Gaze-based applications are increasingly advancing with the availability of large datasets but ensuring data quality presents a substantial challenge when collecting data at scale. It further requires different parties to collaborate, therefore, privacy concerns arise. We propose QualitEye - the first method for verifying image-based gaze data quality. QualitEye employs a new semantic representation of eye images that contains the information required for verification while excluding irrelevant information for better domain adaptation. QualitEye covers a public setting where parties can freely exchange data and a privacy-preserving setting where parties cannot reveal their raw data nor derive gaze features/labels of others with adapted private set intersection protocols. We evaluate QualitEye on the MPIIFaceGaze and GazeCapture datasets and achieve a high verification performance (with a small overhead in runtime for privacy-preserving versions). Hence, QualitEye paves the way for new gaze analysis methods at the intersection of machine learning, human-computer interaction, and cryptography.

We propose GTANet - a novel approach to learning the alignments between human gaze scanpaths and fine-grained task descriptions in vision-language tasks. While the influence of tasks on gaze is known, the relationship between gaze scanpaths and fine-grained task descriptions remains largely unexplored. GTANet addresses this gap by aligning encoded spatiotemporal gaze features with text descriptions. We utilize a Patch-based Gaze Encoder to generate visual-context-aware gaze features, and a Multimodal Feature Mixer to fuse these, capturing cross-modal alignment. To validate the method, we introduce the novel tasks of gaze-based question retrieval and question-based gaze retrieval. Experiments on the AiR and MHUG datasets demonstrate that GTANet significantly and consistently outperforms baseline methods across all Recall@K metrics, achieving a substantial improvement in both gaze-to-task and task-to-gaze retrieval. These results confirm the strong link between human gaze and fine-grained task descriptions, validating the effectiveness of our approach.