Furthermore, via experiments on the NYUv2 and IBims-1 datasets, we demonstrate that these enhanced representations translate to performance improvements in both the in-distribution and out-of-distribution settings.
Ranked #2 on Monocular Depth Estimation on NYU-Depth V2
Based on this notion, we propose a compatibility learning framework, a category-aware Flexible Bidirectional Transformer (FBT), for visual "scene-based set compatibility reasoning" with the cross-domain visual similarity input and auto-regressive complementary item generation.
Taking this advantage, we synthesize a photo-realistic image by combining the structure of a sketch and the visual style of a reference photo.
2 code implementations • 16 Oct 2020 • Dylan Sheldon Rankin, Jeffrey Krupa, Philip Harris, Maria Acosta Flechas, Burt Holzman, Thomas Klijnsma, Kevin Pedro, Nhan Tran, Scott Hauck, Shih-Chieh Hsu, Matthew Trahms, Kelvin Lin, Yu Lou, Ta-Wei Ho, Javier Duarte, Mia Liu
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years.
Computational Physics Distributed, Parallel, and Cluster Computing High Energy Physics - Experiment Data Analysis, Statistics and Probability Instrumentation and Detectors