Amodal Instance Segmentation via Prior-Guided Expansion
Authors: Junjie Chen, Li Niu, Jianfu Zhang, Jianlou Si, Chen Qian, Liqing Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on KINS, D2SA, and COCOA cls datasets, which show the effectiveness of our method. |
| Researcher Affiliation | Collaboration | Junjie Chen1, Li Niu1*, Jianfu Zhang1, Jianlou Si2, Chen Qian2, Liqing Zhang1* 1 The Mo E Key Lab of AI, CSE department, Shanghai Jiao Tong University 2 Sense Time Research, Sense Time |
| Pseudocode | No | The paper describes the methodology in text and figures, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'We implement the proposed method on the codebase of previous work (Xiao et al. 2021)', but it does not provide an explicit statement about releasing its own source code or a link to a repository. |
| Open Datasets | Yes | We conduct extensive experiments on three datasets: KINS (Qi et al. 2019), D2SA (Follmann et al. 2019), and COCOA cls (Zhu et al. 2017). |
| Dataset Splits | No | The paper uses training instances and mentions training objectives, but it does not specify explicit train/validation/test dataset splits with percentages, counts, or references to predefined splits. |
| Hardware Specification | Yes | We conducted the experiments on Ubuntu 18.04 system with 32 GB Intel 9700K CPU and two NVIDIA 1080ti GPU cards. |
| Software Dependencies | Yes | We implement the proposed method on the codebase of previous work (Xiao et al. 2021), which builds on Detectron2 using Python 3.7 and Py Torch 1.4.0 framework. |
| Experiment Setup | Yes | For Mask R-CNN, we follow the setup in previous works (Xiao et al. 2021; Follmann et al. 2019), e.g., predicting amodal box in its box head, using Res Net-50 (He et al. 2016) as backbone, and using channel size C = 256 and spatial size 14 x 14 for the region feature maps of mask heads. |