Deep Neural Networks (DNNs) have been ubiquitously adopted in internet o...
Generalizing models trained on normal visual conditions to target domain...
Tabular visualization techniques integrate visual representations with
t...
Extensive studies on Unsupervised Domain Adaptation (UDA) have propelled...
Infrared cameras are often utilized to enhance the night vision since th...
Domain adaptive semantic segmentation attempts to make satisfactory dens...
Domain generalization (DG) is essentially an out-of-distribution problem...
Vision-based autonomous urban driving in dense traffic is quite challeng...
Deep neural networks (DNNs) have become ubiquitous techniques in mobile ...
Domain adaptation (DA) attempts to transfer the knowledge from a labeled...
Unsupervised domain adaptation has recently emerged as an effective para...
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a
l...
Self-training has greatly facilitated domain adaptive semantic segmentat...
Domain adaptation (DA) paves the way for label annotation and dataset bi...
Human vision is often adversely affected by complex environmental factor...
Domain adaptive semantic segmentation refers to making predictions on a
...
Real-world training data usually exhibits long-tailed distribution, wher...
Domain adaptation has been widely explored by transferring the knowledge...
Domain Adaptation (DA) attempts to transfer knowledge learned in the lab...
Unsupervised domain adaptation challenges the problem of transferring
kn...
Heterogeneous domain adaptation (HDA) transfers knowledge across source ...
Tremendous research efforts have been made to thrive deep domain adaptat...
Deep domain adaptation methods have achieved appealing performance by
le...
Modern communication networks have become very complicated and highly
dy...