50 citations · 75 across the 14 of their papers we have counts for
10 papers · 1 filter
A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation
Hui Tang, Kui Jia
Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. However, exhaustive data annotation is impracticable for each task…
HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization
Zhihao Liang, Zhangjin Huang, Changxing Ding +1
Recovery of an underlying scene geometry from multiview images stands as a long-time challenge in computer vision research. The recent promise leverages neural implicit surface lea…
Unsupervised Domain Adaptation via Distilled Discriminative Clustering
Hui Tang, Yaowei Wang, Kui Jia
Unsupervised domain adaptation addresses the problem of classifying data in an unlabeled target domain, given labeled source domain data that share a common label space but follow…
Adversarial Style Augmentation for Domain Generalization
Yabin Zhang, Bin Deng, Ruihuang Li +2
It is well-known that the performance of well-trained deep neural networks may degrade significantly when they are applied to data with even slightly shifted distributions. Recent…
Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization
Kangjun Liu, Ke Chen, Kui Jia
Fine-grained visual classification can be addressed by deep representation learning under supervision of manually pre-defined targets (e.g., one-hot or the Hadamard codes). Such ta…
Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networks
Jiehong Lin, Zewei Wei, Changxing Ding +1
It is difficult to precisely annotate object instances and their semantics in 3D space, and as such, synthetic data are extensively used for these tasks, e.g., category-level 6D ob…