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Data is the cornerstone of deep learning.
MMDetection: Open MMLab Detection Toolbox and Benchmark
Chen, K.; Wang, J.; Pang, J.; Cao, Y.; Xiong, Y.; Li, X.; Sun, S.; Feng, W.; Liu, Z.; Xu, J.; Zhang, Z.; Cheng, D.; Zhu, C.; Cheng, T.; Zhao, Q.; Li, B.; Lu, X.; Zhu, R.; Wu, Y.; Dai, J.; Wang, J.; Shi, J.; Ouyang, W.; Loy, C. C.; and Lin, D. 2019 · 1906
Earlier work this paper cites.
Data augmentation for object detection via progressive and selective instance-switching
Wang, H.; Wang, Q.; Yang, F.; Zhang, W.; and Zuo, W. 2019 · 1906
Earlier work this paper cites.
A threshold selection method from gray-level histograms
Otsu, N. 1979 · 1979
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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
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Fast r-cnn
Girshick, R. 2015 · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Ssd: Single shot multibox detector
Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.-Y.; and Berg, A. C. 2016 · 2016
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You only look once: Unified, real-time object detection
Redmon, J.; Divvala, S.; Girshick, R.; and Farhadi, A. 2016 · 2016
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Mask r-cnn
He, K.; Gkioxari, G.; Dollár, P.; and Girshick, R. 2017 · 2017
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Generative adversarial networks: An overview
Creswell, A.; White, T.; Dumoulin, V.; Arulkumaran, K.; Sengupta, B.; and Bharath, A. A. 2018 · 2018
Cited alongside, same era.
Cross-domain weakly-supervised object detection through progressive domain adaptation
Inoue, N.; Furuta, R.; Yamasaki, T.; and Aizawa, K. 2018 · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P.; and Dhariwal, P. 2018 · 2018
Cited alongside, same era.
Cascade R-CNN: high quality object detection and instance segmentation
Cai, Z.; and Vasconcelos, N. 2019 · 2019
Cited alongside, same era.
Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 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.
Probabilistic two-stage detection
Zhou, X.; Koltun, V.; and Krähenbühl, P. 2021 · 2021
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Deformable DETR: Deformable Transformers for End-to-End Object Detection
Zhu, X.; Su, W.; Lu, L.; Li, B.; Wang, X.; and Dai, J. 2021 · 2021
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Dall-e for detection: Language-driven context image synthesis for object detection
Ge, Y.; Xu, J.; Zhao, B. N.; Itti, L.; and Vineet, V. 2022 · 2022
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Cascaded Diffusion Models for High Fidelity Image Generation
Ho, J.; Saharia, C.; Chan, W.; Fleet, D. J.; Norouzi, M.; and Salimans, T. 2022 · 2022
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Cross-Domain Adaptive Teacher for Object Detection
Li, Y.-J.; Dai, X.; Ma, C.-Y.; Liu, Y.-C.; Chen, K.; Wu, B.; He, Z.; Kitani, K.; and Vajda, P. 2022 · 2022
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Detectron2
Wu, Y.; Kirillov, A.; Massa, F.; Lo, W.-Y.; and Girshick, R. 2019 · 2019
Cited alongside, same era.
End-to-end object detection with transformers
Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; and Zagoruyko, S. 2020 · 2020
Cited alongside, same era.
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
Grill, J.; Strub, F.; Altché, F.; Tallec, C.; Richemond, P. H.; Buchatskaya, E.; Doersch, C.; Pires, B. Á.; Guo, Z.; Azar, M. G.; Piot, B.; Kavukcuoglu, K.; Munos, R.; and Valko, M. 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Cited alongside, same era.
Learning data augmentation strategies for object detection
Zoph, B.; Cubuk, E. D.; Ghiasi, G.; Lin, T.-Y.; Shlens, J.; and Le, Q. V. 2020 · 2020
Cited alongside, same era.
Scale-aware automatic augmentation for object detection
Chen, Y.; Li, Y.; Kong, T.; Qi, L.; Chu, R.; Li, L.; and Jia, J. 2021 · 2021
Cited alongside, same era.
Swin Transformer V2: Scaling Up Capacity and Resolution
Liu, Z.; Hu, H.; Lin, Y.; Yao, Z.; Xie, Z.; Wei, Y.; Ning, J.; Cao, Y.; Zhang, Z.; Dong, L.; Wei, F.; and Guo, B. 2022 · 2022
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DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
Lu, C.; Zhou, Y.; Bao, F.; Chen, J.; Li, C.; and Zhu, J. 2022 · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
Later among the works it cites.
Palette: Image-to-image diffusion models
Saharia, C.; Chan, W.; Chang, H.; Lee, C.; Ho, J.; Salimans, T.; Fleet, D.; and Norouzi, M. 2022a · 2022
Later among the works it cites.
Dino: Detr with improved denoising anchor boxes for end-to-end object detection
Zhang, H.; Li, F.; Liu, S.; Zhang, L.; Su, H.; Zhu, J.; Ni, L.; and Shum, H. 2022 · 2022
Later among the works it cites.
Gligen: Open-set grounded text-to-image generation
Li, Y.; Liu, H.; Wu, Q.; Mu, F.; Yang, J.; Gao, J.; Li, C.; and Lee, Y. J. 2023 · 2023
Closest in time.
Reco: Region-controlled text-to-image generation
Yang, Z.; Wang, J.; Gan, Z.; Li, L.; Lin, K.; Wu, C.; Duan, N.; Liu, Z.; Liu, C.; Zeng, M.; et al. 2023 · 2023
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X-Paste: Revisit Copy-Paste at Scale with CLIP and StableDiffusion
Zhao, H.; Sheng, D.; Bao, J.; Chen, D.; Chen, D.; Wen, F.; Yuan, L.; Liu, C.; Zhou, W.; Chu, Q.; et al. 2023 · 2023
Closest in time.