29 citations · 46 across the 7 of their papers we have counts for
7 papers
Emage: Non-Autoregressive Text-to-Image Generation
Zhangyin Feng, Runyi Hu, Liangxin Liu +7
Autoregressive and diffusion models drive the recent breakthroughs on text-to-image generation. Despite their huge success of generating high-realistic images, a common shortcoming…
Planning with Logical Graph-based Language Model for Instruction Generation
Fan Zhang, Kebing Jin, Hankz Hankui Zhuo
Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the…
Emu: Generative Pretraining in Multimodality
Quan Sun, Qiying Yu, Yufeng Cui +7
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-mo…
SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills
Zhangyin Feng, Yong Dai, Fan Zhang +6
Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge. This paper p…
One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code
Yong Dai, Duyu Tang, Liangxin Liu +7
People perceive the world with multiple senses (e.g., through hearing sounds, reading words and seeing objects). However, most existing AI systems only process an individual modali…
SkillNet-NLG: General-Purpose Natural Language Generation with a Sparsely Activated Approach
Junwei Liao, Duyu Tang, Fan Zhang +1
We present SkillNet-NLG, a sparsely activated approach that handles many natural language generation tasks with one model. Different from traditional dense models that always activ…
SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding
Fan Zhang, Duyu Tang, Yong Dai +3
Prevailing deep models are single-purpose and overspecialize at individual tasks. However, when being extended to new tasks, they typically forget previously learned skills and lea…