109 citations · 148 across the 4 of their papers we have counts for
7 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…
CPTR: Full Transformer Network for Image Captioning
Wei Liu, Sihan Chen, Longteng Guo +2
In this paper, we consider the image captioning task from a new sequence-to-sequence prediction perspective and propose CaPtion TransformeR (CPTR) which takes the sequentialized ra…
Fast Sequence Generation with Multi-Agent Reinforcement Learning
Longteng Guo, Jing Liu, Xinxin Zhu +1
Autoregressive sequence Generation models have achieved state-of-the-art performance in areas like machine translation and image captioning. These models are autoregressive in that…
Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning
Longteng Guo, Jing Liu, Xinxin Zhu +3
Most image captioning models are autoregressive, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. Recently…
Normalized and Geometry-Aware Self-Attention Network for Image Captioning
Longteng Guo, Jing Liu, Xinxin Zhu +3
Self-attention (SA) network has shown profound value in image captioning. In this paper, we improve SA from two aspects to promote the performance of image captioning. First, we pr…
Vatex Video Captioning Challenge 2020: Multi-View Features and Hybrid Reward Strategies for Video Captioning
Xinxin Zhu, Longteng Guo, Peng Yao +3
This report describes our solution for the VATEX Captioning Challenge 2020, which requires generating descriptions for the videos in both English and Chinese languages. We identifi…