109 citations · 177 across the 7 of their papers we have counts for
7 papers
CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation
Wenxuan Wang, Jing Liu, Xingjian He +5
Referring image segmentation (RIS) is a fundamental vision-language task that intends to segment a desired object from an image based on a given natural language expression. Due to…
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…
Global-Local Propagation Network for RGB-D Semantic Segmentation
Sihan Chen, Xinxin Zhu, Wei Liu +2
Depth information matters in RGB-D semantic segmentation task for providing additional geometric information to color images. Most existing methods exploit a multi-stage fusion str…
AutoCaption: Image Captioning with Neural Architecture Search
Xinxin Zhu, Weining Wang, Longteng Guo +1
Image captioning transforms complex visual information into abstract natural language for representation, which can help computers understanding the world quickly. However, due to…
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…
Semantic-Aware Label Placement for Augmented Reality in Street View
Jianqing Jia, Semir Elezovikj, Heng Fan +5
In an augmented reality (AR) application, placing labels in a manner that is clear and readable without occluding the critical information from the real-world can be a challenging…