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..
Cross-Modal Coherence for Text-to-Image Retrieval
Authors: Malihe Alikhani, Fangda Han, Hareesh Ravi, Mubbasir Kapadia, Vladimir Pavlovic, Matthew Stone10427-10435
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our analysis shows that models trained with image text coherence relations can retrieve images originally paired with target text more often than coherence-agnostic models. We also show via human evaluation that images retrieved by the proposed coherence-aware model are preferred over a coherenceagnostic baseline by a huge margin. |
| Researcher Affiliation | Academia | 1University of Pittsburgh 2Rutgers University |
| Pseudocode | No | The paper describes the model architecture and steps but does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code, contact and data: https://github.com/klory/Cross-Modal Coherence-for-Text-to-Image-Retrieval |
| Open Datasets | Yes | We study the efficacy of CMCM for image-retrieval by leveraging two image-text datasets CITE++ and Clue (Alikhani et al. 2020) that are annotated with image-text coherence relations. |
| Dataset Splits | Yes | We split the CITE++ dataset as 3439/860 for training/testing while the Clue dataset as 6047/1512 for training/testing. 10% of the training data is used as validation. |
| Hardware Specification | No | The paper describes the network details and experimental setup but does not specify any particular GPU models, CPU types, or other hardware used for running experiments. |
| Software Dependencies | No | The paper mentions software components like Resnet50, word2vec, LSTM, and Gensim, but does not provide specific version numbers for these or any other software dependencies needed for replication. |
| Experiment Setup | No | Further training and hyperparameter details are given in the appendix. |