Update README.md

README.md

@@ -16,4 +16,50 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+pretty_name: argus
+license: cc-by-nc-sa-4.0
+task_categories:
+- video-text-to-text
+language:
+- en
 ---
+
+## ARGUS: Hallucination and Omission Evaluation in Video-LLMs
+
+ARGUS is a framework to calculate the degree of hallucination and omission in free-form video captions.
+
+* **ArgusCost‑H** (or Hallucination-Cost) — degree of hallucinated content in the video-caption
+* **ArgusCost‑O** (or Omission-Cost) — degree of omitted content in the video-caption
+
+Lower values indicate better "performance".
+
+If you have any comments or questions, reach out to: [Ruchit Rawal](https://ruchitrawal.github.io/)
+
+Other links - [Website](https://ruchitrawal.github.io/argus/)   [Paper]()   [Code](https://github.com/JARVVVIS/argus)
+
+## Dataset Structure
+
+Each row in the dataset consists of the name of the video-clip i.e. `clip_name` (dtype: str), and the corresponding `human_caption` (dtype: str). Download all the clips from [here]()
+
+### Loading the dataset
+You can load the dataset easily using the Datasets library:
+```
+from datasets import load_dataset
+dataset = load_dataset("tomg-group-umd/argus")
+```
+
+### Cite us:
+
+TODO
+
+### Acknowledgements
+
+The clips are collected from three primary sources: First, we utilize existing video understanding datasets [1] that already contain captions. These videos are manually verified by human authors, and received well in the community.
+Second, we incorporate text-to-video generation datasets [2,3], which include reference videos and short prompts. Since these prompts are insufficient for dense captioning, we manually annotate 10 such videos.
+Lastly, the authors curate additional videos from publicly available sources, such as YouTube, under Creative Commons licenses. We curate 30 such videos, and also manually annotated , with cross-validation among the authors.
+
+[1] AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark
+
+[2] TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation
+
+[3] https://huggingface.co/datasets/finetrainers/cakeify-smol