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README.md
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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#### Who are the source data producers?
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<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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### Annotations [optional]
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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#### Who are the annotators?
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<!-- This section describes the people or systems who created the annotations. -->
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#### Personal and Sensitive Information
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<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
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**BibTeX:**
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[
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**APA:**
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[
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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Refer to the readme.md at https://github.com/amir-kazemi/aidovecl.
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## Dataset Creation
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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Refer to the manuscript currently under review.
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#### Who are the source data producers?
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<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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Refer to the manuscript currently under review.
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### Annotations [optional]
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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Self-annotated using a method currently under review.
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#### Who are the annotators?
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<!-- This section describes the people or systems who created the annotations. -->
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Self-annotated using a method currently under review.
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#### Personal and Sensitive Information
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<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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NA
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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Our dataset currently cannot outpaint multiple vehicles in a single image due to the limitations of the inpainting model, which struggles with generating coherent scenes with multiple vehicles. This also stems from the challenge of selecting seed images that naturally correlate. Consequently, our dataset lacks rich occlusion scenarios crucial for training robust detection systems. Additionally, our reliance on pretrained detection and inpainting models means that objects not included in the original model training may be missed or appear unrealistic. We also face a shortage of seed images, especially for certain vehicle classes like vans, leading to class imbalances. Addressing these issues is crucial for improving our dataset's effectiveness and the accuracy of detection models.
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### Recommendations
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**BibTeX:**
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[Under Review]
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**APA:**
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[Under Review]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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NA
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## More Information [optional]
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NA
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## Dataset Card Authors [optional]
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Amir Kazemi
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## Dataset Card Contact
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kazemi2@illinois.edu
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