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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer
Authors: Yabing Wang, Fan Wang, Jianfeng Dong, Hao Luo
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 17,073 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,907 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 evaluate our proposed approach on two multilingual image-text datasets, Multi30K and MSCOCO, and one video-text dataset, VATEX. The results clearly demonstrate the effectiveness of our proposed method and its high potential for large-scale retrieval. |
| Researcher Affiliation | Collaboration | 1 Zhejiang Gongshang University 2 Xi an Jiaotong University 3 DAMO Academy, Alibaba Group 4 Hupan Lab, Zhejiang Province 5 Zhejiang Key Lab of E-Commerce |
| Pseudocode | No | The paper describes the methodology using mathematical equations and descriptive text, but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the release of source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on two public multilingual image-text retrieval datasets (Multi30K and MSCOCO), as well as a video-text retrieval dataset (VATEX). (...) Multi30K (Elliott et al. 2016): (...) MSCOCO (Chen et al. 2015): (...) VATEX (Wang et al. 2019): |
| Dataset Splits | Yes | We adopt a similar data partition as (Young et al. 2014). (...) We follow the data split as in (Zhou et al. 2021). (...) We adopt a similar data partition as (Chen et al. 2020). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions models like CLIP and mBERT-base and an Adam optimizer, but it does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Besides, we set λ = 0.6, α = 0.4, and τ = 0.07 in our experiments. The batch size is 128, and an Adam optimizer with an initial learning rate 2.5e-5 and adjustment schedule similar to (Luo et al. 2022) is utilized. |