Generative Active Learning for Long-tailed Instance Segmentation
Authors: Muzhi Zhu, Chengxiang Fan, Hao Chen, Yang Liu, Weian Mao, Xiaogang Xu, Chunhua Shen
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that BSGAL outperforms the baseline approach and effectually improves the performance of longtailed segmentation. |
| Researcher Affiliation | Collaboration | 1Zhejiang University, China 2The University of Adelaide, Australia 3The Chinese University of Hong Kong, China 4Ant Group. |
| Pseudocode | Yes | Algorithm 1 Pipeline for copy-paste baseline |
| Open Source Code | No | The paper does not explicitly state that its own implementation code for BSGAL is open-source or provide a link to a repository for its method. |
| Open Datasets | Yes | We choose LVIS (Gupta et al., 2019) as the dataset for our experiments. [...] In this section, we conduct a toy experiment on CIFAR-10 (Krizhevsky et al., 2009)... |
| Dataset Splits | Yes | We use the official LVIS dataset splits, with about 100,000 images in the training set and 20,000 images in the validation set. |
| Hardware Specification | Yes | And we use 4 Nvidia 4090 GPUs with a batch size of 16 during training. As for Swin-L, [...] And we use 4 Nvidia A100 GPUs with a batch size of 16 for training. |
| Software Dependencies | Yes | We first use Stable Diffusion V1.5 (Rombach et al., 2022a) (SD) as the generative model. |
| Experiment Setup | Yes | For Res Net-50, the maximum training iteration is set to 90,000 [...] The other unspecified parameters also follow the same settings as X-Paste (Zhao et al., 2023), such as the Adam W (Loshchilov and Hutter, 2017) optimizer with an initial learning rate of 1e 4. |