| Detecting anomalous events in videos by learning deep representations of appearance and motion D Xu, Y Yan, E Ricci, N Sebe Computer Vision and Image Understanding (CVIU), 2017 | 1072* | 2017 |
| Multi-scale continuous crfs as sequential deep networks for monocular depth estimation D Xu, E Ricci, W Ouyang, X Wang, N Sebe IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017 | 540 | 2017 |
| Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing D Xu, W Ouyang, X Wang, N Sebe IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 535 | 2018 |
| Multi-channel attention selection gan with cascaded semantic guidance for cross-view image translation H Tang, D Xu, N Sebe, Y Wang, JJ Corso, Y Yan IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 | 413 | 2019 |
| Structured attention guided convolutional neural fields for monocular depth estimation D Xu, W Wang, H Tang, H Liu, N Sebe, E Ricci IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 378 | 2018 |
| Group consistent similarity learning via deep CRFs for person re-identification D Chen, D Xu, H Li, N Sebe, X Wang IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 262 | 2018 |
| Learning cross-modal deep representations for robust pedestrian detection D Xu, W Ouyang, E Ricci, X Wang, N Sebe IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017 | 250 | 2017 |
| Attentiongan: Unpaired image-to-image translation using attention-guided generative adversarial networks H Tang, H Liu, D Xu, PHS Torr, N Sebe IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021 | 236 | 2021 |
| Delving into localization errors for monocular 3D object detection X Ma, Y Zhang, D Xu, D Zhou, S Yi, H Li, W Ouyang IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 | 226 | 2021 |
| Multi-class token transformer for weakly supervised semantic segmentation L Xu, W Ouyang, M Bennamoun, F Boussaid, D Xu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 210 | 2022 |
| Unsupervised adversarial depth estimation using cycled generative networks A Pilzer, D Xu, M Puscas, E Ricci, N Sebe IEEE International Conference on 3D Vision (3DV), 2018 | 205 | 2018 |
| Transformer-based attention networks for continuous pixel-wise prediction G Yang, H Tang, M Ding, D Xu, N Sebe, E Ricci IEEE International Conference on Computer Vision (ICCV), 2021 | 183 | 2021 |
| Local class-specific and global image-level generative adversarial networks for semantic-guided scene generation H Tang, D Xu, Y Yan, PHS Torr, N Sebe IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 | 180 | 2020 |
| Leveraging auxiliary tasks with affinity learning for weakly supervised semantic segmentation L Xu, W Ouyang, M Bennamoun, F Boussaid, F Sohel, D Xu IEEE International Conference on Computer Vision (ICCV), 2021 | 162 | 2021 |
| Neural information retrieval: At the end of the early years KD Onal, Y Zhang, IS Altingovde, MM Rahman, P Karagoz, A Braylan, ... Springer Information Retrieval 21 (2), 111-182, 2018 | 153 | 2018 |
| Attention-guided generative adversarial networks for unsupervised image-to-image translation H Tang, D Xu, N Sebe, Y Yan arXiv preprint arXiv:1903.12296, 2019 | 147 | 2019 |
| Dynamic graph message passing networks L Zhang, D Xu, A Arnab, PHS Torr IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 | 146 | 2020 |
| Depth-aware generative adversarial network for talking head video generation FT Hong, L Zhang, L Shen, D Xu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 144 | 2022 |
| Learning deep structured multi-scale features using attention-gated crfs for contour prediction D Xu, W Ouyang, X Alameda-Pineda, E Ricci, X Wang, N Sebe Advances in Neural Information Processing Systems (NeurIPS), 2017 | 136 | 2017 |
| Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts D Xu, R Song, X Wu, N Li, W Feng, H Qian arXiv preprint arXiv:2005.11182, 2014 | 123 | 2014 |
Nicu SebeUniversity of TrentoVerified email at unitn.it
