Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling
Authors: Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, Qibin Zhao
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Various experiments demonstrate that it can achieve the state-of-the-art performance. |
| Researcher Affiliation | Academia | 1 Tensor Learning Unit, Center for Advanced Intelligence Project, RIKEN, Japan 2 College of Computer Science, Hangzhou Dianzi University, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | Datasets. CMU-MOSI dataset [30] consists of 2, 199 opinion video clips from You Tube movie reviews... IEMOCAP dataset [2] contains a total number of 302 videos. |
| Dataset Splits | Yes | CMU-MOSI dataset... There are 1, 284 segments in the train set, 229 segments in the validation set and 686 segments in the test set. IEMOCAP dataset... The division of the train, validation and test sets is 6, 373, 1, 775 and 1, 807, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Implementation details. Following LMF [17], we use CP format as the workhorse low-rank TN in our experiments for weight compression in PTP. The candidate CP ranks are {1, 4, 8, 16}. |