no code implementations • 16 Apr 2024 • Zhengyang Liang, Meiyu Liang, Wei Huang, Yawen Li, Zhe Xue
Our methodology streamlines pre-trained multimodal large models using only their output features and original image-level information, requiring minimal computational resources.
4 code implementations • 10 Jul 2023 • Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, Bo Dai
Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator.
1 code implementation • 4 Jul 2023 • Qi Yan, Zhengyang Liang, Yang song, Renjie Liao, Lele Wang
Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data.
no code implementations • 29 Sep 2021 • Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, Xin Jin, Quanshi Zhang
This paper proposes a hypothesis to analyze the underlying reason for the cognitive difficulty of an image from two perspectives, i. e. a cognitive image usually makes a DNN strongly activated by cognitive concepts; discarding massive non-cognitive concepts may also help the DNN focus on cognitive concepts.
no code implementations • 31 Jul 2021 • Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, Quanshi Zhang
This paper proposes a hypothesis for the aesthetic appreciation that aesthetic images make a neural network strengthen salient concepts and discard inessential concepts.