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 | Conference PDF | Archive PDF | Plain Text | 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 xinyi@smail.nju.edu.cn, {jeffdu, albertqwang, xavierzwlin, kerwinyan}@tencent.com |
| 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. |