Notice: 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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Unsupervised Learning of Object Landmarks via Self-Training Correspondence
Authors: Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This Section presents experiments illustrating the results produced by our method and by recent state-of-the-art approaches, as well as ablation studies shedding light into some of the key properties of our method. Datasets: We validate our approach on faces, human bodies and cat faces. |
| Researcher Affiliation | Collaboration | Dimitrios Mallis University of Nottingham EMAIL Enrique Sanchez Samsung AI Center, Cambridge, UK EMAIL Matt Bell University of Nottingham EMAIL Georgios Tzimiropoulos Queen Mary University of London, UK Samsung AI Center, Cambridge, UK EMAIL |
| Pseudocode | No | The paper describes the algorithms and procedures in prose, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/malldimi1/Unsupervised Landmarks. |
| Open Datasets | Yes | Datasets: We validate our approach on faces, human bodies and cat faces. For faces, we used Celeb A [23], AFLW [18], and the challenging LS3D [4]... For human bodies, we use BBCPose [8] and Human3.6M [14]. |
| Dataset Splits | Yes | For AFLW we used the official train/test partitions, and for LS3D we followed the same protocol as [4] and used the 300W-LP partition [53] to train our models. |
| Hardware Specification | No | The paper does not explicitly state any specific hardware used for running the experiments (e.g., GPU model, CPU type, memory). |
| Software Dependencies | No | All models were implemented in Py Torch [28]. ... For K-means, we used the Faiss library [16]. No specific version numbers for PyTorch or Faiss are provided. |
| Experiment Setup | Yes | We used RMSprop [13], with learning rate equal to 5 10 4, weight decay 10 5 and batch-size 16 for stage 1 and 64 for stage 2. We set M = 100 and M = 250 clusters for facial and body landmarks, respectively. ... no more than 300,000 iterations are necessary for the algorithm to converge for all datasets. |