Fetching the paper…
Reading the bibliography…
Transformers have been widely used in numerous vision problems especially for visual recognition and detection.
N. Ahmed, T. Natarajan, and K. R. Rao, “Discrete cosine transform,” IEEE transactions on Computers , vol. 100, no. 1, pp. 90–93, 1974
1974
Earlier work this paper cites.
C. Papageorgiou and T. Poggio, “A trainable system for object detection,” International Journal of Computer Vision , vol. 38, no. 1, pp. 15–33, 2000
2000
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Simultaneous detection and segmentation,” in ECCV , 2014
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 NeurIPS , 2015
2015
Earlier work this paper cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-based convolutional networks for accurate object detection and segmentation,” IEEE transactions on pattern analysis and machine intelligence , vol. 38, no. 1, pp. 142–158, 2015
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 ECCV , 2016, pp. 21–37
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 CVPR , 2016, pp. 779–788
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS , 2017
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in ICCV , 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in CVPR , 2017
2017
Earlier work this paper cites.
Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick, “Detectron2,” https://github.com/facebookresearch/detectron2 , 2019
2019
Earlier work this paper cites.
H. Hu, Z. Zhang, Z. Xie, and S. Lin, “Local relation networks for image recognition,” in CVPR , 2019
2019
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in ICLR , 2019
2019
Cited alongside, same era.
K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang et al. , “Hybrid task cascade for instance segmentation,” in CVPR , 2019
2019
Cited alongside, same era.
L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu, and M. Pietikäinen, “Deep learning for generic object detection: A survey,” International Journal of Computer Vision , vol. 128, no. 2, pp. 261–318, 2020
2020
Cited alongside, same era.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in ECCV , 2020
2020
Cited alongside, same era.
S. Wu, X. Li, and X. Wang, “Iou-aware single-stage object detector for accurate localization,” Image and Vision Computing , vol. 97, p. 103911, 2020
B. Heo, S. Yun, D. Han, S. Chun, J. Choe, and S. J. Oh, “Rethinking spatial dimensions of vision transformers,” in ICCV , 2021
2021
Later among the works it cites.
X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable DETR: Deformable transformers for end-to-end object detection,” in ICLR , 2021
2021
Later among the works it cites.
B. Dong, F. Zeng, T. Wang, X. Zhang, and Y. Wei, “SOLQ: Segmenting objects by learning queries,” in NeurIPS , 2021
2021
Later among the works it cites.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in ICML , 2021
2021
Later among the works it cites.
J. Lin, X. Mao, Y. Chen, L. Xu, Y. He et al. , “D 2
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
X. Wang, R. Zhang, T. Kong, L. Li, and C. Shen, “Solov2: Dynamic and fast instance segmentation,” in NeurIPS , 2020
2020
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin Transformer: Hierarchical vision transformer using shifted windows,” in ICCV , 2021
2021
Cited alongside, same era.
Z.-H. Jiang, Q. Hou, L. Yuan, D. Zhou, Y. Shi, X. Jin, A. Wang, and J. Feng, “All tokens matter: Token labeling for training better vision transformers,” in NeurIPS , 2021
2021
Cited alongside, same era.
W. Xu, Y. Xu, T. Chang, and Z. Tu, “Co-scale conv-attentional image transformers,” in ICCV , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Y. Fang, S. Yang, X. Wang, Y. Li, C. Fang, Y. Shan, B. Feng, and W. Liu, “Instances as queries,” in ICCV , 2021
2021
Later among the works it cites.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” in ICCV , 2021
2021
Later among the works it cites.
C.-F. Chen, Q. Fan, and R. Panda, “CrossViT: Cross-attention multi-scale vision transformer for image classification,” in ICCV , 2021
2021
Later among the works it cites.
H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized intersection over union: A metric and a loss for bounding box regression,” in CVPR , 2019
2021
Later among the works it cites.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pvtv2: Improved baselines with pyramid vision transformer,” Computational Visual Media , vol. 8, no. 3, pp. 1–10, 2022
2022
Closest in time.
H. Song, D. Sun, S. Chun, V. Jampani, D. Han, B. Heo, W. Kim, and M.-H. Yang, “Vidt: An efficient and effective fully transformer-based object detector,” in ICLR , 2022
2022
Closest in time.