4 citations · 9 across the 6 of their papers we have counts for
6 papers
Control and Realism: Best of Both Worlds in Layout-to-Image without Training
Bonan Li, Yinhan Hu, Songhua Liu +1
Layout-to-Image generation aims to create complex scenes with precise control over the placement and arrangement of subjects. Existing works have demonstrated that pre-trained Text…
SG-Former: Self-guided Transformer with Evolving Token Reallocation
Sucheng Ren, Xingyi Yang, Songhua Liu +1
Vision Transformer has demonstrated impressive success across various vision tasks. However, its heavy computation cost, which grows quadratically with respect to the token sequenc…
Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer
Hao Tang, Songhua Liu, Tianwei Lin +4
Transformer-based models achieve favorable performance in artistic style transfer recently thanks to its global receptive field and powerful multi-head/layer attention operations.…
Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate
Songhua Liu, Jingwen Ye, Xinchao Wang
Style transfer aims to render the style of a given image for style reference to another given image for content reference, and has been widely adopted in artistic generation and im…
Partial Network Cloning
Jingwen Ye, Songhua Liu, Xinchao Wang
In this paper, we study a novel task that enables partial knowledge transfer from pre-trained models, which we term as Partial Network Cloning (PNC). Unlike prior methods that upda…
Learning with Recoverable Forgetting
Jingwen Ye, Yifang Fu, Jie Song +5
Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge. However, the involved training data may not be life-long legitimate du…