most citedEvery Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector

14 citations · 44 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV202114 cited

TransVOS: Video Object Segmentation with Transformers

Jianbiao Mei, Mengmeng Wang, Yeneng Lin +2

Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS). A crucial problem in this tas…

cs.CV20214 cited

Unsupervised Sound Localization via Iterative Contrastive Learning

Yan-Bo Lin, Hung-Yu Tseng, Hsin-Ying Lee +2

Sound localization aims to find the source of the audio signal in the visual scene. However, it is labor-intensive to annotate the correlations between the signals sampled from the…

cs.CV20202 cited

MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation

Liangjian Chen, Shih-Yao Lin, Yusheng Xie +2

Estimating 3D hand poses from a single RGB image is challenging because depth ambiguity leads the problem ill-posed. Training hand pose estimators with 3D hand mesh annotations and…

cs.CV20203 cited

Temporal-Aware Self-Supervised Learning for 3D Hand Pose and Mesh Estimation in Videos

Liangjian Chen, Shih-Yao Lin, Yusheng Xie +2

Estimating 3D hand pose directly from RGB imagesis challenging but has gained steady progress recently bytraining deep models with annotated 3D poses. Howeverannotating 3D poses is…

cs.CV20207 cited

MM-Hand: 3D-Aware Multi-Modal Guided Hand Generative Network for 3D Hand Pose Synthesis

Zhenyu Wu, Duc Hoang, Shih-Yao Lin +5

Estimating the 3D hand pose from a monocular RGB image is important but challenging. A solution is training on large-scale RGB hand images with accurate 3D hand keypoint annotation…

cs.CV202014 cited

Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector

Cheng-Chun Hsu, Yi-Hsuan Tsai, Yen-Yu Lin +1

A domain adaptive object detector aims to adapt itself to unseen domains that may contain variations of object appearance, viewpoints or backgrounds. Most existing methods adopt fe…