Fetching the paper…
Reading the bibliography…
Synthetic images are one of the most promising solutions to avoid high costs associated with generating annotated datasets to train supervised convolutional neural networks (CNN).
Bottom-up Object Detection by Grouping Extreme and Center Points
Zhou, X., Zhuo, J., and Krähenbühl, P. (2019b) · 1901
Earlier work this paper cites.
FCOS: Fully Convolutional One-Stage Object Detection
Tian, Z., Shen, C., Chen, H., and He, T. (2019) · 1904
Earlier work this paper cites.
Zhou, X., Wang, D., and Krähenbühl, P. (2019a) · 1904
Earlier work this paper cites.
Domain Adaptation for Semantic Segmentation with Maximum Squares Loss
Chen, M., Xue, H., and Cai, D. (2019) · 1909
Earlier work this paper cites.
Deep Domain Adaptive Object Detection: a Survey
Li, W., Li, F., Luo, Y., and Wang, P. (2020) · 2002
Earlier work this paper cites.
Corner Proposal Network for Anchor-free, Two-stage Object Detection
Duan, K., Xie, L., Qi, H., Bai, S., Huang, Q., and Tian, Q. (2020a) · 2007
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.
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2015) · 2015
Earlier work this paper cites.
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Dai, J., Li, Y., He, K., and Sun, J. (2016) · 2016
Earlier work this paper cites.
SceneNN: A Scene Meshes Dataset with aNNotations
Hua, B.-S., Pham, Q.-H., Nguyen, D. T., Tran, M.-K., Yu, L.-F., and Yeung, S.-K. (2016) · 2016
Cited alongside, same era.
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016) · 2016
Cited alongside, same era.
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., and Deng, J. (2016) · 2016
Cited alongside, same era.
YOLO9000: Better, Faster, Stronger
Redmon, J. and Farhadi, A. (2016) · 2016
Cited alongside, same era.
Playing for Data: Ground Truth from Computer Games
Richter, S. R., Vineet, V., Roth, S., and Koltun, V. (2016) · 2016
Cited alongside, same era.
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., and Sun, J. (2017) · 2017
Later among the works it cites.
Learning from Synthetic Humans
Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M. J., Laptev, I., and Schmid, C. (2017) · 2017
Later among the works it cites.
CenterNet: Keypoint Triplets for Object Detection
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (2019) · 2019
Later among the works it cites.
Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
Vu, T.-H., Jain, H., Bucher, M., Cord, M., and Pérez, P. (2019) · 2019
Later among the works it cites.
Yu, F., Wang, D., Shelhamer, E., and Darrell, T. (2019) · 2019
Later among the works it cites.
RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A. M. (2016) · 2016
Cited alongside, same era.
DSSD : Deconvolutional Single Shot Detector
Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A. C. (2017) · 2017
Cited alongside, same era.
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017) · 2017
Cited alongside, same era.
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?
McCormac, J., Handa, A., Leutenegger, S., and Davison, A. J. (2017) · 2017
Cited alongside, same era.
Duan, Z., Ozan Tezcan, M., Nakamura, H., Ishwar, P., and Konrad, J. (2020b) · 2020
Closest in time.
FoveaBox: Beyound Anchor-Based Object Detection
Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., and Shi, J. (2020) · 2020
Closest in time.
CornerNet: Detecting Objects as Paired Keypoints
Law, H. and Deng, J. (2020) · 2020
Closest in time.
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning
Scheck, T., Seidel, R., and Hirtz, G. (2020) · 2020
Closest in time.