{"ID":6536342,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-14T05:36:24.914033594Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10130","arxiv_id":"2607.10130","title":"TextGaze: Prompting Gaze Target Estimation with Textual Scene Cues","abstract":"Gaze target estimation aims to infer the position of a person's gaze within a scene. Within mainstream design logic, multi-branch methods require extra supervision and annotations, while streamlined designs prioritize low-level visual saliency over true gaze intent. The former leads to a high annotation burden and hinders domain transfer, whereas the latter causes misalignment between predicted attention and actual gaze targets. To address this issue, we propose TextGaze, a unified cross-modal architecture that leverages a Large Vision-Language Model (LVLM) as scalable semantic guidance to balance the two design paradigms. The model extracts visual features from a frozen encoder and utilizes an LVLM to obtain gaze-aligned textual cues. We design a transformer-based fusion module with hierarchical text supervision to preserve task semantics. Lightweight decoding heads enable the joint prediction of gaze heatmaps and in-/out-of-frame status. We evaluate our method on four mainstream datasets, and the results show competitive performance across key metrics with robust cross-dataset generalisation without extra fine-tuning. Overall, we provide a streamlined alternative to traditional designs and highlight the potential of LVLMs as accessible auxiliary guidance for gaze estimation.","short_abstract":"Gaze target estimation aims to infer the position of a person's gaze within a scene. Within mainstream design logic, multi-branch methods require extra supervision and annotations, while streamlined designs prioritize low-level visual saliency over true gaze intent. The former leads to a high annotation burden and hind...","url_abs":"https://arxiv.org/abs/2607.10130","url_pdf":"https://arxiv.org/pdf/2607.10130v1","authors":"[\"Junhui She\",\"Fei Wang\",\"Kun Li\",\"Yiqi Nie\",\"Yuxin Liu\",\"Zhangling Duan\",\"Xun Yang\"]","published":"2026-07-11T05:40:45Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\",\"Language Model\"]","has_code":false}