2 code implementations • CVPR 2023 • Zhida Feng, Zhenyu Zhang, Xintong Yu, Yewei Fang, Lanxin Li, Xuyi Chen, Yuxiang Lu, Jiaxiang Liu, Weichong Yin, Shikun Feng, Yu Sun, Li Chen, Hao Tian, Hua Wu, Haifeng Wang
Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation.
Ranked #12 on Text-to-Image Generation on MS COCO
no code implementations • 23 Mar 2022 • Yang Liu, Jiaxiang Liu, Li Chen, Yuxiang Lu, Shikun Feng, Zhida Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
We argue that two factors, information bottleneck sensitivity and inconsistency between different attention topologies, could affect the performance of the Sparse Transformer.
no code implementations • 29 Sep 2021 • Yang Liu, Jiaxiang Liu, Yuxiang Lu, Shikun Feng, Yu Sun, Zhida Feng, Li Chen, Hao Tian, Hua Wu, Haifeng Wang
The first factor is information bottleneck sensitivity, which is caused by the key feature of Sparse Transformer — only a small number of global tokens can attend to all other tokens.
no code implementations • SEMEVAL 2021 • Zhida Feng, Jiji Tang, Jiaxiang Liu, Weichong Yin, Shikun Feng, Yu Sun, Li Chen
This paper describes our system participated in Task 6 of SemEval-2021: the task focuses on multimodal propaganda technique classification and it aims to classify given image and text into 22 classes.