Tag
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.