Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering
Authors: Somak Aditya, Yezhou Yang, Chitta Baral
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental analysis of the answers and the key evidential predicates generated on the VQA dataset validate our approach. |
| Researcher Affiliation | Academia | Somak Aditya, Yezhou Yang, Chitta Baral School of Computing, Informatics and Decision Systems Engineering Arizona State University {saditya1,yz.yang,chitta}@asu.edu |
| Pseudocode | No | The paper describes the model architecture and logical formulations but does not include any explicit pseudocode blocks or algorithm sections. |
| Open Source Code | No | The paper states: 'We intend to make the details about the engine publicly available for further research.' and 'We will make our final answers together with ranked key evidence predicates publicly available for further research.' These are promises for future release of details or data, not current availability of the code. The link 'visionandreasoning.wordpress.com' is for examples, not code. |
| Open Datasets | Yes | MSCOCO-VQA (Antol et al. 2015) is the largest VQA dataset that contains both multiple choices and open-ended questions about arbitrary images collected from the Internet. |
| Dataset Splits | Yes | Specifically, we use 82, 783 images for training and 40, 504 validation images for testing. |
| Hardware Specification | No | The paper does not explicitly specify the hardware used for running the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper mentions software components used (e.g., 'pre-trained Dense Captioning system', 'Stanford Dependency parsing', 'word2vec') but does not provide specific version numbers for these or other relevant software libraries/frameworks. |
| Experiment Setup | No | The paper describes the system components and overall approach but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or specific training configurations. |