We anticipate that the proposed dataset and benchmarks will facilitate further research in the field of generative content understanding.

Machine learning models--including prominent zero-shot models--are often trained on datasets whose labels are only a small proportion of a larger label space. Such spaces are commonly equipped with a metric that relates the labels via distances between them.

Many articles in the literature solve the CMDP with an unknown environment.

We provide both theoretical analysis and experimental results to validate the effectiveness of our proposed algorithm.