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From Context to Human: A Review of VLM Contextualization in the Recognition of Human States in Visual Data
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DOI：10.3390/math14010175.png)
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Abstract
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This paper presents a narrative review of the contextualization and contribution offered by vision-language models (VLMs) for human-centric understanding in images. Starting from the correlation between humans and their context (background) and by incorporating VLM-generated embeddings into recognition architectures, recent solutions have advanced the recognition of human actions, the detection and classification of violent behavior, and inference of human emotions from body posture and facial expression. While powerful and general, VLMs may also introduce biases that can be reflected in the overall performance. Unlike prior reviews that focus on a single task or generic image captioning, this review jointly examines multiple human-centric problems in VLM-based approaches. The study begins by describing the key elements of VLMs (including architectural foundations, pre-training techniques, and cross-modal fusion strategies) and explains why they are suitable for contextualization. In addition to highlighting the improvements brought by VLMs, it critically discusses their limitations (including human-related biases) and presents a mathematical perspective and strategies for mitigating them. This review aims to consolidate the technical landscape of VLM-based contextualization for human state recognition and detection. It aims to serve as a foundational reference for researchers seeking to control the power of language-guided VLMs in recognizing human states correlated with contextual cues.
Keywords:
vision-language models (VLMs)
contextual bias
image contextualization
multimodal learning
affective computing
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