Probabilistic Multimodal Learning with von Mises-Fisher Distributions
Authors: Peng Hu, Yang Qin, Yuanbiao Gou, Yunfan Li, Mouxing Yang, Xi Peng
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on nine benchmarks demonstrate the superiority of PML, consistently outperforming 14 state-of-the-art methods. Code is available at https://github. com/XLearning-SCU/2025-IJCAI-PML. The paper includes a dedicated section 4 titled 'Experiments' with subsections for 'Comparisons with State of the Art', 'Ablation Study', and 'Reliability Study', presenting numerous performance tables and analysis. |
| Researcher Affiliation | Academia | 1College of Computer Science, Sichuan University, China. 2National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation, China. All listed affiliations are academic institutions in China. |
| Pseudocode | No | The paper describes its methodology using mathematical formulations and descriptive text within the 'Methodology' section, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github. com/XLearning-SCU/2025-IJCAI-PML. |
| Open Datasets | Yes | We evaluate our framework on nine publicly available benchmark datasets spanning diverse modalities... These datasets comprise Handwritten[Duin, 1998], MSRC-V11, NUSWIDE-OBJECT2 (NUSOBJ) [Chua et al., 2009], Fashion MV [Xu et al., 2022], Scene153, Land Use [Yang and Newsam, 2010], Leaves1004, MVSA [Niu et al., 2016] and UPMC-Food101 [Wang et al., 2015]. |
| Dataset Splits | No | We report the accuracies on the test set to measure the performance. In our experiments, in addition to the normal experimental setting, we also construct a noise setting following [Xu et al., 2024a] to further evaluate the robustness of our method by introducing data noise and noisy correspondences (misaligned views/modalities) on test sets. The paper mentions using 'test sets' but does not specify exact percentages, sample counts, or a detailed splitting methodology for training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and designing 'multi-layer linear networks' for sub-networks, but it does not specify versions of any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used for implementation. |
| Experiment Setup | Yes | In our experiments, the sub-networks fv and gv, which estimate the mean direction and concentration parameter, respectively, are designed as multi-layer linear networks. We exploit the Adam optimizer with a batch size of 32 to train all models, using a learning rate of 1e-4 for all datasets. |