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Jifeng Dai
Jifeng Dai
Associate Professor of EE, Tsinghua University; Adjuct Researcher of Shanghai AI Laboratory
Bestätigte E-Mail-Adresse bei tsinghua.edu.cn - Startseite
Titel
Zitiert von
Zitiert von
Jahr
R-FCN: Object Detection via Region-based Fully Convolutional Networks
J Dai, Y Li, K He, J Sun
Neural Information Processing Systems (NIPS), 2016
73662016
Deformable Convolutional Networks
J Dai, H Qi, Y Xiong, Y Li, G Zhang, H Hu, Y Wei
Computer Vision (ICCV), 2017 IEEE International Conference on, 2017
57362017
Deformable DETR: Deformable Transformers for End-to-End Object Detection
X Zhu, W Su, L Lu, B Li, X Wang, J Dai
ICLR, 2021
40492021
Mmdetection: Open mmlab detection toolbox and benchmark
K Chen, J Wang, J Pang, Y Cao, Y Xiong, X Li, S Sun, W Feng, Z Liu, J Xu, ...
arXiv preprint arXiv:1906.07155, 2019
27352019
Deformable convnets v2: More deformable, better results
X Zhu, H Hu, S Lin, J Dai
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
19492019
Vl-bert: Pre-training of generic visual-linguistic representations
W Su, X Zhu, Y Cao, B Li, L Lu, F Wei, J Dai
The International Conference on Learning Representations (ICLR), 2020
16562020
Instance-aware Semantic Segmentation via Multi-task Network Cascades
J Dai, K He, J Sun
Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on, 2016
16532016
Relation networks for object detection
H Hu, J Gu, Z Zhang, J Dai, Y Wei
Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018
14352018
Fully Convolutional Instance-aware Semantic Segmentation
Y Li, H Qi, J Dai, X Ji, Y Wei
Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, 2017
12982017
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J Dai, K He, J Sun
Computer Vision (ICCV), 2015 IEEE International Conference on, 2015
12352015
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
D Lin, J Dai, J Jia, K He, J Sun
Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on, 2016
11402016
Deep Feature Flow for Video Recognition
X Zhu, Y Xiong, J Dai, L Yuan, Y Wei
Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, 2017
7712017
Flow-Guided Feature Aggregation for Video Object Detection
X Zhu, Y Wang, J Dai, L Yuan, Y Wei
Computer Vision (ICCV), 2017 IEEE International Conference on, 2017
7452017
Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers
Z Li, W Wang, H Li, E Xie, C Sima, T Lu, Y Qiao, J Dai
European conference on computer vision, 1-18, 2022
6662022
Convolutional feature masking for joint object and stuff segmentation
J Dai, K He, J Sun
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, 2015
5372015
Instance-sensitive fully convolutional networks
J Dai, K He, Y Li, S Ren, J Sun
European Conference on Computer Vision (ECCV), 2016
5202016
Exploring cross-image pixel contrast for semantic segmentation
W Wang, T Zhou, F Yu, J Dai, E Konukoglu, L Van Gool
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
4532021
An empirical study of spatial attention mechanisms in deep networks
X Zhu, D Cheng, Z Zhang, S Lin, J Dai
International Conference on Computer Vision (ICCV), 2019
4242019
Vision Transformer Adapter for Dense Predictions
Z Chen, Y Duan, W Wang, J He, T Lu, J Dai, Y Qiao
arXiv preprint arXiv:2205.08534, 2022
3492022
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, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
3432023
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