information processing systems, pp.

Object Recognition Datasets and Challenges: A Review Greedy layer-wise training of deep networks, in: Advances in neural information processing systems, pp

Reference 16

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This paper cites Names and faces in the news.

Object Recognition Datasets and Challenges: A Review Names and faces in the news

Reference 17

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This paper cites CVPR 2018 - Berkeley DeepDrive challenges.

Object Recognition Datasets and Challenges: A Review CVPR 2018 - Berkeley DeepDrive challenges

Reference 19

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This paper cites Towards automatic polyp detection with a polyp appearance model.

Object Recognition Datasets and Challenges: A Review Towards automatic polyp detection with a polyp appearance model

Reference 20

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This paper cites Automatic 3D face authentication.

Object Recognition Datasets and Challenges: A Review Automatic 3D face authentication

Reference 21

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This paper cites Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.

Object Recognition Datasets and Challenges: A Review Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

Reference 22

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This paper cites The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections.

Object Recognition Datasets and Challenges: A Review The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections

Reference 23

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This paper cites Long- term underwater camera surveillance for monitoring and analysis of fish populations.

Object Recognition Datasets and Challenges: A Review Long- term underwater camera surveillance for monitoring and analysis of fish populations

Reference 24

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This paper cites Learning fuzzy concept definitions.

Object Recognition Datasets and Challenges: A Review Learning fuzzy concept definitions

Reference 25

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This paper cites AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance.

Object Recognition Datasets and Challenges: A Review AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance

Reference 26

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This paper cites The EuroCity Persons Dataset: A Novel Benchmark for Object Detection.

Object Recognition Datasets and Challenges: A Review The EuroCity Persons Dataset: A Novel Benchmark for Object Detection

Reference 27

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Object Recognition Datasets and Challenges: A Review Segmentation and Recognition using SfM Point Clouds

Reference 28

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This paper cites Segmentation and Recognition Using Structure from Motion Point Clouds , 44–57.

Object Recognition Datasets and Challenges: A Review Segmentation and Recognition Using Structure from Motion Point Clouds , 44–57

Reference 29

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This paper cites Object Segmentation by Long Term Analysis of Point Trajectories, in: Daniilidis, K., Maragos, P., Paragios, N.

Object Recognition Datasets and Challenges: A Review Object Segmentation by Long Term Analysis of Point Trajectories, in: Daniilidis, K., Maragos, P., Paragios, N

Reference 30

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This paper cites The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation.

Object Recognition Datasets and Challenges: A Review The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation

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Object Recognition Datasets and Challenges: A Review Unresolved cited work

Reference 32

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This paper cites Multi-Modality Vertebra Recognition in Arbitrary Views Using 3D Deformable Hierarchical Model.

Object Recognition Datasets and Challenges: A Review Multi-Modality Vertebra Recognition in Arbitrary Views Using 3D Deformable Hierarchical Model

Reference 33

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Object Recognition Datasets and Challenges: A Review Unresolved cited work

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This paper cites ISIC 2018.

Object Recognition Datasets and Challenges: A Review ISIC 2018

Reference 35

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Object Recognition Datasets and Challenges: A Review Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl

Reference 36

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Object Recognition Datasets and Challenges: A Review Argoverse: 3D tracking and forecasting with rich maps

Reference 37

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Object Recognition Datasets and Challenges: A Review Unresolved cited work

Reference 38

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Object Recognition Datasets and Challenges: A Review D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios

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Object Recognition Datasets and Challenges: A Review Return of the devil in the details: Delving deep into convolutional nets

Reference 40

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This paper cites Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval , 768–783.

