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MAVEN pipeline automates video reasoning data annotation

By PulseAugur Editorial · [3 sources] ·

Researchers have developed MAVEN, an agentic pipeline designed to automate the creation of high-quality structured annotations for video reasoning tasks. This pipeline synthesizes multi-scale event descriptions and supports agent-driven domain adaptation, allowing it to redesign prompts and pipeline structures without manual intervention. MAVEN was used to label over 5,300 traffic videos, and fine-tuning a model called Cosmos-Reason2-8B on this data resulted in performance surpassing Gemini 2.5 Pro and 3.1 Flash on specific evaluation sets. AI

IMPACT Automates video data annotation, potentially accelerating VLM training and improving performance on complex reasoning tasks.

RANK_REASON The cluster describes a new research paper detailing an automated annotation pipeline for video reasoning tasks.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Wenqi Liu, Yunxiao Wang, Shijie Ma, Meng Liu, Qile Su, Tianke Zhang, Haonan Fan, Changyi Liu, Kaiyu Jiang, Jiankang Chen, Kaiyu Tang, Bin Wen, Fan Yang, Tingting Gao, Han Li, Yinwei Wei, Xuemeng Song ·

    VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos

    arXiv:2602.07801v4 Announce Type: replace-cross Abstract: In long-video understanding, conventional uniform frame sampling often fails to capture key visual evidence, leading to degraded performance and increased hallucinations. To address this, recent agentic thinking-with-video…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks

    Training Vision Language Models (VLMs) for video event reasoning requires high-quality structured annotations capturing not only what happened, but when, where, why, and with what consequence, at a scale manual labelling cannot support. We present MAVEN (Multi-stage Agentic Video…

  3. arXiv cs.CV TIER_1 English(EN) · Han Zhang, Wanting Jiang, Tomasz Kornuta, Tian Zheng, Vidya Murali ·

    MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks

    arXiv:2605.21917v1 Announce Type: new Abstract: Training Vision Language Models (VLMs) for video event reasoning requires high-quality structured annotations capturing not only what happened, but when, where, why, and with what consequence, at a scale manual labelling cannot supp…

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