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jylins / videoseek

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[CVPR 2026] VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking

19
2
100% credibility
Found Mar 24, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

VideoSeek is an AI agent that efficiently answers questions about long videos by strategically scanning relevant frames and subtitles instead of processing everything.

How It Works

1
🔍 Discover VideoSeek

You hear about a smart helper that answers questions from videos without watching every second, perfect for quick insights from long clips.

2
đź’» Set up on your computer

Follow simple steps to get the tool ready, like creating a fresh workspace and adding it to your system.

3
đź“ą Pick your video

Choose a video file from your computer or a YouTube link, and add subtitles if available for better understanding.

4
âť“ Ask your question

Type a clear question about what's in the video, like 'What animal is under the sign?' and feel the excitement build.

5
⚡ Let it scan smartly

Run the tool and watch it overview, skim, and focus on key moments to gather clues efficiently.

âś… Get your answer

Receive a spot-on response with reasoning and evidence timestamps, saving you hours of watching.

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AI-Generated Review

What is videoseek?

VideoSeek is a Python CLI tool for querying long videos with natural language questions, using an LLM agent to smartly seek key evidence instead of scanning every frame. Feed it a local video file, YouTube URL, or subtitles, and it outputs a JSON prediction plus a step-by-step reasoning trajectory—processing just 1/300th the frames of rivals for top accuracy on benchmarks like LVBench. Tied to a CVPR 2026 preprint, it's the official impl for efficient video understanding via overview, skim, and focus tools.

Why is it gaining traction?

It crushes inefficient full-video parsing by chaining coarse-to-fine inspections, delivering precise MCQ answers (e.g., "What animal statue...?") with minimal compute—ideal for devs eyeing CVPR 2026 papers on GitHub or CVPR 2026 reddit threads. Customizable via YAML configs or CLI flags like --model_name or --max_steps, it supports any OpenAI-compatible LLM and auto-downloads YouTube vids. Among github cvpr 2024/2025/2026 repos, its agentic workflow stands out for real-world QA without custom training.

Who should use this?

Video AI researchers prototyping long-horizon agents, or backend devs building Q&A over user-uploaded footage like tutorials/security cams. Perfect for CVPR 2026 deadline chasers needing cvpr github template baselines, or teams handling untrimmed lectures/podcasts where subtitles boost recall.

Verdict

Grab it if you're deep in CVPR 2026 reviews/workshops—solid README, one-command install (pip install -e . + ffmpeg), but 19 stars and 1.0% credibility scream early alpha; test on your data before prod. Promising for 2026 video agent hype, lacks tests/broad model support.

(198 words)

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