Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters
Authors: Zheng Wu, Ruiheng Chang, Jiaxu Ma, Cewu Lu, Chi Keung Tang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and comparisons are performed to verify our method. We build a dataset consisting of pixel-level annotated images of HOC. |
| Researcher Affiliation | Academia | 1 Shanghai Jiao Tong University 2 HKUST |
| Pseudocode | No | The paper describes algorithmic steps in paragraph form but does not contain a structured pseudocode or clearly labeled algorithm block. |
| Open Source Code | No | The dataset and codes will be released. |
| Open Datasets | Yes | We build a dataset consisting of pixel-level annotated images of HOC. The dataset and codes will be published with the paper. |
| Dataset Splits | No | The paper mentions evaluating using COCO metrics and provides total instance count for their custom dataset, but it does not specify explicit training, validation, and test splits (e.g., percentages or sample counts) for their custom dataset. |
| Hardware Specification | Yes | on four Titan X GPU. |
| Software Dependencies | No | The paper mentions using Mask R-CNN and fine-tuning VGG-16 and AlexNet models, but it does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We finetune the model for 30,000 iterations with a learning rate of 0.001, minibatch size of 32, momentum of 0.9 and weight decay of 0.01. |