Object Recognition Datasets and Challenges: A Review Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval , 768–783

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Object Recognition Datasets and Challenges: A Review High Performance Convolutional Neural Networks for Document Processing, in: Lorette, G

Reference 42

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Object Recognition Datasets and Challenges: A Review Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs

Reference 43

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Object Recognition Datasets and Challenges: A Review Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs

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Object Recognition Datasets and Challenges: A Review TensorMask: A Foundation for Dense Object Segmentation

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Object Recognition Datasets and Challenges: A Review Rethinking Atrous Convolution for Semantic Image Segmentation

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Object Recognition Datasets and Challenges: A Review A survey on object detection in optical remote sensing images

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Object Recognition Datasets and Challenges: A Review Unresolved cited work

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Object Recognition Datasets and Challenges: A Review Salientshape: Group saliency in image collections

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Object Recognition Datasets and Challenges: A Review Remote Sensing Image Scene Classification: Benchmark and State of the Art

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Object Recognition Datasets and Challenges: A Review Xception: Deep learning with depthwise separable convolutions

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Object Recognition Datasets and Challenges: A Review KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving

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Object Recognition Datasets and Challenges: A Review Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) , 1–12

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Object Recognition Datasets and Challenges: A Review Functional Map of the World

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Object Recognition Datasets and Challenges: A Review The Cityscapes Dataset for Semantic Urban Scene Understanding

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Object Recognition Datasets and Challenges: A Review A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset

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Object Recognition Datasets and Challenges: A Review SARAS-ESAD Dataset

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Object Recognition Datasets and Challenges: A Review SARAS-ESAD 2020

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Object Recognition Datasets and Challenges: A Review EchoNet-Dynamic Dataset

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Object Recognition Datasets and Challenges: A Review Histograms of oriented gradients for human detection

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Object Recognition Datasets and Challenges: A Review ImageNet: A large-scale hierarchical image database , 248–255doi:10.1109/cvpr.2009.5206848

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Object Recognition Datasets and Challenges: A Review DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

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Object Recognition Datasets and Challenges: A Review Automatic detection of geometrical anomalies in composites manufacturing : a deep learning-based computer vision approach

Reference 63

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Object Recognition Datasets and Challenges: A Review D2-City Detection Domain Adaptation Challenge

Reference 64

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Object Recognition Datasets and Challenges: A Review ELCAP Public Lung Image Database

Reference 65

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Object Recognition Datasets and Challenges: A Review Pedestrian detection: A benchmark, Institute of Electrical and Electronics Engineers (IEEE)

Reference 66

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Object Recognition Datasets and Challenges: A Review SpaceNet: A Remote Sensing Dataset and Challenge Series

Reference 67

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Object Recognition Datasets and Challenges: A Review Monocular pedestrian detection: Survey and experiments, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp

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Object Recognition Datasets and Challenges: A Review ”Hello! My name is

Reference 69

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Object Recognition Datasets and Challenges: A Review The Pascal Visual Object Classes Challenge: A Retrospective

Reference 70

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Object Recognition Datasets and Challenges: A Review Camouflaged Object Detection

Reference 71

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Object Recognition Datasets and Challenges: A Review The pascal visual object classes (VOC) challenge

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.339192Z

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Object Recognition Datasets and Challenges: A Review Salient objects in clutter: Bringing salient object detection to the foreground

Reference 73

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Object Recognition Datasets and Challenges: A Review Camouflaged object detection , 2774–2784doi: 10

Reference 74

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Object Recognition Datasets and Challenges: A Review Learning Generative Visual Models from Few Training Examples :

Reference 75

Resolution: verified exact; doi, observed 2026-08-06T11:51:11.111833Z

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Object Recognition Datasets and Challenges: A Review JumpCut: Non-Successive Mask Transfer and Interpolation for Video Cutout

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.347108Z

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Object Recognition Datasets and Challenges: A Review WordNet: an Electronic Lexical Database

Reference 78

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Object Recognition Datasets and Challenges: A Review Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge

Reference 79

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Object Recognition Datasets and Challenges: A Review Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.354948Z

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Object Recognition Datasets and Challenges: A Review A review on deep learning techniques applied to semantic segmentation

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.361080Z

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Object Recognition Datasets and Challenges: A Review Research and development of power grid dispatching operation control system based on transmission section control

Reference 82

Resolution: verified exact; doi, observed 2026-08-06T11:51:11.101072Z

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This paper cites The KITTI 2D Object Evaluation Benchmark.

