Do We Need More Training Data or Better Models for Object Detection?

Cited by (179)

Items 101–179 of 179

1. Inverting and Visualizing Features for Object Detection
   Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, Antonio B. Torralba · 2012 · arXiv (Cornell University)
2. Decision support for the efficient annotation of bioacoustic events
   Anthony Truskinger, Michael Towsey, Paul Roe · 2014 · Ecological Informatics
3. Automatic Sensor Data Stream Segmentation for Real-time Activity Prediction in Smart Spaces
   Hyunjeong Cho, Jihoon An, Intaek Hong · 2015
4. Looking Inside Category: Subcategory-Aware Object Recognition
   Jian Dong, Qiang Chen, Jiashi Feng · 2014 · IEEE Transactions on Circuits and Systems for Video Technology
5. Phrasal Recognition
   Ali Farhadi, Mohammad Amin Sadeghi · 2013 · IEEE Transactions on Pattern Analysis and Machine Intelligence
6. Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
   Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James H. Thrall, Quanzheng Li · 2017 · Lecture notes in computer science
7. Context Forest for Object Class Detection
   Davide Modolo, Alexander Vezhnevets, Vittorio Ferrari · 2015 · British Machine Vision Conference (BMVC)
8. When User Interest Meets Data Quality: A Novel User Filter Scheme for Mobile Crowd Sensing
   Wensheng Li, Fan Li, Kashif Sharif · 2017 · IEEE International Conference on Parallel and Distributed Systems (ICPADS)
9. Learning Discriminative Collections of Part Detectors for Object Recognition
   Kevin J. Shih, Ian Endres, Derek Hoiem · 2014 · IEEE Transactions on Pattern Analysis and Machine Intelligence
10. Count on Me: Learning to Count on a Single Image
    Francesco Setti, Davide Conigliaro, Michele Tobanelli · 2017 · IEEE Transactions on Circuits and Systems for Video Technology
11. Learning with dataset bias in latent subcategory models
    Dimitris Stamos, Samuele Martelli, Moin Nabi · 2015 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
12. A MapReduce framework to improve template matching uncertainty
    Nicholas J. Napoli, Kevin Leach, Laura E. Barnes · 2016 · International Conference on Big Data and Smart Computing
13. Exploitable synthetic sensor imagery from high-fidelity, physics-based target and background modeling
    Stacy E. Howington, Jerrell R. Ballard, Raju V. Kala · 2019
14. Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging
    Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lü, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel J. Mollura, Ronald M. Summers · 2017 · Advances in computer vision and pattern recognition
15. Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors
    Harith Al-Sahaf, Mengjie Zhang, Ausama Al-Sahaf · 2020
16. Improved Boosting Performance by Explicit Handling of Ambiguous Positive Examples
    Miroslav Kobetski, Josephine Sullivan · 2014 · Advances in intelligent systems and computing
17. Structured Prediction for Object Detection in Deep Neural Networks
    Hannes Schulz, Sven Behnke · 2014 · Lecture notes in computer science
18. Pedestrian detection based on sparse coding and transfer learning
    Feidie Liang, Sheng Tang, Yongdong Zhang, Zuo-Xin Xu, Jintao Li · 2013 · Machine Vision and Applications
19. Peer Review #1 of "Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (v0.1)"
    2018 · PeerJ
20. SAIS: Self-Adaptive Identification of Security Bug Reports
    Shaikh Mostafa, Bridgette Findley, Na Meng · 2019 · IEEE Transactions on Dependable and Secure Computing
21. Backpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology Data
    Soo See Chai, Wei Keat Wong, Kok Luong Goh · 2016 · Journal of Computer Science
22. A Scalable Approach for Facial Action Unit Classifier Training UsingNoisy Data for Pre-Training
    Alberto Fung, Daniel McDuff · 2019 · arXiv (Cornell University)
23. Bayesian Detector Combination for Object Detection with Crowdsourced Annotations
    Zhi Qin Tan, Olga Isupova, Gustavo Carneiro, Xiatian Zhu, Yunpeng Li · 2024 · Lecture notes in computer science
24. Towards Explainable Visual Vessel Recognition Using Fine-Grained Classification and Image Retrieval
    Heiko Karus, Friedhelm Schwenker, Michael Münz, Michael Teutsch · 2024 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
25. Subcategory Clustering with Latent Feature Alignment and Filtering for Object Detection
    Zhiwei Ruan, Guijin Wang, Jing‐Hao Xue · 2014 · IEEE Signal Processing Letters
26. Properties of Datasets Predict the Performance of Classifiers
    Omid Aghazadeh, Stefan Carlsson · 2013 · British Machine Vision Conference (BMVC)
27. Core dataset extraction from unlabeled medical big data for lesion localization
