Domain generalization (DG) aims to learn domain-generalizable models fro...
Black-box unsupervised domain adaptation (UDA) learns with source predic...
Traditional domain adaptation assumes the same vocabulary across source ...
Robust point cloud parsing under all-weather conditions is crucial to le...
Most visual recognition studies rely heavily on crowd-labelled data in d...
LiDAR point clouds, which are usually scanned by rotating LiDAR sensors
...
The recently proposed DEtection TRansformer (DETR) has established a ful...
Most existing scene text detectors focus on detecting characters or word...
Video semantic segmentation has achieved great progress under the superv...
Domain adaptive panoptic segmentation aims to mitigate data annotation
c...
Semi-supervised semantic segmentation learns from small amounts of label...
Point cloud data have been widely explored due to its superior accuracy ...
Unsupervised domain adaptation aims to align a labeled source domain and...
Training effective Generative Adversarial Networks (GANs) requires large...
Video semantic segmentation is an essential task for the analysis and
un...
Transfer learning from synthetic to real data has been proved an effecti...
Generative Adversarial Networks (GANs) have become the de-facto standard...
Unsupervised domain adaptation (UDA) aims to learn a well-performed mode...
Instance contrast for unsupervised representation learning has achieved ...
Unsupervised domain adaptation (UDA) involves a supervised loss in a lab...
Contemporary domain adaptive semantic segmentation aims to address data
...
The prevalent approach in domain adaptive object detection adopts a two-...
Recent progresses in domain adaptive semantic segmentation demonstrate t...
Panoptic segmentation unifies semantic segmentation and instance segment...
Domain generalization aims to learn a generalizable model from a known s...
Scene understanding based on LiDAR point cloud is an essential task for
...
Unsupervised domain adaptive object detection aims to adapt detectors fr...
Despite the rapid progress of generative adversarial networks (GANs) in ...