8 citations · 17 across the 6 of their papers we have counts for
13 papers
Meta Transferring for Deblurring
Po-Sheng Liu, Fu-Jen Tsai, Yan-Tsung Peng +3
Most previous deblurring methods were built with a generic model trained on blurred images and their sharp counterparts. However, these approaches might have sub-optimal deblurring…
DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation
Liangjian Chen, Shih-Yao Lin, Yusheng Xie +3
Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formati…
Regularizing Meta-Learning via Gradient Dropout
Hung-Yu Tseng, Yi-Wen Chen, Yi-Hsuan Tsai +3
With the growing attention on learning-to-learn new tasks using only a few examples, meta-learning has been widely used in numerous problems such as few-shot classification, reinfo…
Deep Semantic Matching with Foreground Detection and Cycle-Consistency
Yun-Chun Chen, Po-Hsiang Huang, Li-Yu Yu +3
Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intr…
Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond
Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin +1
Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismat…
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang +1
Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e.g., synthetic to real images). The adapted representations often do no…