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 [1].
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training
Authors: Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, Junmo Kim
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we show that each component that comprises Uni CLIP contributes well to the final performance. |
| Researcher Affiliation | Collaboration | Janghyeon Lee LG AI Research EMAIL Jongsuk Kim KAIST EMAIL |
| Pseudocode | No | The paper describes methods using text and mathematical formulas but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | Yes | Datasets For reproducibility, we use publicly available datasets for training and evaluation in our experiments, including CC3M [23], CC12M [2], De CLIP YFCC15M [13, 25] for training and Pets [18], CIFAR-10, CIFAR-100 [12], SUN397 [29], Food-101 [1], Flowers [17], Cars [11], Caltech101 [8], Aircraft [14], DTD [6], Image Net-1k [21], Flickr30k [19], COCO Captions [5] for evaluation. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] |
| Hardware Specification | No | The paper's main text does not specify specific hardware details such as GPU/CPU models or types of internal clusters/cloud providers used for experiments, although the ethics statement indicates this information was provided. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | Settings For each original image and corresponding text caption, one weakly augmented image, two strongly augmented images, and one text form a positive group in our experiments. Detailed augmentation and optimization configurations can be found in Appendix. |