most citedORDNet: Capturing Omni-Range Dependencies for Scene Parsing

22 citations · 39 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2021

Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation

Tianrui Hui, Shaofei Huang, Si Liu +5

Language-queried video actor segmentation aims to predict the pixel-level mask of the actor which performs the actions described by a natural language query in the target frames. E…

cs.CV2021

LI-Net: Large-Pose Identity-Preserving Face Reenactment Network

Jin Liu, Peng Chen, Tao Liang +5

Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously. Most existing methods directly apply driving facial la…

cs.CV202122 cited

ORDNet: Capturing Omni-Range Dependencies for Scene Parsing

Shaofei Huang, Si Liu, Tianrui Hui +4

Learning to capture dependencies between spatial positions is essential to many visual tasks, especially the dense labeling problems like scene parsing. Existing methods can effect…

cs.CL20204 cited

Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learning

Chunyuan Yuan, Qianwen Ma, Wei Zhou +2

The dissemination of fake news significantly affects personal reputation and public trust. Recently, fake news detection has attracted tremendous attention, and previous studies ma…

cs.CV202010 cited

Linguistic Structure Guided Context Modeling for Referring Image Segmentation

Tianrui Hui, Si Liu, Shaofei Huang +4

Referring image segmentation aims to predict the foreground mask of the object referred by a natural language sentence. Multimodal context of the sentence is crucial to distinguish…

cs.CV20203 cited

Referring Image Segmentation via Cross-Modal Progressive Comprehension

Shaofei Huang, Tianrui Hui, Si Liu +5

Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approach…