eye-tracking

A Malawian innovator has developed an AI-powered system that allows people with motor disabilities to control computers using eye movements, aiming to provide greater independence without specialized hardware.

Decode by Entropik launches AI Creative Insights, a tool that predicts ad performance using AI-moderated eye tracking and emotion analysis before media spending.

This paper compares gaze behavior in the MapTask and MUNDEX corpora to understand common grounding in collaborative tasks, finding that task-directed gaze is associated with aligned references and understood judgments, but the effects are modest.

This paper evaluates whether Cantonese-adapted language models better predict Cantonese reading using eye-tracking data, comparing models like CKIP GPT-2 and CantoneseLLM-7B. Results indicate that more extensive Cantonese-specific training improves predictive fit, though performance varies by information-theoretic measure.

Matic is a home robot that responds to voice commands and uses eye and finger tracking to locate spots on the floor, demonstrating advanced interaction capabilities.

This paper explores using lightweight webcam-based eye-tracking features to improve keyphrase extraction from Chinese academic abstracts, introducing the CLIS-ET corpus and showing that fixation features enhance KPE performance.

This paper proposes using off-the-shelf CLIP-style multimodal encoders with a bimodal attribution method to predict gaze behavior in visual world experiments, successfully replicating a seminal study on human predictive processing without fine-tuning.

This paper introduces LEXIC, a lightweight method that injects precomputed word-level difficulty signals (GPT-2 surprisal, word frequency, word length) into gaze-only models for reading comprehension prediction, achieving slight improvements over baseline on the EyeBench benchmark.

This paper investigates whether EEG signals can complement eye-tracking signals for automatic keyphrase extraction from microblogs. Using the ZuCo corpus, the authors show that cognitive signals, especially EEG, improve AKE performance across different models.

IXI is showcasing working prototypes of autofocusing lenses that use liquid crystal and eye tracking to automatically switch between prescriptions, aiming to replace multifocal glasses.

This paper explores using a Vision-Language Model (VLM) to detect attention loss in educational videos by combining gaze data with video content, but finds that VLM approaches do not outperform traditional machine learning baselines.

This paper introduces GroupAffect-4, a multimodal dataset of 40 participants in 10 four-person groups performing collaborative tasks. It includes aligned physiology, eye-tracking, audio, self-report, and personality data, along with benchmark targets for within-person, between-person, and group-level analysis.

Researchers probe language model representations to predict human reading times across five languages, finding early layers outperform surprisal for early-pass measures while surprisal remains superior for late-pass measures.

This paper presents the NTIRE 2026 Challenge on Video Saliency Prediction, introducing a novel dataset of 2,000 diverse videos with saliency maps collected via crowdsourced mouse tracking from over 5,000 assessors. Over 20 teams participated, with 7 passing the final phase, and all data is made publicly available.