metadata
license: apache-2.0
pretty_name: EPIC-Bench
task_categories:
- visual-question-answering
- object-detection
language:
- en
Modalities:
- Image
- Text
tags:
- embodied-perception
- mask-grounding
- vision-language-models
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: metadata.jsonl
🎯 EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models
📃 Overview
📚 EPIC-Bench is a Mask-Grounding-based benchmark designed to evaluate a VLM’s Visual Perception capability in Embodied Scenarios. EPIC-Bench covers 3 High-Level Categories and 23 Task Types, following the realistic Embodied Workflow:
- 🎯 TargetLocalization: Pinpoint the right object in the scene from a natural-language instruction.
- 🧭 Navigation: Approach the target step by step by reading key visual cues along the way.
- 🤲 Manipulation: Operate on the target through fine-grained, action-oriented Grounded Perception.
The goal is to measure whether models can reliably perceive the critical Visual information required throughout the Embodied Process.
Example visualization of EPIC-Bench. For more, visit our Project Page or download the dataset to explore the full benchmark locally.
✨ Highlights
- Embodied-Scenario evaluation of VLM Visual Perception capability.
- Focus on Visual Grounding / Perception without language shortcut exploitation.
- Diverse and Fine-Grained task design.
📰 News
- [2026.5.15] 🚀 HuggingFace and ModelScope Dataset are available!
- [2026.5.15] 🚀 Project Page and Evaluation Code are released, the arXiv paper will come soon.
📋 Todo
- Evaluation code for EPIC-Bench
- The EPIC-Bench datasets
- Make the evaluation pipeline compatible with mask outputs
🏆 Leaderboard and Benchmark
Please refer to the EPIC-Bench Homepage for:
- Leaderboard
- Full dataset downloads
- EPIC-Bench data examples
📚 Citation
@article{EPIC-Bench,
title={EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models},
author={XXX, XXX, XXX},
journal={},
year={2026}
}
📜 License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.