ARGUS: Hallucination and Omission Evaluation in Video-LLMs
Paper • 2506.07371 • Published • 8
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Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
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jwt=token,
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File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
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File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
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File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
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options=merged_options,
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File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
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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
Other links - Website Paper Code
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
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