485 citations · 666 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
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…
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…
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Xingjun Ma, Bo Li, Yisen Wang +6
Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors durin…