Synchronization issues between audio and video are one of the most disturbing quality defects in film production and live broadcasting. Even a discrepancy as short as 45 milliseconds can degrade the viewer's experience enough to warrant manual quality checks over entire movies. In this paper, we study the automatic discovery of such issues. Specifically, we focus on the alignment of lip movements with spoken words, targeting realistic production scenarios which can include background noise and music, intricate head poses, excessive makeup, or scenes with multiple individuals where the speaker is unknown. Our model's robustness also extends to various media specifications, including different video frame rates and audio sample rates. To address these challenges, we present a model fully based on Transformers that encodes face crops or full video frames and raw audio using timestamp information, identifies the speaker and provides highly accurate synchronization predictions much faster than previous methods.

This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison.