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We develop a probabilistic interpretation of two-stage object detection.
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MMDetection: Open mmlab detection toolbox and benchmark
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Microsoft COCO: Common objects in context
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Zitnick, C. L. and Dollár, P · 2014
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Ren, S., He, K., Girshick, R., and Sun, J · 2015
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He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C · 2016
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Stacked hourglass networks for human pose estimation
Newell, A., Yang, K., and Deng, J · 2016
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Relay backpropagation for effective learning of deep convolutional neural networks
Shen, L., Lin, Z., and Huang, Q · 2016
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Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
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Yolo9000: better, faster, stronger
Redmon, J. and Farhadi, A · 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
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K · 2017
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Cascade r-cnn: Delving into high quality object detection
Cai, Z. and Vasconcelos, N · 2018
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Cornernet: Detecting objects as paired keypoints
Law, H. and Deng, J · 2018
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Freeanchor: Learning to match anchors for visual object detection
Zhang, X., Wan, F., Liu, C., Ji, R., and Ye, Q · 2019
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Yolov4: Optimal speed and accuracy of object detection
Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y. M · 2020
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End-to-end object detection with transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S · 2020
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Reppoints v2: Verification meets regression for object detection
Chen, Y., Zhang, Z., Cao, Y., Wang, L., Lin, S., and Hu, H · 2020
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Centripetalnet: Pursuing high-quality keypoint pairs for object detection
Dong, Z., Li, G., Liao, Y., Wang, F., Ren, P., and Qian, C · 2020
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Redmon, J. and Farhadi, A · 2018
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Deep layer aggregation
Yu, F., Wang, D., Shelhamer, E., and Darrell, T · 2018
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Centernet: Object detection with keypoint triplets
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q · 2019
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LVIS: A dataset for large vocabulary instance segmentation
Gupta, A., Dollar, P., and Girshick, R · 2019
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Scale-aware trident networks for object detection
Li, Y., Chen, Y., Wang, N., and Zhang, Z · 2019
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Generalized intersection over union: A metric and a loss for bounding box regression
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S · 2019
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Objects365: A large-scale, high-quality dataset for object detection
Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., and Sun, J · 2019
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Duan, K., Xie, L., Qi, H., Bai, S., Huang, Q., and Tian, Q · 2020
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Multiple anchor learning for visual object detection
Ke, W., Zhang, T., Huang, Z., Ye, Q., Liu, J., and Huang, D · 2020
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Probabilistic anchor assignment with iou prediction for object detection
Kim, K. and Lee, H. S · 2020
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Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution
Qiao, S., Chen, L.-C., and Yuille, A · 2020
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Borderdet: Border feature for dense object detection
Qiu, H., Ma, Y., Li, Z., Liu, S., and Sun, J · 2020
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Revisiting the sibling head in object detector
Song, G., Liu, Y., and Wang, X · 2020
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Scalability in perception for autonomous driving: An open dataset benchmark
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Fcos: A simple and strong anchor-free object detector
Tian, Z., Shen, C., Chen, H., and He, T · 2020
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Scaled-yolov4: Scaling cross stage partial network
Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y. M · 2020
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efficientdet-pytorch
Wightman, R · 2020
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Dense reppoints: Representing visual objects with dense point sets
Yang, Z., Xu, Y., Xue, H., Zhang, Z., Urtasun, R., Wang, L., Lin, S., and Hu, H · 2020
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Rethinking pre-training and self-training
Zoph, B., Ghiasi, G., Lin, T.-Y., Cui, Y., Liu, H., Cubuk, E. D., and Le, Q. V · 2020
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