Iro Laina
Iro Laina
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Cited by
Cited by
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
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
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
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
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
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
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
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
Guide Me: Interacting with Deep Networks
C Rupprecht, I Laina, N Navab, GD Hager, F Tombari
Computer Vision and Pattern Recognition (CVPR), 2018, 2018
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
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
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
L Karazija, I Laina, C Rupprecht
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
I Laina, RC Fong, A Vedaldi
arXiv preprint arXiv:2010.14551, 2020
Semantics, Language and Geometry: Learning to Understand the Scene
I Laina
Technische Universität München, 2020
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