HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding

• URL: http://arxiv.org/abs/2410.06777v1
• Date: Wed, 9 Oct 2024 11:14:07 GMT
• Title: HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding
• Authors: Keliang Li, Zaifei Yang, Jiahe Zhao, Hongze Shen, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen,
• Abstract summary: We introduce HERM-Bench, a benchmark for evaluating the human-centric understanding capabilities of MLLMs. Our work reveals the limitations of existing MLLMs in understanding complex human-centric scenarios. We present HERM-100K, a comprehensive dataset with multi-level human-centric annotations, aimed at enhancing MLLMs' training.
• Score: 68.4046326104724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The significant advancements in visual understanding and instruction following from Multimodal Large Language Models (MLLMs) have opened up more possibilities for broader applications in diverse and universal human-centric scenarios. However, existing image-text data may not support the precise modality alignment and integration of multi-grained information, which is crucial for human-centric visual understanding. In this paper, we introduce HERM-Bench, a benchmark for evaluating the human-centric understanding capabilities of MLLMs. Our work reveals the limitations of existing MLLMs in understanding complex human-centric scenarios. To address these challenges, we present HERM-100K, a comprehensive dataset with multi-level human-centric annotations, aimed at enhancing MLLMs' training. Furthermore, we develop HERM-7B, a MLLM that leverages enhanced training data from HERM-100K. Evaluations on HERM-Bench demonstrate that HERM-7B significantly outperforms existing MLLMs across various human-centric dimensions, reflecting the current inadequacy of data annotations used in MLLM training for human-centric visual understanding. This research emphasizes the importance of specialized datasets and benchmarks in advancing the MLLMs' capabilities for human-centric understanding.

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