MaskBooster: End-to-End Self-Training for Sparsely Supervised Instance Segmentation
Authors: Shida Zheng, Chenshu Chen, Xi Yang, Wenming Tan
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 {zhengshida,chenchenshu,yangxi6,tanwenming}@hikvision.com |
| 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. |