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20192021
most citedORDNet: Capturing Omni-Range Dependencies for Scene Parsing

22 citations · 31 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CV20211 cited

Cross-Modal Progressive Comprehension for Referring Segmentation

Si Liu, Tianrui Hui, Shaofei Huang +3

Given a natural language expression and an image/video, the goal of referring segmentation is to produce the pixel-level masks of the entities described by the subject of the expre…

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.CV20205 cited

Automated Model Compression by Jointly Applied Pruning and Quantization

Wenting Tang, Xingxing Wei, Bo Li

In the traditional deep compression framework, iteratively performing network pruning and quantization can reduce the model size and computation cost to meet the deployment require…

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…

cs.CV2019

A Real-Time Cross-modality Correlation Filtering Method for Referring Expression Comprehension

Yue Liao, Si Liu, Guanbin Li +4

Referring expression comprehension aims to localize the object instance described by a natural language expression. Current referring expression methods have achieved good performa…