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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
CoMIR: Contrastive Multimodal Image Representation for Registration
Authors: Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Natasa Sladoje
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess the extent of achieved rotational equivariance and the stability of the representations with respect to weight initialization, training set, and hyperparameter settings, on a remote sensing dataset of RGB and near-infrared images. We evaluate the learnt representations through registration of a biomedical dataset of bright-field and second-harmonic generation microscopy images; two modalities with very little apparent correlation. |
| Researcher Affiliation | Academia | Nicolas Pielawski , Elisabeth Wetzer*, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Nataša Sladoje Dept. of Information Technology, Uppsala University, Sweden |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/MIDA-group/Co MIR. |
| Open Datasets | Yes | Zurich Dataset. The open Zurich dataset [57] consists of 20 aerial images of the city of Zurich of about 930 940px. |
| Dataset Splits | Yes | The training set consists of 40 image pairs of size 834 834px which are center-cropped from the corresponding original images. The validation set for the CNN training consists of another 25 such pairs, a tuning set for registration parameters of additional 7 pairs, and the test set for evaluation of another 134 pairs. |
| Hardware Specification | No | The paper mentions 'training (GPU, 1345s)' and 'inference (CPU, 5s/image)' but does not provide specific hardware details such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as programming language versions or library versions (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We set the temperature to τ = 0.5. We use 46 negative samples. ... For both datasets patches of the size 128 128px were chosen. ... The SHG images were preprocessed by applying a log-transform log(1 + x) with x [0, 1]. |