Multi-Modal Knowledge Hypergraph for Diverse Image Retrieval
Authors: Yawen Zeng, Qin Jin, Tengfei Bao, Wenfeng Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world datasets have well verified the effectiveness and explainability of our proposed method. |
| Researcher Affiliation | Collaboration | 1 Byte Dance AI Lab 2 School of Information, Renmin University of China |
| Pseudocode | No | The paper describes its methods and components in text and mathematical formulas but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'We implement our solution based on the Tensorflow framework3.', but the footnote 3 links to the Tensorflow framework itself, not to the authors' specific implementation code for their proposed method. |
| Open Datasets | Yes | Div150Ad Hoc1 is a dataset for the competition of diverse social image retrieval (Ionescu et al. 2016), which contains a variety of keyword-based queries... 1http://campus.pub.ro/lab7/bionescu/Div150Adhoc.html. Div4002 is constructed by Media Eval Workshop (Ionescu et al. 2014)... 2http://multimediaeval.org/mediaeval2014/diverseimages2014 |
| Dataset Splits | No | The paper mentions using 'training batches' for losses and specifies hyperparameters used on the datasets, but it does not explicitly provide the train/validation/test dataset splits (e.g., percentages or sample counts) used for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Tensorflow framework' but does not specify a version number for Tensorflow or any other key software dependencies with their versions. |
| Experiment Setup | Yes | For specific hyperparameters in our method, the instance numbers m, the margin , the threshold τ and the balance factors λ1, λ2 are set as (8, 0.4, 0.4, 0.4, 0.3) and (8, 0.4, 0.3, 0.3, 0.3) on Div150Ad Hoc and Div400 datasets, respectively. |