1 code implementation • 19 Jul 2022 • Shuai Bai, Huiling Zhou, Zhikang Li, Chang Zhou, Hongxia Yang
Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a reference person image.
Ranked #3 on Virtual Try-on on VITON
no code implementations • 24 May 2022 • Zhikang Li, Huiling Zhou, Shuai Bai, Peike Li, Chang Zhou, Hongxia Yang
The fashion industry has diverse applications in multi-modal image generation and editing.
4 code implementations • 7 Feb 2022 • Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang
In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization.
Ranked #1 on Visual Question Answering on VQA v2 test-std (yes/no metric)
no code implementations • 7 Dec 2021 • Huiling Zhou, Jie Liu, Zhikang Li, Jin Yu, Hongxia Yang
With user history represented by a domain-aware sequential model, a frequency encoder is applied to the underlying tags for user content preference learning.
no code implementations • NeurIPS 2021 • Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang
Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.
no code implementations • NeurIPS 2021 • Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang
Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.
no code implementations • 1 Mar 2021 • Junyang Lin, Rui Men, An Yang, Chang Zhou, Ming Ding, Yichang Zhang, Peng Wang, Ang Wang, Le Jiang, Xianyan Jia, Jie Zhang, Jianwei Zhang, Xu Zou, Zhikang Li, Xiaodong Deng, Jie Liu, Jinbao Xue, Huiling Zhou, Jianxin Ma, Jin Yu, Yong Li, Wei Lin, Jingren Zhou, Jie Tang, Hongxia Yang
In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.