Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
Authors: Yanpeng Sun, Qiang Chen, Xiangyu He, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Jian Cheng, Zechao Li, Jingdong Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our Singular Value Fine-tuning (SVF) approach on various few-shot segmentation methods with different backbones. We achieve state-of-the-art results on both Pascal-5i and COCO-20i across 1-shot and 5-shot settings. |
| Researcher Affiliation | Collaboration | Yanpeng Sun1 , Qiang Chen2 , Xiangyu He3 , Jian Wang2, Haocheng Feng2 Junyu Han2, Errui Ding2, Jian Cheng3, Zechao Li1 , Jingdong Wang2 1School of Computer Science and Engineering, Nanjing University of Science and Technology 2Baidu VIS 3NLPR, Institute of Automation, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 Pseudocode of SVF in Python style |
| Open Source Code | Yes | The source code and models will be available at https://github.com/syp2ysy/SVF. |
| Open Datasets | Yes | Experiments are conducted on Pascal-5i[29] and COCO-20i [24]. Following the previous work [23, 35, 29], we separate all classes in both datasets into 4 folds. |
| Dataset Splits | Yes | In this task, datasets are split into the training set (Dtrain) with base classes (Ctrain) and the testing set (Dtest) with novel classes (Ctest)... |
| Hardware Specification | Yes | All model runs on four NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions using an "SGD optimizer" but does not specify version numbers for any software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | We use SGD optimizer with cosine Learning rate decay [21], the learning rate 0.015 and the random seed 321 when fine-tuning backbone. ... All models are trained 200 epochs on Pascal-5i with batch size 8 and trained 50 epochs on COCO-20i with batch size 8. Image is resized to 473 473 on Pascal-5i and 641 641 on COCO-20i. |