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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Tailor Versatile Multi-Modal Learning for Multi-Label Emotion Recognition
Authors: Yi Zhang, Mingyuan Chen, Jundong Shen, Chongjun Wang9100-9108
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we conduct experiments on the benchmark MMER dataset CMU-MOSEI in both aligned and unaligned settings, which demonstrate the superiority of TAILOR over the state-of-the-arts.In this section, we give empirically evaluations and analysis of our proposed TAILOR |
| Researcher Affiliation | Academia | State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China EMAIL, {chjwang}@nju.edu.cn |
| Pseudocode | No | The paper includes mathematical equations, but it does not present any pseudocode or algorithm blocks with structured steps formatted like code. |
| Open Source Code | Yes | 2https://github.com/kniter1/TAILOR |
| Open Datasets | Yes | We conduct experiments on benchmark multimodal multi-label dataset CMU-MOSEI (Zadeh et al. 2018c) |
| Dataset Splits | No | Table 1 summarizes details of CMU-MOSEI in both word-aligned and unaligned settings. While the paper mentions using CMU-MOSEI, a benchmark dataset, it does not explicitly provide the specific training/validation/test splits (e.g., percentages or sample counts) used for reproducibility. It only lists modality dimensions and sequence lengths. |
| Hardware Specification | Yes | All experiments are running with one GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions that parameters are optimized by Adam (Kingma and Ba 2015), but it does not provide specific version numbers for any software components, libraries, or programming languages used. |
| Experiment Setup | Yes | We set hyper-parameters α = 0.01, β = 5e 6 and γ = 0.5. The batch size is 64. For layer number in Transformer Encoder, we set nv = na = 4, nt = 6 in uni-modal encoders, nc = 3 in cross-modal encoders. The size of hidden layers in encoders and decoder is d = 256, the head number hl = hm = 8. All parameters in TAILOR are optimized by Adam (Kingma and Ba 2015) with an initial learning rate of 1e 5 for aligned setting, 1e 4 for unaligned setting and employ a liner decay learning rate schedule with a warm-up strategy. |