license: other
license_name: multiple-original-licenses
license_link: LICENSE
task_categories:
- zero-shot-image-classification
VL-Compositionality-Benchmarks
This repository contains a collection of evaluation benchmarks used to assess the compositional understanding of Vision-Language Models (VLMs), as presented in the paper Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality.
Official GitHub Repository: hiker-lw/MACCO
Dataset Summary
These benchmarks are designed to evaluate how well models like CLIP capture object relations, attribute-object bindings, and word order dependencies, moving beyond "bag-of-words" behavior.
The collection includes the following benchmarks:
- ARO (Attributes, Relations, and Order)
- VL-Checklist (including HAKE, SWiG, and VG)
- Sugar-Crepe
- VALSE
- What's Up
Data Preparation
According to the official repository, datasets should be organized under a datasets/ directory.
Notes for VL-Checklist
The VL-Checklist files are split into multiple parts and must be concatenated before extraction. You can use the following commands:
cat hake.tar.gz.part-* > hake.tar.gz
cat swig.tar.gz.part-* > swig.tar.gz
cat vg.tar.gz.part-* > vg.tar.gz
After extraction, the expected structure for VL-Checklist is:
datasets/
└── VL_checklist/
├── VL_checklist_datasets/
│ ├── data/
│ ├── hake/
│ ├── swig/
│ └── vg/
└── VL_checklist_json_data/
Citation
@misc{li2026crossmodalmaskedcompositionalconcept,
title={Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality},
author={Wei Li and Zhen Huang and Xinmei Tian},
year={2026},
eprint={2606.13288},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.13288},
}