Face-Human-Bench: A Comprehensive Benchmark of Face and Human Understanding for Multi-modal Assistants
This work addresses the need for better assessment tools in the multi-modal assistant community, though it is incremental as it builds on existing datasets and methods.
The authors tackled the lack of comprehensive evaluation for face and human understanding in multi-modal assistants by creating Face-Human-Bench, a benchmark with 1800 problems per set, and evaluated 25 MLLMs to analyze performance correlations and prompting impacts.
Faces and humans are crucial elements in social interaction and are widely included in everyday photos and videos. Therefore, a deep understanding of faces and humans will enable multi-modal assistants to achieve improved response quality and broadened application scope. Currently, the multi-modal assistant community lacks a comprehensive and scientific evaluation of face and human understanding abilities. In this paper, we first propose a hierarchical ability taxonomy that includes three levels of abilities. Then, based on this taxonomy, we collect images and annotations from publicly available datasets in the face and human community and build a semi-automatic data pipeline to produce problems for the new benchmark. Finally, the obtained Face-Human-Bench includes a development set and a test set, each with 1800 problems, supporting both English and Chinese. We conduct evaluations over 25 mainstream multi-modal large language models (MLLMs) with our Face-Human-Bench, focusing on the correlation between abilities, the impact of the relative position of targets on performance, and the impact of Chain of Thought (CoT) prompting on performance. We also explore which abilities of MLLMs need to be supplemented by specialist models. The dataset and evaluation code have been made publicly available at https://face-human-bench.github.io.