arXiv Machine Learning
Oct 16

DuET: Note-Frame Dual Encoding for Piano Performance-to-Score Transcription

DuET is a Dual-Encoder Transformer that jointly learns note-level piano performance-to-score transcription and frame-level beat/downbeat tracking. It uses bidirectional cross‑attention between note and frame encoders and an autoregressive decoder that alternates between their representations to predict structured score attributes. The model introduces TimeRoPE and Time‑Pitch Rotary Positional Encoding (TPRoPE) to share a performance‑time reference and encode relative temporal and pitch relationships, achieving significant error reductions on ASAP and Gangqinpu datasets.

By Wei Zeng, Ye Wang
arXiv AI
Oct 16

InkSight: Offline-to-Online Handwriting Conversion by Teaching Vision-Language Models to Read and Write

InkSight is a system that converts offline handwritten notes captured in photos into digital ink, enabling seamless transition from pen-and-paper to editable, vectorized formats. Unlike prior methods that rely heavily on geometric image properties, InkSight integrates reading and writing priors, allowing it to learn from limited paired data. The model successfully derenders handwritten text in diverse photos and even simple sketches, with human evaluations showing 87% of outputs as valid tracings and 67% resembling human pen trajectories.

By Blagoj Mitrevski, Arina Rak, Julian Schnitzler, Chengkun Li, Andrii Maksai, Jesse Berent, Claudiu Musat