Unsupervised Learning of Object Landmarks through Conditional Image Generation
Authors: Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our approach can learn object landmarks from synthetic image deformations or videos, all without manual supervision, while outperforming state-of-the-art unsupervised landmark detectors. We further show that our method is applicable to a large variety of datasets faces, people, 3D objects, and digits without any modifications. |
| Researcher Affiliation | Academia | 1 Visual Geometry Group University of Oxford {tomj,ankush,vedaldi}@robots.ox.ac.uk 2 School of Informatics University of Edinburgh hbilen@ed.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project code and details are available at: http://www.robots.ox.ac.uk/ ~vgg/research/unsupervised_landmarks/ |
| Open Datasets | Yes | We use the 200k Celeb A [24] images after resizing them to 128 128 resolution. We consider Vox Celeb [28], a large dataset of face tracks... BBC-Pose [3], and Human3.6M [17]. Small NORB [26] dataset... SVHN digits [29] |
| Dataset Splits | Yes | Model selection is done using 10% validation split of the training data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions optimizers and pre-trained models (e.g., Adam, VGG-19) but does not provide specific version numbers for software dependencies or frameworks. |
| Experiment Setup | Yes | The learning rate is set to 10 2, and lowered by a factor of 10 once the training error stops decreasing; the ℓ2-norm of the gradients is bounded to 1.0. |