Multi-Modal Learning over User-Contributed Content from Cross-Domain Social Media
Authors: Wen-Yu Lee
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experiments was conducted on a large-scale image dataset that contains more than 540,000 images with 9,360 annotated ground truth images in 21 query categories. Results showed that my approach improved about 77% mean average precision for top-10 tags, compared with a common approach considering only visual features. Further, we discovered relevance of tags and images (Wu et al. 2013). Experiments on an image dataset of San Francisco showed that the approach outperformed common approaches that considers visual features only, geo-tags only, and both of visual features and geo-tags. |
| Researcher Affiliation | Academia | Wen-Yu Lee National Taiwan University, Taipei, Taiwan majorrei@cmlab.csie.ntu.edu.tw |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions using a 'large-scale image dataset' and 'an image dataset of San Francisco' for experiments, but it does not provide any concrete access information (link, DOI, repository, or formal citation with author/year for public availability) for these datasets. |
| Dataset Splits | No | The paper mentions the size of a dataset used ('540,000 images with 9,360 annotated ground truth images') but does not specify any training, validation, or test splits, percentages, or sample counts needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU/GPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions, solver versions) that would be needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the methods and features used (e.g., Map Reduce, sparse coding) but does not provide specific experimental setup details such as hyperparameter values, learning rates, batch sizes, or optimizer settings. |