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..
VistaNet: Visual Aspect Attention Network for Multimodal Sentiment Analysis
Authors: Quoc-Tuan Truong, Hady W. Lauw305-312
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on restaurant reviews showcase the effectiveness of visual aspect attention, visa-vis visual features or textual attention. |
| Researcher Affiliation | Academia | Quoc-Tuan Truong, Hady W. Lauw School of Information Systems Singapore Management University EMAIL EMAIL |
| Pseudocode | No | The paper includes architectural diagrams and mathematical equations, but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Vista Net is implemented using Tensor Flow4. https://github.com/Preferred AI/vista-net |
| Open Datasets | No | We use a dataset of online reviews crawled from the Food and Restaurants categories of Yelp.com, covering 5 different major US cities... The datasets and codes used in this submission will be released publicly upon publication. |
| Dataset Splits | Yes | We keep the number of examples balanced across classes, and split 80% of the data for training, 5% for validation and 15% for test. |
| Hardware Specification | No | The paper mentions software like TensorFlow and pre-trained models like VGG-16, but does not specify any CPU, GPU, or memory details used for running the experiments. |
| Software Dependencies | No | The paper mentions 'NLTK' and 'Tensor Flow4', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | GRU cells are 50-dimensional for word and sentence encoding, (100dimensional due to bidirectional RNN). Context vectors U, V and K are also 100-dimensional for the attention spaces of word, sentence, and document. In training, we use RMSprop (Tieleman and Hinton 2012) for gradient based optimization with a mini-batch size of 32. |