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In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications.
E. Xie, P. Sun, X. Song, W. Wang, X. Liu, D. Liang, C. Shen, and P. Luo, “PolarMask: Single shot instance segmentation with polar representation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn · 1909
Earlier work this paper cites.
P. Viola and M. Jones, “Robust real-time object detection,” Int. J. Comp. Vis
2001
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2009
Earlier work this paper cites.
C. Shen, P. Wang, S. Paisitkriangkrai, and A. van den Hengel, “Training effective node classifiers for cascade classification,” Int. J. Comp. Vis
2013
Earlier work this paper cites.
P. Dollár, R. Appel, S. Belongie, and P. Perona, “Fast feature pyramids for object detection,” IEEE Trans. Pattern Anal. Mach. Intell
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and L. Zitnick, “Microsoft COCO: Common objects in context,” in Proc. Eur. Conf. Comp. Vis
2014
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Proc. Adv. Neural Inf. Process. Syst
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
R. Girshick, “Fast R-CNN,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2015
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in Proc. Eur. Conf. Comp. Vis
2016
Earlier work this paper cites.
F. Liu, C. Shen, G. Lin, and I. Reid, “Learning depth from single monocular images using deep convolutional neural fields,” IEEE Trans. Pattern Anal. Mach. Intell
2016
Earlier work this paper cites.
J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang, “Unitbox: An advanced object detection network,” in Proc. ACM Int. Conf. Multimedia
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2016
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Earlier work this paper cites.
Y. Chen, C. Shen, X.-S. Wei, L. Liu, and J. Yang, “Adversarial PoseNet: A structure-aware convolutional network for human pose estimation,” in Proc. IEEE Int. Conf. Comp. Vis
2017
Earlier work this paper cites.
X. Zhou, C. Yao, H. Wen, Y. Wang, S. Zhou, W. He, and J. Liang, “EAST: an efficient and accurate scene text detector,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Earlier work this paper cites.
J. Redmon and A. Farhadi, “YOLO9000: better, faster, stronger,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Earlier work this paper cites.
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and K. Murphy, “Speed/accuracy trade-offs for modern convolutional object detectors,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Earlier work this paper cites.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” in Proc. AAAI Conf. Artificial Intell
2017
Cited alongside, same era.
A. Shrivastava, R. Sukthankar, J. Malik, and A. Gupta, “Beyond skip connections: Top-down modulation for object detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proc. IEEE Int. Conf. Comp. Vis
2017
Cited alongside, same era.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767
Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick, “Detectron2.” https://github.com/facebookresearch/detectron2 , 2019
2019
Later among the works it cites.
K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, “CenterNet: Keypoint triplets for object detection,” in Proc. IEEE Int. Conf. Comp. Vis
2019
Later among the works it cites.
X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Liu, C. Shun, J. Wang, and C. Shen, “Structured knowledge distillation for dense prediction,” IEEE Trans. Pattern Anal. Mach. Intell
2020
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2018
Cited alongside, same era.
T. He, Z. Tian, W. Huang, C. Shen, Y. Qiao, and C. Sun, “An end-to-end textspotter with explicit alignment and attention,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2018
Cited alongside, same era.
H. Law and J. Deng, “Cornernet: Detecting objects as paired keypoints,” in Proc. Eur. Conf. Comp. Vis
2018
Cited alongside, same era.
R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár, and K. He, “Detectron.” https://github.com/facebookresearch/detectron , 2018
2018
Cited alongside, same era.
Y. Wu and K. He, “Group normalization,” in Proc. Eur. Conf. Comp. Vis
2018
Cited alongside, same era.
F. Yu, D. Wang, E. Shelhamer, and T. Darrell, “Deep layer aggregation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Z. Tian, T. He, C. Shen, and Y. Yan, “Decoders matter for semantic segmentation: Data-dependent decoding enables flexible feature aggregation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2019
Cited alongside, same era.
Closest in time.
R. Zhang, Z. Tian, C. Shen, M. You, and Y. Yan, “Mask encoding for single shot instance segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Closest in time.
Y. Lee and J. Park, “CenterMask: Real-time anchor-free instance segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Closest in time.
H. Chen, K. Sun, Z. Tian, C. Shen, Y. Huang, and Y. Yan, “BlendMask: Top-down meets bottom-up for instance segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Closest in time.
Y. Liu, H. Chen, C. Shen, T. He, L. Jin, and L. Wang, “ABCNet: Real-time scene text spotting with adaptive Bezier-curve network,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Closest in time.
G. Wang, C. Luo, X. Sun, Z. Xiong, and W. Zeng, “Tracking by instance detection: A meta-learning approach,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Closest in time.
D. Guo, J. Wang, Y. Cui, Z. Wang, and S. Chen, “SiamCAR: Siamese fully convolutional classification and regression for visual tracking,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Closest in time.
K. Duan, L. Xie, H. Qi, S. Bai, Q. Huang, and Q. Tian, “Corner proposal network for anchor-free, two-stage object detection,” in Proc. Eur. Conf. Comp. Vis
2020
Closest in time.
N. Samet, S. Hicsonmez, and E. Akbas, “HoughNet: Integrating near and long-range evidence for bottom-up object detection,” in Proc. Eur. Conf. Comp. Vis
2020
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H. Qiu, Y. Ma, Z. Li, S. Liu, and J. Sun, “BorderDet: Border feature for dense object detection,” in Proc. Eur. Conf. Comp. Vis
2020
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X. Li, W. Wang, L. Wu, S. Chen, X. Hu, J. Li, J. Tang, and J. Yang, “Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection,” in Proc. Adv. Neural Inf. Process. Syst
2020
Closest in time.
S. Zhang, C. Chi, Y. Yao, Z. Lei, and S. Z. Li, “Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Closest in time.
2020
Closest in time.
Z. Tian, C. Shen, and H. Chen, “Conditional convolutions for instance segmentation,” in Proc. Eur. Conf. Computer Vision (ECCV)
2020
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M. Tan, R. Pang, and Q. Le, “EfficientDet: Scalable and efficient object detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Closest in time.
X. Chu, A. Zheng, X. Zhang, and J. Sun, “Detection in crowded scenes: One proposal, multiple predictions,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Closest in time.