5 papers · 1 filter
Towards Training-Free Open-World Classification with 3D Generative Models
Xinzhe Xia, Weiguang Zhao, Yuyao Yan +4
3D open-world classification is a challenging yet essential task in dynamic and unstructured real-world scenarios, requiring both open-category and open-pose recognition. To addres…
Covariance-based Space Regularization for Few-shot Class Incremental Learning
Yijie Hu, Guanyu Yang, Zhaorui Tan +3
Few-shot Class Incremental Learning (FSCIL) presents a challenging yet realistic scenario, which requires the model to continually learn new classes with limited labeled data (i.e.…
Generalized W-Net: Arbitrary-style Chinese Character Synthesization
Haochuan Jiang, Guanyu Yang, Fei Cheng +1
Synthesizing Chinese characters with consistent style using few stylized examples is challenging. Existing models struggle to generate arbitrary style characters with limited examp…
W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep Neural Networks
Haochuan Jiang, Guanyu Yang, Kaizhu Huang +1
Due to the huge category number, the sophisticated combinations of various strokes and radicals, and the free writing or printing styles, generating Chinese characters with diverse…
Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges
Weiguang Zhao, Guanyu Yang, Rui Zhang +5
With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, methods transferring language or language-imag…