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..
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding
Authors: Yi Xin, Junlong Du, Qiang Wang, Zhiwen Lin, Ke Yan
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four dense scene understanding tasks demonstrate the superiority of VMT-Adapter(-Lite), achieving a 3.96% (1.34%) relative improvement compared to singletask full fine-tuning, while utilizing merely 1% (0.36%) trainable parameters of the pre-trained model. |
| Researcher Affiliation | Collaboration | Yi Xin1,2, Junlong Du2, Qiang Wang2, Zhiwen Lin2, Ke Yan2* 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2Youtu Lab, Tencent EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods using equations and prose, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement or link regarding open-source code for the VMT-Adapter method is provided in the paper. |
| Open Datasets | Yes | To evaluate our proposed approach for multi-task dense scene understanding, we follow the prior works (Vandenhende et al. 2021; Liu et al. 2022) and conduct experiments on the PASCALContext (Vandenhende, Georgoulis, and Van Gool 2020) dataset. |
| Dataset Splits | Yes | PASCAL-Context comprises 4,998 and 5,105 images in the training and testing splits, respectively. |
| Hardware Specification | Yes | We conduct all experiments using the Py Torch toolkit on 4 NVIDIA V100 GPUs. |
| Software Dependencies | No | We conduct all experiments using the Py Torch toolkit on 4 NVIDIA V100 GPUs. (PyTorch is mentioned, but without a specific version number). |
| Experiment Setup | Yes | Specifically, we use batch size 12 and train for 60 epochs for each task. We employ the Adam optimizer with a learning rate 1e 4 and a weight decay 1e 4, and the learning rate is linearly decreased with respect to the iteration. |