Search Results for author:
Found 2 papers, 1 papers with code
Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection
We address this challenging setting by presenting an object detection algorithm able to exploit a pre-trained source model and perform unsupervised adaptation by using only one target sample seen at test time.
(DE)^2 CO: Deep Depth Colorization
A qualitative analysis of the images obtained with this approach clearly indicates that learning the optimal mapping preserves the richness of depth information better than current hand-crafted approaches.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
