GazeLLM: a plug-and-play zero-shot LLM reasoning framework for boosting gaze target detection

In the gaze behavior understanding task, existing vision-based models demonstrate inherent limitations in high-dimensional semantic understanding, while vision-language models (VLMs) encounter challenges in precise object localization. To address this issue, we propose GazeLLM, the first zero-shot large language model (LLM) boosted framework for gaze target reasoning. Our key innovations include three aspects. First, we have structured object extraction. Using off-the-shelf detectors (e.g., MM-GroundingDINO and Depth Anything V2), we convert images into 3D object representations, including head and gaze direction, object categories, and metric depth. Second, we implemented an autonomous chain-of-thought (CoT) reasoning system. We designed self-generated CoT prompts to guide pretrained LLMs, such as ChatGPT o3-mini-high, to predict gaze targets via spatial-semantic analysis. Third, we proposed a plug-and-play module. We employed a novel cross-modal fusion mechanism that combines the LLM’s probability dictionaries with vision-based gaze heatmaps via Gaussian-weighted multi-hot mapping. Extensive experiments show that GazeLLM significantly improves state-of-the-art models, increasing their performance from 17% to 34% on challenging cases, such as long-range targets or rare categories, without the need for retraining. It also extends seamlessly to multi-person social gaze tasks (e.g., a 42% LAEO AP gain on the AVA-LAEO benchmark). Our framework demonstrates superior generalizability and interpretability compared to VLMs, validating the efficacy of LLMs in understanding gaze behavior by mining semantic cues.