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..
Variational Amodal Object Completion
Authors: Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, Sanja Fidler
NeurIPS 2020 | Venue PDF | 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 EMAIL |
| 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 ο¬rst 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. |