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..
MaskBooster: End-to-End Self-Training for Sparsely Supervised Instance Segmentation
Authors: Shida Zheng, Chenshu Chen, Xi Yang, Wenming Tan
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Abundant experiments are conducted on COCO and BDD100K datasets and validate the effectiveness of Mask Booster. |
| Researcher Affiliation | Industry | Hikvision Research Institute EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using the MMDetection toolbox, but does not provide a specific statement or link for the open-source code of Mask Booster or its methodology. |
| Open Datasets | Yes | Abundant experiments are conducted on COCO and BDD100K datasets... COCO (Lin et al. 2014) 0.1%/1%/10% protocols and BDD100K (Yu et al. 2020). |
| Dataset Splits | Yes | COCO To fully assess an approach of Sp SIS, we randomly sample a ratio of ρ instances in COCO train2017 keeping GT masks while removing GT masks for the rest of the instances. Three datasets are constructed: COCO 0.1%, COCO 1% and COCO 10% with ρ = 0.1%/1%/10%, which have 880, 8656 and 86k GT masks, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only mentioning general settings without specification. |
| Software Dependencies | No | The paper mentions using the MMDetection toolbox but does not provide specific version numbers for it or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | The optimizer we use is SGD with a momentum of 0.9. EMA ratio is set as α = 1e 3. The loss weights for pseudo masks are λh = 1 and λs = 10. For COCO 0.1%, due to the extremely limited GT masks, we set λh = 0.2 and apply Copy Paste (Ghiasi et al. 2021). All experiments are under multi-scale and 3 training schedule. |