Object Recognition Datasets and Challenges: A Review The KITTI 2D Object Evaluation Benchmark

Reference 83

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Object Recognition Datasets and Challenges: A Review Unresolved cited work

Reference 84

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Object Recognition Datasets and Challenges: A Review Vision meets robotics: The KITTI dataset

Reference 85

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Object Recognition Datasets and Challenges: A Review The KITTI 3D Object Evaluation Benchmark

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.366963Z

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Object Recognition Datasets and Challenges: A Review Fast r-cnn, in: Proceedings of the IEEE international conference on computer vision, pp

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.373676Z

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This paper cites Overview of LifeCLEF Plant identification task 2019: Diving into data deficient tropical countries, in: CEUR Workshop Proceedings, pp.

Object Recognition Datasets and Challenges: A Review Overview of LifeCLEF Plant identification task 2019: Diving into data deficient tropical countries, in: CEUR Workshop Proceedings, pp

Reference 89

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This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

Object Recognition Datasets and Challenges: A Review Rich feature hierarchies for accurate object detection and semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.375651Z

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Observation d1653b4a-3b5c-44b7-97a6-1982bb052ce7 · outbound

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Object Recognition Datasets and Challenges: A Review STARE Database

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.380297Z

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Object Recognition Datasets and Challenges: A Review MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.386301Z

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Observation 880303cb-8e24-41aa-88c4-f0313a0e2ed2 · outbound

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Object Recognition Datasets and Challenges: A Review Caltech-256 Object Category Dataset , 300

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.384276Z

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This paper cites Semantic Contours from Inverse Detectors * - Hariharan et al.pdf.

Object Recognition Datasets and Challenges: A Review Semantic Contours from Inverse Detectors * - Hariharan et al.pdf

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.390264Z

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This paper cites Lvis: A dataset for large vocabulary instance segmentation.

Object Recognition Datasets and Challenges: A Review Lvis: A dataset for large vocabulary instance segmentation

Reference 96

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Observation 58757db1-b5a4-43bf-8117-075ae43b7c39 · outbound

This paper cites Deep residual learning for image recognition, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society.

Object Recognition Datasets and Challenges: A Review Deep residual learning for image recognition, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.394525Z

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This paper cites Mask r-cnn, in: Proceedings of the IEEE international conference on computer vision, pp.

Object Recognition Datasets and Challenges: A Review Mask r-cnn, in: Proceedings of the IEEE international conference on computer vision, pp

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.392643Z

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Object Recognition Datasets and Challenges: A Review Philip Chang, K., Munishkumaran, S., 2001

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.398508Z

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Observation 1a0acb35-5626-412d-bf71-365c655dc0c7 · outbound

This paper cites Philip Chang, K., Munishkumaran, S.,.

Object Recognition Datasets and Challenges: A Review Philip Chang, K., Munishkumaran, S.,

Reference 100

Resolution: malformed identifier; no resolver link, observed 2026-08-06T11:51:10.396596Z

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Observation 707eca05-e348-4fc4-af0d-772766025230 · outbound

This paper cites The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes , 1–14.

Object Recognition Datasets and Challenges: A Review The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes , 1–14

Reference 101

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Observation ee5f0597-9f98-4a3f-9594-b8c98fd1d6ce · outbound

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Object Recognition Datasets and Challenges: A Review Learning Spatial Context: Using Stuff to Find Things

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.400565Z

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Observation 0aa378b9-fddc-4c09-995d-47082feafb5f · outbound

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Object Recognition Datasets and Challenges: A Review Reducing the dimensionality of data with neural networks

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.406497Z

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Observation 8e2748dc-92b1-4dc7-b4c8-079504bb4fab · outbound

This paper cites A fast learning algorithm for deep belief nets.

Object Recognition Datasets and Challenges: A Review A fast learning algorithm for deep belief nets

Reference 104

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.404598Z

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Observation cd3ad8a5-8483-48fa-a5c9-244f3e829bbc · outbound

This paper cites The iNaturalist Species Classification and Detection Dataset, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.

Object Recognition Datasets and Challenges: A Review The iNaturalist Species Classification and Detection Dataset, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp

Reference 105

Resolution: unresolved; no resolver link, observed 2026-08-06T11:51:10.410373Z

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