@inproceedings{zhao-etal-2025-venus,
title = "{VENUS}: A {VLLM}-driven Video Content Discovery System for Real Application Scenarios",
author = "Zhao, Minyi and
Liu, Yi and
Wen, Jianfeng and
Zhang, Boshen and
Chang, Hailang and
Ouyang, Zhiheng and
Wang, Jie and
He, Wensong and
Zhou, Shuigeng",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.4/",
doi = "10.18653/v1/2025.emnlp-industry.4",
pages = "50--64",
ISBN = "979-8-89176-333-3",
abstract = "Video Content Discovery (VCD) is to identify the specific videos defined by a certain pre-specified text policy (or constraint), which plays a crucial role in building a healthy and high-quality Web content ecology. Currently, related works typically employ multiple classifiers or similarity-based systems to support VCD. However, these approaches are difficult to manage, lack generalization power, and suffer from low performance. To tackle these problems, this paper presents a new Vision-Language Large Model (VLLM)-driven VCD system called VENUS (the abbreviation of Video contENt UnderStander). Concretely, we first develop an automatic policy-guided sequential annotator (APSA) to generate high-quality, VCD-specific, and reasoning-equipped instruct-tuning data for model training, then extend the VLLM inference to support VCD better. Following that, we construct a real VCD test set called VCD-Bench, which includes a total of 13 policies and 57K videos. Furthermore, to evaluate its practical efficacy, we deploy VENUS in three different real scenarios. Extensive experiments on both the VCD-Bench and public evaluation datasets for various VCD-related tasks demonstrate the superiority of VENUS over existing baselines."
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<abstract>Video Content Discovery (VCD) is to identify the specific videos defined by a certain pre-specified text policy (or constraint), which plays a crucial role in building a healthy and high-quality Web content ecology. Currently, related works typically employ multiple classifiers or similarity-based systems to support VCD. However, these approaches are difficult to manage, lack generalization power, and suffer from low performance. To tackle these problems, this paper presents a new Vision-Language Large Model (VLLM)-driven VCD system called VENUS (the abbreviation of Video contENt UnderStander). Concretely, we first develop an automatic policy-guided sequential annotator (APSA) to generate high-quality, VCD-specific, and reasoning-equipped instruct-tuning data for model training, then extend the VLLM inference to support VCD better. Following that, we construct a real VCD test set called VCD-Bench, which includes a total of 13 policies and 57K videos. Furthermore, to evaluate its practical efficacy, we deploy VENUS in three different real scenarios. Extensive experiments on both the VCD-Bench and public evaluation datasets for various VCD-related tasks demonstrate the superiority of VENUS over existing baselines.</abstract>
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%0 Conference Proceedings
%T VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios
%A Zhao, Minyi
%A Liu, Yi
%A Wen, Jianfeng
%A Zhang, Boshen
%A Chang, Hailang
%A Ouyang, Zhiheng
%A Wang, Jie
%A He, Wensong
%A Zhou, Shuigeng
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F zhao-etal-2025-venus
%X Video Content Discovery (VCD) is to identify the specific videos defined by a certain pre-specified text policy (or constraint), which plays a crucial role in building a healthy and high-quality Web content ecology. Currently, related works typically employ multiple classifiers or similarity-based systems to support VCD. However, these approaches are difficult to manage, lack generalization power, and suffer from low performance. To tackle these problems, this paper presents a new Vision-Language Large Model (VLLM)-driven VCD system called VENUS (the abbreviation of Video contENt UnderStander). Concretely, we first develop an automatic policy-guided sequential annotator (APSA) to generate high-quality, VCD-specific, and reasoning-equipped instruct-tuning data for model training, then extend the VLLM inference to support VCD better. Following that, we construct a real VCD test set called VCD-Bench, which includes a total of 13 policies and 57K videos. Furthermore, to evaluate its practical efficacy, we deploy VENUS in three different real scenarios. Extensive experiments on both the VCD-Bench and public evaluation datasets for various VCD-related tasks demonstrate the superiority of VENUS over existing baselines.
%R 10.18653/v1/2025.emnlp-industry.4
%U https://aclanthology.org/2025.emnlp-industry.4/
%U https://doi.org/10.18653/v1/2025.emnlp-industry.4
%P 50-64
Markdown (Informal)
[VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios](https://aclanthology.org/2025.emnlp-industry.4/) (Zhao et al., EMNLP 2025)
ACL
- Minyi Zhao, Yi Liu, Jianfeng Wen, Boshen Zhang, Hailang Chang, Zhiheng Ouyang, Jie Wang, Wensong He, and Shuigeng Zhou. 2025. VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 50–64, Suzhou (China). Association for Computational Linguistics.