To this end, we propose a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id-related feature space exclusively.
Ranked #6 on Unsupervised Domain Adaptation on Market to MSMT
Multifocal displays, one of the classic approaches to satisfy the accommodation cue, place virtual content at multiple focal planes, each at a di erent depth.
Monocular Depth Estimation is usually treated as a supervised and regression problem when it actually is very similar to semantic segmentation task since they both are fundamentally pixel-level classification tasks.
Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation.
Ranked #10 on Domain Adaptation on VisDA2017
The importance of each image is usually considered either equal or based on a quality assessment of that image independent of other images and/or videos in that image set.
This paper targets the problem of image set-based face verification and identification.
In this paper, we propose a novel UDA framework based on an iterative self-training procedure, where the problem is formulated as latent variable loss minimization, and can be solved by alternatively generating pseudo labels on target data and re-training the model with these labels.
Recent deep networks achieved state of the art performanceon a variety of semantic segmentation tasks.
Ranked #11 on Image-to-Image Translation on GTAV-to-Cityscapes Labels
Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications.
While deep learning methods have achieved state-of-the-art performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks.
On the other hand, traditional methods using signal priors can be used in all linear inverse problems but often have worse performance on challenging tasks.
However, existing CF designs do not account for the fact that the multiplication of two DFTs in the frequency domain corresponds to a circular correlation in the time/spatial domain.
A typical object alignment system consists of a landmark appearance model which is used to obtain an initial shape and a shape model which refines this initial shape by correcting the initialization errors.