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license: cc-by-4.0 |
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# UAL-Bench: The First Comprehensive Unusual Activity Localization Benchmark |
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[](https://arxiv.org/abs/2410.01180) |
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 |
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[](https://github.com/Hasnat79/UAL_Bench) |
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## UAL Bench Datasets |
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We introduce UAL-Bench, a comprehensive benchmark for unusual activity localization, featuring three video datasets: UAG-OOPS, UAG-SSBD, UAG-FunQA, and an instruction-tune dataset: OOPS-UAG-Instruct. |
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## Usage |
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- Go to Files and versions |
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- download the tar file |
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- untar the file. For example: |
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```bash |
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tar -xvf uag_oops.tar |
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``` |
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- Follow the [project repository](https://github.com/Hasnat79/UAL_Bench) for more details including loading the data in your project, implementing VLM-LLM approach etc. |
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