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..
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 | Venue PDF | 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. |