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In this paper, we aim to study how to build a strong instance segmenter with minimal training time and GPUs, as opposed to the majority of current approaches that pursue more accurate instance segmenter by building more advanced frameworks at the cost of longer training time and higher GPU requirements.
Rich feature hierarchies for accurate object detection and semantic segmentation
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S. Ren, K. He, R. Girshick, and J. Sun · 2015
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
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Cascade r-cnn: Delving into high quality object detection
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Cascade r-cnn: high quality object detection and instance segmentation
Z. Cai and N. Vasconcelos · 2019
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Hybrid task cascade for instance segmentation
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Mask scoring r-cnn
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Objects365: A large-scale, high-quality dataset for object detection
S. Shao, Z. Li, T. Zhang, C. Peng, G. Yu, X. Zhang, J. Li, and J. Sun · 2019
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End-to-end object detection with transformers
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Solov2: Dynamic and fast instance segmentation
X. Wang, R. Zhang, T. Kong, L. Li, and C. Shen · 2020
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Deformable detr: Deformable transformers for end-to-end object detection
X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai · 2020
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Masked-attention mask transformer for universal image segmentation
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar · 2021
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Per-pixel classification is not all you need for semantic segmentation
B. Cheng, A. Schwing, and A. Kirillov · 2021
Cited alongside, same era.
Solq: Segmenting objects by learning queries
B. Dong, F. Zeng, T. Wang, X. Zhang, and Y. Wei · 2021
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Instances as queries
Y. Fang, S. Yang, X. Wang, Y. Li, C. Fang, Y. Shan, B. Feng, and W. Liu · 2021
Cited alongside, same era.
Rankseg: Adaptive pixel classification with image category ranking for segmentation
H. He, Y. Yuan, X. Yue, and H. Hu · 2022
Later among the works it cites.
D. Jia, Y. Yuan, H. He, X. Wu, H. Yu, W. Lin, L. Sun, C. Zhang, and H. Hu · 2022
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Mask dino: Towards a unified transformer-based framework for object detection and segmentation
F. Li, H. Zhang, S. Liu, L. Zhang, L. M. Ni, H.-Y. Shum, et al · 2022
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Expediting large-scale vision transformer for dense prediction without fine-tuning
W. Liang, Y. Yuan, H. Ding, X. Luo, W. Lin, D. Jia, Z. Zhang, C. Zhang, and H. Hu · 2022
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A convnet for the 2020s
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie · 2022
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Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo · 2021
Cited alongside, same era.
Segmenter: Transformer for semantic segmentation
R. Strudel, R. Garcia, I. Laptev, and C. Schmid · 2021
Cited alongside, same era.
Sparse r-cnn: End-to-end object detection with learnable proposals
P. Sun, R. Zhang, Y. Jiang, T. Kong, C. Xu, W. Zhan, M. Tomizuka, L. Li, Z. Yuan, C. Wang, et al · 2021
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K-net: Towards unified image segmentation
W. Zhang, J. Pang, K. Chen, and C. C. Loy · 2021
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Vision transformer adapter for dense predictions
Z. Chen, Y. Duan, W. Wang, J. He, T. Lu, J. Dai, and Y. Qiao · 2022
Cited alongside, same era.
Masked-attention mask transformer for universal image segmentation
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar · 2022
Cited alongside, same era.
Pointly-supervised instance segmentation
B. Cheng, O. Parkhi, and A. Kirillov · 2022
Cited alongside, same era.
J. Schult, F. Engelmann, A. Hermans, O. Litany, S. Tang, and B. Leibe · 2022
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Superpoint transformer for 3d scene instance segmentation
J. Sun, C. Qing, J. Tan, and X. Xu · 2022
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Internimage: Exploring large-scale vision foundation models with deformable convolutions
W. Wang, J. Dai, Z. Chen, Z. Huang, Z. Li, X. Zhu, X. Hu, T. Lu, L. Lu, H. Li, et al · 2022
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Soit: Segmenting objects with instance-aware transformers
X. Yu, D. Shi, X. Wei, Y. Ren, T. Ye, and W. Tan · 2022
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Dino: Detr with improved denoising anchor boxes for end-to-end object detection
H. Zhang, F. Li, S. Liu, L. Zhang, H. Su, J. Zhu, L. M. Ni, and H.-Y. Shum · 2022
Later among the works it cites.
Detr doesn’t need multi-scale or locality design, 2023
Y. Lin, Y. Yuan, Z. Zhang, C. Li, N. Zheng, and H. Hu · 2023
Closest in time.
Revisiting detr pre-training for object detection
Y. Ma, W. Liang, Y. Hao, B. Chen, X. Yue, C. Zhang, and Y. Yuan · 2023
Closest in time.