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Iro Laina
Iro Laina
Verified email at robots.ox.ac.uk - Homepage
Title
Cited by
Cited by
Year
Deeper Depth Prediction with Fully Convolutional Residual Networks
I Laina, C Rupprecht, V Belagiannis, F Tombari, N Navab
3D Vision (3DV), 2016 Fourth International Conference on, 239-248, 2016
16482016
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
K Tateno, F Tombari, I Laina, N Navab
Computer Vision and Pattern Recognition (CVPR), 2017, 6243-6252, 2017
6342017
Concurrent segmentation and localization for tracking of surgical instruments
I Laina, N Rieke, C Rupprecht, JP Vizcaíno, A Eslami, F Tombari, ...
International conference on medical image computing and computer-assisted …, 2017
1392017
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
C Rupprecht, I Laina, R DiPietro, M Baust, F Tombari, GD Hager, N Navab
International Conference on Computer Vision (ICCV) 2017, 2016
1312016
2017 robotic instrument segmentation challenge
M Allan, A Shvets, T Kurmann, Z Zhang, R Duggal, YH Su, N Rieke, ...
arXiv preprint arXiv:1902.06426, 2019
762019
Towards unsupervised image captioning with shared multimodal embeddings
I Laina, C Rupprecht, N Navab
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
532019
Semantic image manipulation using scene graphs
H Dhamo, A Farshad, I Laina, N Navab, GD Hager, F Tombari, ...
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
482020
Peeking behind objects: Layered depth prediction from a single image
H Dhamo, K Tateno, I Laina, N Navab, F Tombari
Pattern Recognition Letters 125, 333-340, 2019
362019
Guide Me: Interacting with Deep Networks
C Rupprecht, I Laina, N Navab, GD Hager, F Tombari
Computer Vision and Pattern Recognition (CVPR), 2018, 2018
272018
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions
G Ghazaei, I Laina, C Rupprecht, F Tombari, N Navab, K Nazarpour
Asian Conference on Computer Vision (ACCV) 2018, 2018
182018
Finding an unsupervised image segmenter in each of your deep generative models
L Melas-Kyriazi, C Rupprecht, I Laina, A Vedaldi
arXiv preprint arXiv:2105.08127, 2021
132021
Unsupervised part discovery from contrastive reconstruction
S Choudhury, I Laina, C Rupprecht, A Vedaldi
Advances in Neural Information Processing Systems 34, 28104-28118, 2021
112021
Clevrtex: A texture-rich benchmark for unsupervised multi-object segmentation
L Karazija, I Laina, C Rupprecht
arXiv preprint arXiv:2111.10265, 2021
112021
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization
L Melas-Kyriazi, C Rupprecht, I Laina, A Vedaldi
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
72022
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
I Laina, RC Fong, A Vedaldi
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
52020
Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations
V Tschernezki, I Laina, D Larlus, A Vedaldi
arXiv preprint arXiv:2209.03494, 2022
12022
Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion
S Choudhury, L Karazija, I Laina, A Vedaldi, C Rupprecht
arXiv preprint arXiv:2205.07844, 2022
12022
The Curious Layperson: Fine-Grained Image Recognition without Expert Labels
S Choudhury, I Laina, C Rupprecht, A Vedaldi
arXiv preprint arXiv:2111.03651, 2021
12021
Measuring the interpretability of unsupervised representations via quantized reversed probing
I Laina, YM Asano, A Vedaldi
ICLR, 2022
2022
Semantik, Sprache und Geometrie: Szenenverständnis lernen
I Laina
Ausgezeichnete Informatikdissertationen 2020, 2021
2021
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