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From Gaze to Meaning: A Training-Free AI Agent for Unified Grounding and Explanation

Shayan Nasiriboukani, Sara Atito, Mohammad Nezamipour, Muhammad Awais

arXiv:2609.06208v1cs.CV

TL;DR

Existing approaches often separate gaze, grounding, and explanation, limiting unified gaze-guided visual reasoning without extensive supervision. GTA uses pretrained vision-language models, gaze-based visual prompts, and uncertainty-triggered memory retrieval; it reports state-of-the-art performance on GazeFollow and GazeHOI, with richer semantic predictions and open-vocabulary flexibility.

  • Problem

    Existing systems often separate gaze estimation, object grounding, and explanation, and may rely on task-specific supervision and pipelines that generalize poorly.

    Source: §1 · p. 2

  • Method

    GTA prompts pretrained vision-language models with gaze cues and uses memory retrieval to refine predictions when uncertainty is high, without parameter updates.

    Source: §1 · p. 2, §5 · p. 14

  • Results

    GTA achieves state-of-the-art performance on GazeFollow and GazeHOI, with richer semantic interpretations and flexible open-vocabulary outputs.

    Source: §1 · p. 2, §5 · p. 15

  • Takeaways & Limitations

    The results indicate that gaze can guide training-free visual reasoning while improving accuracy and interpretability without additional supervision.

    Source: §5 · p. 15

Abstract

from arXiv · show

Understanding human attention is fundamental for scene interpretation, yet existing approaches often rely on heavily trained models that lack interpretability. Prior methods struggle to jointly reason about gaze targets, attended objects, and visual grounding without extensive supervision. To the best of our knowledge, this work introduces the first training-free Gaze Target Agent (GTA) for gaze-guided reasoning across tasks such as gaze target prediction, attention localization, and object identification. This is achieved by leveraging pretrained vision-language models, augmenting them with visually guided prompts, and employing a memory-based retrieval strategy for high-uncertainty samples to improve performance without additional training. We evaluate our approach using both quantitative metrics and qualitative results. Quantitatively, our method achieves state of the art performance on the GazeFollow and GazeHOI benchmarks. Qualitatively, our agent provides detailed semantic predictions, predicts the correct targets even when ground truth labels are wrong, and remains flexible without vocabulary constraints.

1 Introduction

Gaze is a useful signal for interpreting people and scenes, but existing systems often separate gaze, grounding, and explanation. GTA combines gaze-guided reasoning with pretrained models and uncertainty-triggered retrieval, and reports state-of-the-art gaze-target prediction on GazeFollow and GazeHOI.

Motivation

  • Gaze modeling supports interpretation of behavior, interactions, and joint attention, which can signal communication, collaboration, and social engagement.The introduction describes gaze as part of how observers identify people, infer attention, and determine attended objects or regions.Source: §1 · pp. 1–2
  • Existing systems often separate gaze estimation, object grounding, and explanation, while relying on task-specific supervision and pipelines that may generalize poorly.The introduction also notes that fluent descriptions may be weakly grounded and that object hallucination remains a challenge.Source: §1 · p. 2

Approach

  • GTA uses detected people and zero-shot gaze estimates to prompt a vision-language model, with memory retrieval refining high-uncertainty predictions without parameter updates.Gaze also guides alignment with candidate object regions, supporting grounding and reducing hallucination.Source: §1 · p. 2

Results

  • GTA achieves state-of-the-art gaze-target prediction on GazeFollow and GazeHOI, with richer semantic predictions and flexible outputs without vocabulary constraints.The reported qualitative results also include correct target predictions when ground-truth annotations are inaccurate.Source: §1 · pp. 2–3

2 Related Works

Prior gaze research spans target localization, 3D and end-to-end estimation, social gaze, and vision-language approaches. The paper identifies continuing limits in semantic gaze understanding, including predefined categories and dependence on intermediate object detection.

