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license: apache-2.0
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# EOC-Bench : Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?

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[![arXiv preprint](https://img.shields.io/badge/arxiv-2506.05287-ECA8A7?logo=arxiv)](https://arxiv.org/abs/2506.05287) 
[![GitHub](https://img.shields.io/badge/%20Git%20Hub-Code-yellow)](https://github.com/alibaba-damo-academy/EOCBench/)
[![Project Page](https://img.shields.io/badge/🌐%20Project-Page-9DC3E6)](https://circleradon.github.io/EOCBench/)
[![Learderboard](https://img.shields.io/badge/🏆%20Leaderboard-Page-96D03A)](https://circleradon.github.io/EOCBench/#leaderboard)


## 🔍 Overview
we introduce <strong>EOC-Bench</strong>, an innovative benchmark designed to systematically evaluate object-centric embodied cognition in dynamic egocentric scenarios.
Specially, <strong>EOC-Bench</strong> features 3,277 meticulously annotated QA pairs categorized into three temporal categories: Past, Present, and Future, covering 11 fine-grained evaluation  dimensions and 3 visual object referencing types.
To ensure thorough assessment, we develop a  mixed-format human-in-the-loop annotation framework with four types of questions and design a novel multi-scale temporal accuracy metric for open-ended temporal evaluation. 

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    <img src="https://cdn-uploads.huggingface.co/production/uploads/64a3fe3dde901eb01df12398/gJb0lE0mi6EskZQ8H0Qsm.png" width="100%" style="margin-bottom: 0.2;"/>
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## 📚 Tasks Definition
EOC-Bench structures questions into three temporally grounded categories: **Past, Present, and Future**, with a total of **11** categories.

![data.png](https://cdn-uploads.huggingface.co/production/uploads/64a3fe3dde901eb01df12398/wDwXgMA6UNyvtdWhqqzq9.png)

### 📈 Evaluation
Please see our [GitHub](https://github.com/alibaba-damo-academy/EOCBench/).