Notice: 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. Full accuracy metrics and methodology are described in [1].
Multi-Granular Multimodal Clue Fusion for Meme Understanding
Authors: Li Zheng, Hao Fei, Ting Dai, Zuquan Peng, Fei Li, Huisheng Ma, Chong Teng, Donghong Ji
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on the widely-used MET-MEME bilingual dataset demonstrate significant improvements over state-of-the-art baselines. Specifically, there is an 8.14% increase in precision for offensiveness detection task, and respective accuracy enhancements of 3.53%, 3.89%, and 3.52% for metaphor recognition, sentiment analysis, and intention detection tasks. |
| Researcher Affiliation | Academia | 1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China 2National University of Singapore, Singapore, Singapore 3Laboratory for Advanced Computing and Intelligence Engineering, Wuxi, China 4North China Institute of Computing Technology, beijing, China EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methodology using mathematical formulations and textual explanations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing code or provide links to a code repository for the described methodology. |
| Open Datasets | Yes | To verify the effectiveness of our model, we conduct experiments on the benchmark MET-MEME bilingual dataset (Xu et al. 2022), which contains both English and Chinese memes. |
| Dataset Splits | No | The paper mentions using the MET-MEME bilingual dataset and discusses training, but does not explicitly provide the train/validation/test splits used for their experiments (e.g., specific percentages or sample counts). |
| 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 Multilingual BERT and VGG16 models, but it does not specify any software dependencies with their version numbers (e.g., Python version, PyTorch/TensorFlow versions, or specific library versions). |
| Experiment Setup | No | The paper states that the model is trained using a standard gradient descent algorithm and cross-entropy loss, and mentions 'τ is the temperature to scale the logits' as a parameter in the loss function. However, it does not provide specific hyperparameters such as learning rate, batch size, number of epochs, or detailed optimizer settings. |