Search Results for author:
Found 17 papers, 7 papers with code
Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
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
Towards Occlusion-Aware Multifocal Displays
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.
Deep Classification Network for Monocular Depth Estimation
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.
Confidence Regularized Self-Training
Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation.
Ranked #16 on
Domain Adaptation
on VisDA2017
Attention Control with Metric Learning Alignment for Image Set-based Recognition
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.
Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets
This paper targets the problem of image set-based face verification and identification.
Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
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.
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
Recent deep networks achieved state of the art performanceon a variety of semantic segmentation tasks.
Simultaneous Edge Alignment and Learning
Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications.
Towards Multifocal Displays with Dense Focal Stacks
We present a virtual reality display that is capable of generating a dense collection of depth/focal planes.
One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection Models
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.
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
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.
Random Features for Sparse Signal Classification
Random features is an approach for kernel-based inference on large datasets.
Structured Hough Voting for Vision-based Highway Border Detection
We propose a vision-based highway border detection algorithm using structured Hough voting.
Zero-Aliasing Correlation Filters for Object Recognition
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.
Maximum Margin Vector Correlation Filter
Correlation Filters (CFs) are a class of classifiers which are designed for accurate pattern localization.
Correlation Filters for Object Alignment
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.
