TallyQA: Answering Complex Counting Questions
Authors: Manoj Acharya, Kushal Kafle, Christopher Kanan8076-8084
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we describe a series of experiments to evaluate the efficacy of multiple algorithms on both simple and complex counting questions. Results for all methods on How Many-QA and both of Tally QA s test sets are given in Table 3. |
| Researcher Affiliation | Academia | Manoj Acharya, Kushal Kafle, Christopher Kanan Chester F. Carlson Center for Imaging Science Rochester Institute of Technology {ma7583, kk6055, kanan}@rit.edu |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | No explicit statement or link regarding the open-sourcing of the research's code was found. |
| Open Datasets | Yes | We created Tally QA, the world s largest dataset for open-ended counting... It is now publicly available. Tally QA s images are drawn from both COCO and Visual Genome. |
| Dataset Splits | Yes | Tally QA is split into one training split (Train) and two test splits: Test-Simple and Test-Complex. Using our simple-complex classifier, Train was found to have 188,439 simple and 60,879 complex questions. The number of questions in each split is given in Table 2. |
| Hardware Specification | No | The lab thanks NVIDIA for the donation of a GPU, but no specific GPU model or other hardware specifications for the experiments are provided. |
| Software Dependencies | No | The paper mentions software like Faster R-CNN, ResNet-101, Adam optimizer, and SpaCy but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The GRU used in all models are regularized using a dropout of 0.3. RCN is trained using the Adam optimizer with a learning rate of 7e 4 and a batch size of 64 samples. |