11 citations · 19 across the 8 of their papers we have counts for
9 papers
Compensating Visual Insufficiency with Stratified Language Guidance for Long-Tail Class Incremental Learning
Xi Wang, Xu Yang, Donghao Sun +1
Long-tail class incremental learning (LT CIL) remains highly challenging because the scarcity of samples in tail classes not only hampers their learning but also exacerbates catast…
A Bridge from Audio to Video: Phoneme-Viseme Alignment Allows Every Face to Speak Multiple Languages
Zibo Su, Kun Wei, Jiahua Li +3
Speech-driven talking face synthesis (TFS) focuses on generating lifelike facial animations from speech input. Current TFS models perform well in English but struggle with non-Engl…
Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space
Xiang Zhang, Kun Wei, Xu Yang +3
As Large Language Models (LLMs) become increasingly prevalent, their security vulnerabilities have already drawn attention. Machine unlearning is introduced to seek to mitigate the…
Perceive, Verify and Understand Long Video: Multi-Granular Perception and Active Verification via Interactive Agents
Jiahua Li, Zhanhe Zhang, Chenghao Xu +4
Long videos, characterized by temporal complexity and sparse task-relevant information, pose significant reasoning challenges for AI systems. Although existing Large Language Model…
Do You Guys Want to Dance: Zero-Shot Compositional Human Dance Generation with Multiple Persons
Zhe Xu, Kun Wei, Xu Yang +1
Human dance generation (HDG) aims to synthesize realistic videos from images and sequences of driving poses. Despite great success, existing methods are limited to generating video…
Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning
Xiangyu Li, Xu Yang, Kun Wei +2
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual a…
Doubly Contrastive Deep Clustering
Zhiyuan Dang, Cheng Deng, Xu Yang +1
Deep clustering successfully provides more effective features than conventional ones and thus becomes an important technique in current unsupervised learning. However, most deep cl…
Incremental Embedding Learning via Zero-Shot Translation
Kun Wei, Cheng Deng, Xu Yang +1
Modern deep learning methods have achieved great success in machine learning and computer vision fields by learning a set of pre-defined datasets. Howerver, these methods perform u…
Deep Spectral Clustering using Dual Autoencoder Network
Xu Yang, Cheng Deng, Feng Zheng +2
The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embeddi…