Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Unsupervised Learning of Object Landmarks through Conditional Image Generation
Authors: Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,879 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,166 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL 2 School of Informatics University of Edinburgh EMAIL |
| 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. |