1 code implementation • Findings (ACL) 2022 • Wei Li, Can Gao, guocheng niu, Xinyan Xiao, Hao liu, Jiachen Liu, Hua Wu, Haifeng Wang
In particular, we propose to conduct grounded learning on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the visual and textual semantic spaces on different types of corpora.
no code implementations • Findings (ACL) 2022 • Luyang Huang, guocheng niu, Jiachen Liu, Xinyan Xiao, Hua Wu
To bridge the gap between image understanding and generation, we further design a novel commitment loss.
1 code implementation • NeurIPS 2021 • Zhenyu Huang, guocheng niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, Xi Peng
Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels.
no code implementations • 18 May 2021 • Bofeng Wu, guocheng niu, Jun Yu, Xinyan Xiao, Jian Zhang, Hua Wu
This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation.
3 code implementations • ACL 2021 • Wei Li, Can Gao, guocheng niu, Xinyan Xiao, Hao liu, Jiachen Liu, Hua Wu, Haifeng Wang
Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other.
Ranked #3 on Image Captioning on MS COCO
no code implementations • IJCNLP 2019 • Guocheng Niu, Hengru Xu, Bolei He, Xinyan Xiao, Hua Wu, Sheng Gao
For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling.
1 code implementation • ICLR 2018 • chao qiao, Bo Huang, guocheng niu, daren li, daxiang dong, wei he, dianhai yu, Hua Wu
In this paper, we propose a new method of learning and utilizing task-specific distributed representations of n-grams, referred to as “region embeddings”.