Eduardo P'erez-Pellitero

TL;DR: This work proposes an invertible model, capable of bidirectional mapping between RAW and RGB domains, that employs end-to-end learning of rich parameter representations that are free from direct parametric supervision and additionally enable simple and plausible data augmentation.

TL;DR: This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022, and the competition set-up, datasets, proposed methods and their results.

TL;DR: A strategy that learns to jointly align and assess the alignment and exposure reliability using an HDR-aware, uncertainty-driven attention map that robustly merges the frames into a single high quality HDR image is introduced.

TL;DR: This work conducts a thorough analysis of why and how object detection models forget catastrophically, and modifications to SOTA distillation based method Faster-ILOD provide consistent improvements, achieving state of the art performance in the class incremental setting, as well as significantly improving domain incremental detection abilities.