Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Exploring Models and Data for Image Question Answering
Authors: Mengye Ren, Ryan Kiros, Richard Zemel
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,168 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,523 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Results |
| Researcher Affiliation | Academia | Mengye Ren1, Ryan Kiros1, Richard S. Zemel1,2 University of Toronto1 Canadian Institute for Advanced Research2 EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release the complete details of the models at https://github.com/renmengye/ imageqa-public. |
| Open Datasets | Yes | COCO-QA dataset can be downloaded at http://www.cs.toronto.edu/ mren/ imageqa/data/cocoqa |
| Dataset Splits | Yes | Table 1: COCO-QA question type break-down CATEGORY TRAIN % TEST % OBJECT 54992 69.84% 27206 69.85% NUMBER 5885 7.47% 2755 7.07% COLOR 13059 16.59% 6509 16.71% LOCATION 4800 6.10% 2478 6.36% TOTAL 78736 100.00% 38948 100.00% |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Stanford parser', 'Word Net', and 'NLTK software package', but does not provide specific version numbers for these dependencies. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |