An Empirical Study on the Language Modal in Visual Question Answering
Authors: Daowan Peng, Wei Wei, Xian-Ling Mao, Yuanyuan Fu, Dangyang Chen
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper attempts to provide new insights into the influence of language modality on VQA performance from an empirical study perspective. To achieve this, we conducted a series of experiments on six models. The results of these experiments revealed that... |
| Researcher Affiliation | Collaboration | 1Cognitive Computing and Intelligent Information Processing (CCIIP) Laboratory, School of Computer Science and Technology, Huazhong University of Science and Technology 2Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL) 3Department of Computer Science and Technology, Beijing Institute of Technology 4Ping An Property & Casualty Insurance Company of China, Ltd |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented in the paper. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing its own source code or provide a link to a code repository. |
| Open Datasets | Yes | Dataset: We selected the widely-used VQAv2 benchmark [Goyal et al., 2017] and its OOD benchmark, VQA-CPv2 [Agrawal et al., 2018]. |
| Dataset Splits | Yes | The results on the VQAv2 validation split demonstrate that all models experience varying degrees of performance degradation when evaluated on variant questions. |
| Hardware Specification | No | No specific hardware (e.g., GPU models, CPU types, or cloud instances with specs) used for running experiments is mentioned in the paper. |
| Software Dependencies | No | The paper mentions software components like BERT, LSTM, or GRU, but does not specify their version numbers or other ancillary software dependencies required for replication. |
| Experiment Setup | Yes | Regarding the implementation details in the training process, we adhere to the experimental settings of the open-source codes and do not modify other parameters such as learning rate, batch size, or optimizer. |