V-Agent: An Interactive Video Search System Using Vision-Language Models
By: SunYoung Park, Jong-Hyeon Lee, Youngjune Kim, Daegyu Sung, Younghyun Yu, Young-rok Cha, Jeongho Ju
Published: 2025-12-22
View on arXiv →Abstract
We introduce V-Agent, a novel multi-agent platform designed for advanced video search and interactive user-system conversations. By fine-tuning a vision-language model (VLM) with a small video preference dataset and enhancing it with a retrieval vector from an image-text retrieval model, we overcome the limitations of traditional text-based retrieval systems in multimodal scenarios. This system significantly improves the accuracy and interactivity of video content discovery and management, making complex video searches more intuitive and efficient for users.
Impact
practical
Topics
5
💡 Simple Explanation
Imagine a smart assistant that can watch hours of video for you. Instead of just searching for keywords like 'dog', you can tell it: 'Find the scene where the dog chases the cat, but only after the car drives away.' V-Agent understands this complex request, scans the video, checks specific moments to be sure, and can even ask you questions to clarify if it's unsure. It turns video search into a conversation.
🎯 Problem Statement
Traditional video search relies on metadata or simple visual matching, which fails when users have complex, multi-part, or vague queries (e.g., specific actions, sequences of events, or abstract concepts). Users struggle to translate their intent into a query the system understands.
🔬 Methodology
The system utilizes a hierarchical agent approach. A 'Planner' LLM decomposes the user's natural language query into actionable steps. A 'Retriever' fetches candidate video segments using vector similarity (CLIP embeddings). A 'Verifier' (VLM) inspects these segments visually to confirm fine-grained details. The system maintains a dialogue history, allowing the user to refine the search or the agent to ask for help when confidence is low.
📊 Results
V-Agent demonstrates superior performance over baseline methods (like direct CLIP retrieval or non-interactive QA) on benchmarks such as Ego4D or NExT-QA. It specifically excels in 'temporal localization accuracy' and 'success rate on multi-hop queries', reducing false positives by verifying candidates before presenting them to the user.
✨ Key Takeaways
The future of search is agentic and interactive. Static indexing is insufficient for the nuance of video data. By combining the planning capability of LLMs with the visual understanding of VLMs, we can solve the 'long-tail' of search queries, provided we can manage the inference costs.
🔍 Critical Analysis
The paper presents a solid advancement in semantic video retrieval by moving beyond simple embedding similarity. The use of an agentic framework allows for handling much higher complexity in user queries. However, the system's reliance on iterative VLM calls introduces significant latency and cost barriers that are not fully addressed. While 'interactive' is a key feature, in practice, users often prefer instant results over a conversation with a search engine. The success of V-Agent depends heavily on the speed/cost evolution of the underlying VLMs.
💰 Practical Applications
- Freemium SaaS platform for content creators to index their raw footage.
- Enterprise licensing for security firms needing semantic surveillance search.
- API access charged per video-minute indexed and per search query.