yangruoliu

yangruoliu / VideoDetective

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VideoDetective: Clue Hunting via both Extrinsic Query and Intrinsic Relevance for Long Video Understanding

videodetective.github.io
47
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100% credibility
Found Mar 24, 2026 at 47 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

VideoDetective is a research tool that answers multiple-choice questions about long videos by intelligently locating and analyzing relevant segments.

How It Works

1
🔍 Discover VideoDetective

You hear about a smart tool that can watch long videos and answer your questions by finding the key moments.

2
💻 Get ready on your computer

You download the simple program and prepare a few everyday tools like a video player helper.

3
🔗 Link your AI helper

You connect a smart AI service so the tool can understand videos and think like a detective.

4
📹 Choose your video and question

You pick a video file and type a question with answer choices, like 'What is the person doing? A. Running B. Walking'.

5
🕵️ Let the detective search

You start the tool and watch it cleverly scan the video, hunting for clues step by step to find the right spots.

✅ Get your answer and proof

You receive the best answer with a picture showing belief changes over time, plus full details to see exactly why.

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Star Growth

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AI-Generated Review

What is VideoDetective?

VideoDetective is a Python inference framework that boosts multimodal LLMs for long video understanding and question answering. It hunts clues via both extrinsic query relevance and intrinsic video structure, smartly selecting sparse key frames to fit tight context windows—letting models "see less but know more." Run it via a simple CLI script or core API with your OpenAI-compatible VLM like Qwen-VL, feeding in a video path and query for JSON results with belief visualizations.

Why is it gaining traction?

It stands out by fusing query-driven search with video graph affinities in an iterative loop, delivering consistent gains across mainstream VLMs on VideoQA benchmarks. Devs love the plug-and-play setup: copy a .env template, add your API key, and test on any MP4—no heavy training needed. Outputs include predictions, debug traces, and heatmaps tracking relevance propagation.

Who should use this?

Video ML engineers benchmarking VLMs on long-form QA tasks like Video-MME. App devs prototyping interactive video analysis tools, such as forensic clue hunting in surveillance footage or educational content querying. Anyone hitting MLLM context limits on hour-long videos.

Verdict

Worth a quick test for long video QA—strong docs, runnable demo, and arXiv paper make it accessible despite 47 stars and 1.0% credibility score. Early-stage maturity means watch for updates, but the Python API lowers barriers to experimenting with structured video relevance.

(187 words)

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