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..
K-LITE: Learning Transferable Visual Models with External Knowledge
Authors: Sheng Shen, Chunyuan Li, Xiaowei Hu, Yujia Xie, Jianwei Yang, Pengchuan Zhang, Zhe Gan, Lijuan Wang, Lu Yuan, Ce Liu, Kurt Keutzer, Trevor Darrell, Anna Rohrbach, Jianfeng Gao
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study the performance of K-LITE on two important computer vision problems, image classification and object detection, benchmarking on 20 and 13 different existing datasets, respectively. The proposed knowledge-augmented models show significant improvement in transfer learning performance over existing methods. |
| Researcher Affiliation | Collaboration | Microsoft \University of California, Berkeley |
| Pseudocode | No | The paper describes methods and processes but does not include any formally structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/microsoft/klite. |
| Open Datasets | Yes | We pre-train on Image Net-21K [15] and GCC [76, 11]/YFCC [84] datasets... Following GLIP [50], we pre-train on Object365 [75]... |
| Dataset Splits | No | The paper mentions training and testing datasets (Table 1) and evaluates performance metrics but does not explicitly specify validation dataset splits or their sizes in the main text. |
| Hardware Specification | No | The main text states that hardware specifications are provided in the Appendix, which is not available for analysis. |
| Software Dependencies | No | The paper mentions 'Spacy [32]' without a version number, and refers to other models/frameworks like CLIP, ALIGN, Uni CL, and GLIP, but does not provide specific version numbers for software dependencies or libraries used for implementation. |
| Experiment Setup | No | The main text states that training details including hyperparameters are provided in the Appendix, which is not available for analysis. |