Learning Label Dependencies for Visual Information Extraction
Authors: Minghong Yao, Liansheng Zhuang, Houqiang Li, Jiuchang Wei
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public datasets have demonstrated the effectiveness of our framework. |
| Researcher Affiliation | Academia | University of Science and Technology of China |
| Pseudocode | Yes | Algorithm 1 Training procedures. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of open-source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Table 1 lists three VIE benchmark datasets: FUNSD [Jaume et al., 2019], CORD [Park et al., 2019], and SROIE [Huang et al., 2019]. |
| Dataset Splits | No | Table 1 lists three VIE benchmark datasets: FUNSD Train 149, Test 50 Forms CORD Train 800, Test 100 Receipts SROIE Train 626, Test 276 Receipts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam W optimizer' but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The Adam W optimizer is employed for fine-tuning, with an initial learning rate of 5e-5 and a linear decay learning rate scheduler. We employed the Adam W optimizer to train it, with a learning rate set to 5e-5 and a batch size of 1. For the fixed-point search, we utilized the Broyden [Broyden, 1965] method, with a maximum iteration step limit set to 10. We set Tinner to be 4 during training and testing. |