Gaze Target Prediction

  • Gaze-target methods progressed from scene or activity priors to coordinate and heatmap prediction, then added depth, pose, ocular, and object-aware cues.The cited approaches include two-branch person-specific maps and methods using geometric cues, multimodal fusion, and gaze-cone alignment [44, 11, 12, 23, 24, 45, 51, 52, 71, 72, 4,17,21,32,36,54,20,43,69,6,9–11,18,46,58,73,55,61,74].Source: §2 · p. 3

End-to-End and 3D Gaze Estimation

  • End-to-end set prediction jointly estimates human and gaze instances, while 3D gaze-vector methods provide direction without identifying a target.Contrastive methods seek cross-domain robustness by aligning similar gaze semantics and suppressing identity or quality factors.Source: §2 · pp. 3–4

Social and Multi-Person Gaze

  • Social and multi-person gaze methods model mutual gaze, shared attention, per-person heatmaps, and pairwise social labels using interaction-aware or unified architectures.The cited work includes temporal, people–scene, and spatio-temporal interaction modules.Source: §2 · p. 4

Vision Language Models for Gaze

  • Vision-language models offer open-vocabulary, zero-shot capabilities that can connect gaze targets with descriptive concepts expressed in language.The passage frames this as a way to interpret attention more flexibly and semantically [1] [53].Source: §2 · p. 4

Semantic Gaze Understanding

  • Semantic gaze systems combine gaze with object detection or language models, but remain constrained by predefined categories or intermediate detection pipelines.Examples include joint gaze-location and object-identity prediction, and GazeLLM's zero-shot reasoning over structured scene representations.Source: §2 · p. 4

3 Architecture

GTA combines pretrained components in a gaze-guided pipeline that detects people, estimates attention, reasons about attended objects, and grounds predictions. Visual prompts encode each person's gaze, uncertainty can trigger memory-based refinement, and object grounding aligns candidate regions with gaze.

3 Architecture

  • The architecture integrates gaze estimation, object reasoning, and grounding, using gaze as an attentional cue and a memory mechanism to guide uncertain predictions.Retrieval occurs when uncertainty exceeds a predefined threshold, using relevant examples from stored training data to help resolve ambiguous gaze predictions.Source: §3 · pp. 4–5, Figure 2 · p. 5

3.1 Gaze-Guided Visual Encoding

  • For each detected head, pretrained gaze estimation produces a fixation heatmap or out-of-frame decision, then a blue arrow renders the predicted direction.The system uses a pretrained YOLOv8 head detector and zero-shot Gaze-LLE; a shared DINOv2 scene representation is reused across people. It creates one prompted image per person, preserving gaze cues as interpretable geometry without modifying the VLM.Source: §3.1 · pp. 5–6

3.2 Vision Language Reasoning and Retrieval

  • The VLM produces candidate predictions and logits; the probability of its first generated token defines confidence, and uncertainty is its complement.Retrieval activates when uncertainty exceeds the mean uncertainty of previous predictions. Gaze-conditioned image embeddings are compared with memory embeddings, and the six nearest labeled examples are added as in-context demonstrations without parameter updates.Source: §3.2 · pp. 6–7

3.3 Gaze-Guided Object Grounding

  • Grounding selects the candidate object region with the greatest overlap with the predicted gaze heatmap, helping distinguish instances in cluttered scenes.The paper evaluates pretrained detection and segmentation models; Table 1 compares GGVL and GTA with prior SOTA, reporting Acc@1, Acc@3, and AP@50/mIoU by dataset. Its caption notes that Params denotes learnable parameters.Source: §3.3 · pp. 7–8, Table 1 · p. 8

4 Experiments

The experiments compare GTA with prior methods and its GGVL baseline, then examine retrieval, uncertainty, prompting, and vocabulary choices. Qualitative examples show richer attended-object descriptions, predictions that differ from apparently incorrect labels, and open-vocabulary outputs.