    Kehua Guo, Yifei Wang, Jian Kang · 2021 · Big Data Research
28. KIT MOMA: A Mobile Machines Dataset
    Yusheng Xiang, Wang, Hongzhe, Tianqing Su, Ruoyu Li, Christine Brach, Samuel S. Mao, Marcus Geimer · 2020 · arXiv (Cornell University)
29. Facial landmarking for in-the-wild images with local inference based on global appearance
    Brais Martínez, Maja Pantić · 2015 · Image and Vision Computing
30. Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter
    Jing Sun, Yimu Ji, Shangdong Liu · 2023 · Electronics
31. Ensembles of Correlation Filters for Object Detection
    Ryan Tokola, David S. Bolme · 2015 · IEEE Winter Conference on Applications of Computer Vision
32. Scene-specific pedestrian detection based on transfer learning and saliency detection for video surveillance
    Weiwei Xing, Pingping Bai, Shunli Zhang, Peng Bao · 2017 · Automatic Control and Computer Sciences
33. Zero-Shot Unseen Speaker Anonymization via Voice Conversion
    Hyung-Pil Chang, In-Chul Yoo, Changhyeon Jeong, Dongsuk Yook · 2022 · IEEE Access
34. Weak supervision for detecting object classes from activities
    Abhilash Srikantha, Jüergen Gall · 2016 · Computer Vision and Image Understanding
35. Factorized appearances for object detection
    Josep M. Gonfaus, Marco Pedersoli, Jordi Gonzàlez · 2015 · Computer Vision and Image Understanding
36. Extracting Temporal and Causal Relations between Events
    Paramita Mirza · 2016 · arXiv (Cornell University)
37. HealthCam: Machine Learning Models on Mobile Devices for Unhealthy Packaged Food Detection and Classification
    David Cantillo, Brandon Cervantes, Jairo Alberto Cardona Peña · 2021 · IEEE International Conference on E-health Networking, Application & Services (HealthCom)
38. Low-Cost Object Detection Models for Traffic Control Devices through Domain Adaption of Geographical Regions
    Dahyun Oh, Kyubyung Kang, Sungchul Seo, Jinwu Xiao, Kyochul Jang, Kibum Kim, Hyungkeun Park, Jeong-Hun Won · 2023 · Remote Sensing
39. Simple Real-Time Pattern Recognition for Industrial Automation
    Vikram Voleti, Prakhar Mohan, Saket Kumar Gupta · 2017
40. High-level spatial modeling in convolutional neural network with application to pedestrian detection
    Feng Liu, Yongzhen Huang, Wankou Yang · 2015 · IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)
41. When a Classifier Meets More Data
    Zhicheng Liao, Yangyong Zhu · 2014 · Procedia Computer Science
42. Machine-learning methods in the classification of water bodies
    Marek Sołtysiak, Marcin Blachnik, Dominika Dąbrowska · 2016 · Environmental & Socio-economic Studies
43. Humans Disagree With the IoU for Measuring Object Detector Localization Error
    Ombretta Strafforello, Vanathi Rajasekart, Osman Semih Kayhan · 2022 · Proceedings - International Conference on Image Processing
44. Decomposition and Extraction: A New Framework for Visual Classification
    Yuqiang Fang, Qiang Ke Chen, Lin Sun · 2014 · IEEE Transactions on Image Processing
45. Sign Recognition - How well does Single Shot Multibox Detector sum up? A Quantitative Study
    R. Manikandan · 2018 · IEEE Applied Imagery Pattern Recognition Workshop
46. Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation
    Mateusz Malarczyk, Jules‐Raymond Tapamo, Marcin Kamiński · 2022 · Energies
47. Learning to Detect Vehicles by Clustering Appearance Patterns
    Eshed Ohn-Bar, Mohan Manubhai Trivedi · 2015 · arXiv (Cornell University)
48. Concealed Objects in a Scene: A Closer Look at the Localization Block of Object Detection Models
    Jinky G. Marcelo, Gian Marco I. Te, Macario O. Cordel · 2021 · Proceedings - International Conference on Tools with Artificial Intelligence, TAI
49. Exemplar Network: A Generalized Mixture Model
    Chikao Tsuchiya, Tomasz Malisiewicz, Antonio B. Torralba · 2014 · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition
50. Leveraging external resources for offensive content detection in social media
    György Kovács, Pedro Alonso, Rajkumar Saini, Marcus Liwicki · 2022 · AI Communications
51. A Dataset for Object Detection in Night Rainy Scenes
    Haoyuan Zhang, Tingtao Liu, Zhenyu Wu, Danwei Wang · 2023 · IEEE Conference on Cybernetics and Intelligent Systems
52. Crowd Teaches the Machine: Reducing Cost of Crowd-Based Training of Machine Classifiers
    Radosław Nielek, Filip Georgiew, Adam Wierzbicki · 2016 · Lecture notes in computer science
53. Development of vegetation mapping with deep convolutional neural network
    Sae-han Suh, Ji-eun Jhang, Kwanghee Won, Sung-Y. Shin, Chang Oan Sung · 2018 · ACM Conference on Research in Adaptive and Convergent Systems (RACS)
