21 citations · 21 across the 3 of their papers we have counts for
6 papers
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
Jing Liu, Xinxin Zhu, Fei Liu +8
In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources. OPT is constructe…
Contextualized Perturbation for Textual Adversarial Attack
Dianqi Li, Yizhe Zhang, Hao Peng +4
Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of genera…
Learning Nonparametric Human Mesh Reconstruction from a Single Image without Ground Truth Meshes
Kevin Lin, Lijuan Wang, Ying Jin +2
Nonparametric approaches have shown promising results on reconstructing 3D human mesh from a single monocular image. Unlike previous approaches that use a parametric human model li…
Learning to Generate Multiple Style Transfer Outputs for an Input Sentence
Kevin Lin, Ming-Yu Liu, Ming-Ting Sun +1
Text style transfer refers to the task of rephrasing a given text in a different style. While various methods have been proposed to advance the state of the art, they often assume…
Domain Adaptive Text Style Transfer
Dianqi Li, Yizhe Zhang, Zhe Gan +4
Text style transfer without parallel data has achieved some practical success. However, in the scenario where less data is available, these methods may yield poor performance. In t…
Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning
Dianqi Li, Qiuyuan Huang, Xiaodong He +2
We study how to generate captions that are not only accurate in describing an image but also discriminative across different images. The problem is both fundamental and interesting…