Variational Amodal Object Completion
Authors: Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, Sanja Fidler
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on complex street scenes demonstrate state-of-the-art performance in amodal mask completion, and showcase high quality scene editing results. |
| Researcher Affiliation | Collaboration | NVIDIA1 University of Toronto2 Vector Institute3 {huling ,dacunamarrer, kkreis, seungwookk, sfidler}@nvidia.com |
| Pseudocode | No | The paper describes the method using text and equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that its source code is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | KINS [27] is a large scale dataset derived from KITTI [10], which contains both instance and amodal annotations. The Cityscapes dataset [7] contains 5,000 images of driving scenes... |
| Dataset Splits | Yes | KINS: The dataset consists of 7,474 images for training and 7,517 images for testing. Following [39], we use the first 10% images from the test set as validation set (750 images in total). Cityscapes: ...including 2,975 images for training, 500 for validation, and 1,525 for testing. |
| Hardware Specification | No | The paper states 'Please refer to supplementary material for training and model implementation details,' but does not provide specific hardware details in the main text. |
| Software Dependencies | No | The paper states 'Please refer to supplementary material for training and model implementation details,' but does not specify software dependencies with version numbers in the main text. |
| Experiment Setup | No | The paper states 'Please refer to supplementary material for training and model implementation details,' and mentions optimizing hyperparameters, but does not provide specific values for hyperparameters or system-level training settings in the main text. |