54. Seeding Deep Learning using Wireless Localization.
    Zhujun Xiao, Yanzi Zhu, Yuxin Chen · 2018 · arXiv (Cornell University)
55. Reaching the Ceiling? Empirical Scaling Behaviour for Deep Eeg Pathology Decoding
    Ann-Kathrin Kiessner, Robin Tibor Schirrmeister, Joschka Boedecker, Tonio Ball A · 2023 · SSRN Electronic Journal
56. Incorporating Part Appearances Symmetry for Human Pose Estimation
    Xiangbin Shi, Meng Wang, Qin Dai · 2014 · International Conference on Virtual Reality and Visualization
57. Object Recognition Using Digitally Generated Images as Training Data
    Anton Ericson · 2013 · KTH Publication Database DiVA (KTH Royal Institute of Technology)
58. Expanding the breadth and detail of object recognition
    Ian Endres · 2013
59. New descriptor for object detection using an improved ensemble-based technique
    Amin Mohamed Ahsan · 2015
60. Active Collaborative Ensemble Tracking
    Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba · 2017 · arXiv (Cornell University)
61. Analysing and detecting twitter spam
    Chao Chen · 2016
62. Challenging Images For Minds and Machines
    Amir Rosenfeld, John K. Tsotsos · 2018 · arXiv (Cornell University)
63. Active Collaboration of Classifiers for Visual Tracking
    Kourosh Meshgi, Shigeyuki Oba · 2018 · InTech eBooks
64. Addressing Training Bias via Automated Image Annotation
    Zhujun Xiao, Yanzi Zhu, Yuxin Chen · 2018 · arXiv (Cornell University)
65. How much data for facial action unit detection?
    Jeffrey M. Girard, Jeffrey F. Cohn, László A. Jeni · 2018
66. Active collaborative ensemble tracking
    Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba · 2017 · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
67. Empirical Upper Bound, Error Diagnosis and Invariance Analysis of Modern Object Detectors
    Ali Borji · 2020 · arXiv (Cornell University)
68. Classification of Artificial and Real Objects Using Faster Region-Based Convolutional Neural Networks
    Ritvik Sai Teegavarapu, Debojit Biswas · 2021 · IEEE Symposium Series on Computational Intelligence (SSCI)
69. Peer Review #2 of "Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (v0.2)"
    2018 · PeerJ
70. Peer Review #2 of "Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (v0.1)"
    2018 · PeerJ
71. Peer Review #1 of "Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (v0.2)"
    2018 · PeerJ
72. Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors
    Harith Al-Sahaf, Mengjie Zhang, Ausama Al-Sahaf · 2020
73. TextUnet: Text Segmentation Using U-net
    Awritrojit Banerjee, Ranjit Ghoshal, Arijit Ghosal · 2022 · Lecture notes in networks and systems
74. Gauging the difficulty of image segmentation
    Marek Franaszek · 2022
75. Aligning Object Detector Bounding Boxes with Human Preference
    Ombretta Strafforello, Osman Semih Kayhan, Oana Inel · 2025 · Lecture notes in computer science
76. Boosting Deformable Part Model by Sample Sharing and Outlier Ablation
    Feng Liu, Yongzhen Huang, Liang Wang · 2013 · Lecture notes in computer science
77. Artificial Intelligence-Aided Detection of Breast Cancer Using Elastography: A Meta-Analysis of Diagnostic Test Accuracy
    Ibrahim Elmakaty, Ruba Abdo, Amr Ouda, Mohamed Elmarasi, Mohamed Elahtem, Yaman A. Khamis, Mohammed Imad Malki · 2026 · AI
78. Mid-level representations for modeling objects
    Stavros Tsogkas · 2016
79. Multi-Task Multi-Sample Learning
    Yusuf Aytar, Andrew Zisserman · 2015 · Lecture notes in computer science