4 Experiments

  • Recognition is evaluated with Acc@1, Acc@3, and MultiAcc@1, while GazeHOI additionally reports grounding by measuring alignment of predicted semantic labels with annotated human-object interaction targets.For localization, RF-DETR generates candidate boxes, and the box overlapping the predicted gaze heatmap most is treated as the gaze-target object. The best-performing ablation configuration supplies the final GTA result.Source: §4.1 · pp. 8–9, §4.2 · p. 9

4.1 Quantitative Evaluation

  • GazeFollow GTA adds 4.3% Top 1, 4.4% Top 3, and 5.2% multi-label accuracy over GGVL; on GazeHOI, Top 3 accuracy is 3.9% higher than baseline.For GazeFollow, GGVL itself improves over previous methods by 0.3% Top 1 and 4.2% Top 3 under the shared target vocabulary.Source: §4.1 · pp. 8–9

4.2 Ablation Study

  • Across public VLM backbones and model sizes, GTA consistently improves over GGVL on both GazeFollow and GazeHOI, with gains also reported for private VLMs.The comparison holds gaze localization fixed to isolate recognition and reasoning; reported backbones include LLaVA, Qwen3, and Gemini 2.5.Source: §4.2 · pp. 9–10, Table 2 · p. 10
  • Dynamic thresholding gives the best Top 1 and multi-label accuracy, while fixed threshold 25 gives the best Top 3 and closely matches dynamic thresholding across metrics.Performance is stable across thresholds except when retrieval is applied to every sample, which can introduce noisy candidates for already-correct predictions.Source: §4.2 · p. 10, Table 3 · p. 11
  • A 5px blue arrow achieves 49.29 top-1 and 55.79 multi-label accuracy, while retrieval with k = 6 gives the best overall retrieval-size performance.Arrow prompts outperform non-arrow prompts, and larger retrieval sets may add less relevant examples.Source: §4.2 · p. 11, Table 4 · p. 12, Table 5 · p. 12
  • Open-vocabulary GTA gains 9.79 Top-1, 10.52 Top-3, and 10.48 MultiAcc@1 points; fixed-vocabulary gains are 3.54, 3.03, and 4.31 points, respectively.With E5 semantic matching, Open + GTA achieves 47.85 Top-1, compared with 51.48 for Fixed + GTA.Source: §4.2 · pp. 11–12, Table 6 · p. 13
  • Fixed threshold 55 triggers retrieval for 729/4781 samples (15.2%); 520 of those samples were originally incorrect, compared with retrieval on about 45% under dynamic thresholding.The fixed-threshold option is described as preserving most of the performance gain while reducing retrieval calls.Source: §4.2 · pp. 12–13

4.3 Qualitative Evaluation

  • Qualitative examples show richer attended-object labels, gaze-target predictions that better match the image than apparently incorrect annotations, and flexible open-vocabulary descriptions.The first two analyses use a predefined vocabulary; the final analysis removes that constraint.Source: §4.3 · p. 13, Figure 3 · p. 14

5 Conclusion

GTA unifies gaze localization, object recognition, grounding, and uncertainty-aware refinement in a training-free pipeline. It achieves state-of-the-art results while producing richer semantic interpretations and supporting open-vocabulary outputs.

5 Conclusion

  • A lightweight memory module activates only for uncertain samples and retrieves similar gaze-conditioned examples for in-context refinement without parameter updates.The pipeline also converts gaze predictions into explicit visual prompts for off-the-shelf vision-language models.Source: §5 · p. 14
  • GTA’s open-vocabulary predictions frequently describe attended objects more precisely than the ground-truth annotations.Source: Figure 4 · p. 15
  • GTA achieves state-of-the-art performance on GazeFollow and GazeHOI while remaining competitive with fine-tuned approaches.Qualitative analyses also show richer gaze-target interpretations, recovery of correct predictions when annotations are wrong, and open-vocabulary outputs when label constraints are removed.Source: §5 · p. 15

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