Winston H. Hsu

TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.

TL;DR: The large-scale concept ontology for multimedia (LSCOM) is the first of its kind designed to simultaneously optimize utility to facilitate end-user access, cover a large semantic space, make automated extraction feasible, and increase observability in diverse broadcast news video data sets.

TL;DR: A novel coding framework called Cross-Age Reference Coding (CARC), which is able to encode the low-level feature of a face image with an age-invariant reference space and can achieve state-of-the-art performance on both the dataset and other widely used dataset for face recognition across age, MORPH dataset.

TL;DR: In the NIST TRECVID-2004 evaluation as discussed by the authors, shot boundary detection, high-level feature detection, story segmentation, and search were all performed by the same team.

TL;DR: Experimental results show that although state-of-the-art methods can achieve competitive performance compared to average human performance, majority votes of several humans can achieve much higher performance on this task and the gap between machine and human would imply possible directions for further improvement of cross-age face recognition in the future.