1 citations · 1 across the 4 of their papers we have counts for
4 papers
AIR-DA: Adversarial Image Reconstruction for Unsupervised Domain Adaptive Object Detection
Kunyang Sun, Wei Lin, Haoqin Shi +3
Unsupervised domain adaptive object detection is a challenging vision task where object detectors are adapted from a label-rich source domain to an unlabeled target domain. Recent…
MATE: Masked Autoencoders are Online 3D Test-Time Learners
M. Jehanzeb Mirza, Inkyu Shin, Wei Lin +8
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring…
Multi-Frequency-Aware Patch Adversarial Learning for Neural Point Cloud Rendering
Jay Karhade, Haiyue Zhu, Ka-Shing Chung +3
We present a neural point cloud rendering pipeline through a novel multi-frequency-aware patch adversarial learning framework. The proposed approach aims to improve the rendering r…
CycDA: Unsupervised Cycle Domain Adaptation from Image to Video
Wei Lin, Anna Kukleva, Kunyang Sun +3
Although action recognition has achieved impressive results over recent years, both collection and annotation of video training data are still time-consuming and cost intensive. Th…