Circular-based Relation Probing Evaluation (CRPE)

CRPE is a benchmark designed to quantitatively evaluate the object recognition and relation comprehension ability of models. The evaluation is formulated as single-choice questions.

The benchmark consists of four splits: Existence, Subject, Predicate, and Object.

The Existence split evaluates the object recognition ability while the remaining splits are designed to evaluate the capability of relation comprehension, focusing on probing each of the elements in the relation triplets (subject, predicate, object) separately. Some data examples are shown below.

Additionally, to evaluate the dependency on language priors, we also include abnormal data in our evaluation. These images in these abnormal data depict relation triplets that are very rare in the real world.

For a robust evaluation, we adopt CircularEval as our evaluation strategy. Under this setting, a question is considered as correctly answered only when the model consistently predicts the correct answer in each of the N iterations, with N corresponding to the number of choices. In each iteration, a circular shift is applied to both the choices and the answer to form a new query for the model.

CRPE contains the following files:

• crpe_exist.jsonl: the evaluation data of Existence split.
• crpe_exist_meta.jsonl: the evaluation data of Existence split without CircularEval.
• crpe_relation.jsonl: the evaluation data of Subject, Predicate, and Object split.
• crpe_relation_meta.jsonl: the evaluation data of Subject, Predicate, and Object split without CircularEval.

NOTE: You should use crpe_exist.jsonl and crpe_relation.jsonl for evaluation. The evaluation script is presented here.

See our project to learn more details!

Citation

If you find our work useful in your research, please consider cite:

@article{wang2023allseeing,
  title={The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World},
  author={Wang, Weiyun and Shi, Min and Li, Qingyun and Wang, Wenhai and Huang, Zhenhang and Xing, Linjie and Chen, Zhe and Li, Hao and Zhu, Xizhou and Cao, Zhiguo and others},
  journal={arXiv preprint arXiv:2308.01907},
  year={2023}
}
@article{wang2024allseeing_v2,
  title={The All-Seeing Project V2: Towards General Relation Comprehension of the Open World},
  author={Wang, Weiyun and Ren, Yiming and Luo, Haowen and Li, Tiantong and Yan, Chenxiang and Chen, Zhe and Wang, Wenhai and Li, Qingyun and Lu, Lewei and Zhu, Xizhou and others},
  journal={arXiv preprint arXiv:2402.19474},
  year={2024}
}