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Few-shot object detection (FSOD) aims to detect objects using only a few examples.
The pascal visual object classes (voc) challenge
Everingham, M.; Van Gool, L.; Williams, C. K.; Winn, J.; and Zisserman, A. 2010 · 2010
Earlier work this paper cites.
ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
Earlier work this paper cites.
Fast R-CNN
Girshick, R. 2015 · 2015
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What makes for effective detection proposals?
Hosang, J.; Benenson, R.; Dollár, P.; and Schiele, B. 2015 · 2015
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Siamese neural networks for one-shot image recognition
Koch, G.; Zemel, R.; and Salakhutdinov, R. 2015 · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.-Y.; and Berg, A. C. 2016 · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection
Redmon, J.; Divvala, S.; Girshick, R.; and Farhadi, A. 2016 · 2016
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Matching networks for one shot learning
Vinyals, O.; Blundell, C.; Lillicrap, T.; Wierstra, D.; et al. 2016 · 2016
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A survey of model compression and acceleration for deep neural networks
Cheng, Y.; Wang, D.; Zhou, P.; and Zhang, T. 2017 · 2017
Earlier work this paper cites.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
Earlier work this paper cites.
Single shot object detection with top-down refinement
Han, G.; Zhang, X.; and Li, C. 2017b · 2017
Earlier work this paper cites.
Mask r-cnn
He, K.; Gkioxari, G.; Dollár, P.; and Girshick, R. 2017 · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Lin, T.-Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; and Belongie, S. 2017 · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J.; Swersky, K.; and Zemel, R. 2017 · 2017
Cited alongside, same era.
Dynamic few-shot visual learning without forgetting
Gidaris, S.; and Komodakis, N. 2018 · 2018
Cited alongside, same era.
Semi-supervised DFF: Decoupling detection and feature flow for video object detectors
Han, G.; Zhang, X.; and Li, C. 2018 · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
Sung, F.; Yang, Y.; Zhang, L.; Xiang, T.; Torr, P. H.; and Hospedales, T. M. 2018 · 2018
Cited alongside, same era.
OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features
Osokin, A.; Sumin, D.; and Lomakin, V. 2020 · 2020
Later among the works it cites.
Incremental Few-Shot Object Detection
Perez-Rua, J.-M.; Zhu, X.; Hospedales, T. M.; and Xiang, T. 2020 · 2020
Later among the works it cites.
Frustratingly Simple Few-Shot Object Detection
Wang, X.; Huang, T. E.; Darrell, T.; Gonzalez, J. E.; and Yu, F. 2020 · 2020
Later among the works it cites.
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
Wu, J.; Liu, S.; Huang, D.; and Wang, Y. 2020 · 2020
Later among the works it cites.
Meta-rcnn: Meta learning for few-shot object detection
Wu, X.; Sahoo, D.; and Hoi, S. 2020 · 2020
Later among the works it cites.
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild
Xiao, Y.; and Marlet, R. 2020 · 2020
Later among the works it cites.
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One-shot object detection with co-attention and co-excitation
Hsieh, T.-I.; Lo, Y.-C.; Chen, H.-T.; and Liu, T.-L. 2019 · 2019
Cited alongside, same era.
Few-shot object detection via feature reweighting
Kang, B.; Liu, Z.; Wang, X.; Yu, F.; Feng, J.; and Darrell, T. 2019 · 2019
Cited alongside, same era.
Repmet: Representative-based metric learning for classification and few-shot object detection
Karlinsky, L.; Shtok, J.; Harary, S.; Schwartz, E.; Aides, A.; Feris, R.; Giryes, R.; and Bronstein, A. M. 2019 · 2019
Cited alongside, same era.
Fcos: Fully convolutional one-stage object detection
Tian, Z.; Shen, C.; Chen, H.; and He, T. 2019 · 2019
Cited alongside, same era.
Meta-learning to detect rare objects
Wang, Y.-X.; Ramanan, D.; and Hebert, M. 2019 · 2019
Cited alongside, same era.
Meta r-cnn: Towards general solver for instance-level low-shot learning
Yan, X.; Chen, Z.; Xu, A.; Wang, X.; Liang, X.; and Lin, L. 2019 · 2019
Cited alongside, same era.
Query Adaptive Few-Shot Object Detection With Heterogeneous Graph Convolutional Networks
Han, G.; He, Y.; Huang, S.; Ma, J.; and Chang, S.-F. 2021 · 2021
Closest in time.
Partner-Assisted Learning for Few-Shot Image Classification
Ma, J.; Xie, H.; Han, G.; Chang, S.-F.; Galstyan, A.; and Abd-Almageed, W. 2021 · 2021
Closest in time.
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
Sun, B.; Li, B.; Cai, S.; Yuan, Y.; and Zhang, C. 2021 · 2021
Closest in time.
The Met Dataset: Instance-level Recognition for Artworks
Ypsilantis, N.-A.; Garcia, N.; Han, G.; Ibrahimi, S.; Van Noord, N.; and Tolias, G. 2021 · 2021
Closest in time.
Hallucination Improves Few-Shot Object Detection
Zhang, W.; and Wang, Y.-X. 2021 · 2021
Closest in time.
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection
Zhu, C.; Chen, F.; Ahmed, U.; Shen, Z.; and Savvides, M. 2021 · 2021
Closest in time.
Task-Adaptive Negative Class Envision for Few-Shot Open-Set Recognition
Huang, S.; Ma, J.; Han, G.; and Chang, S.-F. 2022 · 2022
Closest in time.