253 citations · 341 across the 14 of their papers we have counts for
19 papers
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Lukas Hoyer, Dengxin Dai, Qin Wang +2
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a…
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
Suman Saha, Anton Obukhov, Danda Pani Paudel +4
We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. Semantic segmentation and monocular d…
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Rui Gong, Yuhua Chen, Danda Pani Paudel +5
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advant…
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Lukas Hoyer, Dengxin Dai, Yuhua Chen +3
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a…
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
Rui Gong, Dengxin Dai, Yuhua Chen +2
One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities. This necessitates methods to combine and reuse existing datasets that…
Analogical Image Translation for Fog Generation
Rui Gong, Dengxin Dai, Yuhua Chen +2
Image-to-image translation is to map images from a given \emph{style} to another given \emph{style}. While exceptionally successful, current methods assume the availability of trai…
Consistency Guided Scene Flow Estimation
Yuhua Chen, Luc Van Gool, Cordelia Schmid +1
Consistency Guided Scene Flow Estimation (CGSF) is a self-supervised framework for the joint reconstruction of 3D scene structure and motion from stereo video. The model takes two…
Unbiased Mean Teacher for Cross-domain Object Detection
Jinhong Deng, Wen Li, Yuhua Chen +1
Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distincti…
Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing
Suman Saha, Wenhao Xu, Menelaos Kanakis +4
Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this di…
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera
Yuhua Chen, Cordelia Schmid, Cristian Sminchisescu
We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video - addressing the difficulty of acquiring r…
Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach
Yuhua Chen, Wen Li, Xiaoran Chen +1
Recently, increasing attention has been drawn to training semantic segmentation models using synthetic data and computer-generated annotation. However, domain gap remains a major b…
DLOW: Domain Flow for Adaptation and Generalization
Rui Gong, Wen Li, Yuhua Chen +1
In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to…
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
Yuhua Chen, Jordi Pont-Tuset, Alberto Montes +1
This paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest. The problem is formulated as pixel-wise retrieval in…
The 2018 DAVIS Challenge on Video Object Segmentation
Sergi Caelles, Alberto Montes, Kevis-Kokitsi Maninis +4
We present the 2018 DAVIS Challenge on Video Object Segmentation, a public competition specifically designed for the task of video object segmentation. It builds upon the DAVIS 201…
Domain Adaptive Faster R-CNN for Object Detection in the Wild
Yuhua Chen, Wen Li, Christos Sakaridis +2
Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution misma…
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
Yuhua Chen, Wen Li, Luc Van Gool
Exploiting synthetic data to learn deep models has attracted increasing attention in recent years. However, the intrinsic domain difference between synthetic and real images usuall…
Video Object Segmentation Without Temporal Information
Kevis-Kokitsi Maninis, Sergi Caelles, Yuhua Chen +4
Video Object Segmentation, and video processing in general, has been historically dominated by methods that rely on the temporal consistency and redundancy in consecutive video fra…
Semantically-Guided Video Object Segmentation
Sergi Caelles, Yuhua Chen, Jordi Pont-Tuset +1
This paper tackles the problem of semi-supervised video object segmentation, that is, segmenting an object in a sequence given its mask in the first frame. One of the main challeng…
Is Image Super-resolution Helpful for Other Vision Tasks?
Dengxin Dai, Yujian Wang, Yuhua Chen +1
Despite the great advances made in the field of image super-resolution (ISR) during the last years, the performance has merely been evaluated perceptually. Thus, it is still unclea…