Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
Authors: Rakshith R. Shetty, Mario Fritz, Bernt Schiele
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,017 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,471 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show on two datasets that our method effectively removes a wide variety of objects using weak supervision only. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Informatics, Saarland Informatics Campus 2CISPA Helmholtz Center i.G., Saarland Informatics Campus Saarbrücken, Germany |
| Pseudocode | No | The paper describes the model and optimization process in text and equations but does not include any clearly labeled “Pseudocode” or “Algorithm” blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | we train and test our model mainly on the COCO dataset [9]... We use the segmentation masks from Pascal-VOC 2012 dataset [27]... We use the Flickr Logos dataset [28] |
| Dataset Splits | No | The paper mentions training and testing on datasets like COCO, Pascal-VOC, and Flickr Logos, but it does not specify explicit dataset splits (e.g., percentages or counts for training, validation, and test sets) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., libraries or frameworks like PyTorch or TensorFlow versions, or specific solver versions) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the loss functions and optimization strategy (“optimized in alternate epochs using gradient descent”) but does not provide specific concrete hyperparameter values (e.g., learning rates, batch sizes, values for λc, λp, etc.) or detailed training configurations in the main text. |