VELDA: Relating an Image Tweet’s Text and Images
Authors: Tao Chen, Hany SalahEldeen, Xiangnan He, Min-Yen Kan, Dongyuan Lu
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world image tweets in both English and Chinese and other user generated content, show that VELDA significantly outperforms existing methods on cross-modality image retrieval.Even in other domains where emotion does not factor in image choice directly, our VELDA model demonstrates good generalization ability, achieving higher fidelity modeling of such multimedia documents. |
| Researcher Affiliation | Academia | 1School of Computing, National University of Singapore 2Department of Computer Science, Old Dominion University 3NUS Interactive and Digital Media Institute, Singapore |
| Pseudocode | No | The information provided is insufficient. The paper describes its model formulation and parameter estimation through text and mathematical equations, but does not present any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The information provided is insufficient. The paper does not contain any statement about releasing its source code, nor does it provide a link to a code repository for the methodology described. |
| Open Datasets | No | The information provided is insufficient. The paper describes how the authors collected their own datasets (Weibo, Twitter, Google-Zh, Google-En, POTD) based on queries, but does not provide any specific links, DOIs, repository names, or explicit statements that these collected datasets are publicly available for download or access. |
| Dataset Splits | No | The information provided is insufficient. The paper states, 'We randomly split each dataset into 90% as training set and the remaining 10% as testing set,' but does not mention a separate validation split or how validation was handled for hyperparameter tuning. |
| Hardware Specification | No | The information provided is insufficient. The paper does not specify any particular hardware components such as GPU models, CPU models, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The information provided is insufficient. The paper mentions various software components and methods used (e.g., 'Chinese word segmentation program', 'SIFT', 'k-means', 'Google Translate') but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | Our development testing showed that VELDA operated well over a wide range of hyperparameter settings. As such, we fix the six sets of hyperparameters to relatively standard settings: αV =1, αE=1, βV =0.1, βE=0.1, γ = 0.1, and η = 0.5. We then tune the number of visual topics (K) and emotional topics (E) in a grid search for each dataset (see Table 2 for the detailed settings). |