no code implementations • 12 Jun 2024 • Meiziniu Li, Dongze Li, Jianmeng Liu, Jialun Cao, Yongqiang Tian, Shing-Chi Cheung
Our evaluation shows that DLLens can synthesize counterparts for more than twice as many APIs found by state-of-the-art techniques on these libraries.
no code implementations • 8 Jun 2024 • Jianmeng Liu, Yichen Liu, Yuyao Zhang, Zeyuan Meng, Yu-Wing Tai, Chi-Keung Tang
Recent conditional 3D completion works have mainly relied on CLIP or BERT to encode textual information, which cannot support complex instruction.
no code implementations • 5 Dec 2023 • Jianmeng Liu, Yuyao Zhang, Zeyuan Meng, Yu-Wing Tai, Chi-Keung Tang
This paper explores promptable NeRF generation (e. g., text prompt or single image prompt) for direct conditioning and fast generation of NeRF parameters for the underlying 3D scenes, thus undoing complex intermediate steps while providing full 3D generation with conditional control.
1 code implementation • 12 Sep 2023 • Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan YAO, Tong Zhang
Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different alignment-forgetting trade-offs, we propose Heterogeneous Model Averaging (HMA) to Heterogeneously find various combination ratios of model layers.