activity
20162024
most citedFeature Alignment and Restoration for Domain Generalization and Adaptation

35 citations · 141 across the 18 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

cs.CV2019

EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks

Pengfei Zhang, Jianru Xue, Cuiling Lan +3

Recurrent neural networks (RNNs) are capable of modeling temporal dependencies of complex sequential data. In general, current available structures of RNNs tend to concentrate on c…

cs.CV2019

Semantics-Aligned Representation Learning for Person Re-identification

Xin Jin, Cuiling Lan, Wenjun Zeng +2

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally seman…

cs.CV2019

CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing

Xin Jin, Cuiling Lan, Wenjun Zeng +2

Objects at different spatial positions in an image exhibit different scales. Adaptive receptive fields are expected to capture suitable ranges of context for accurate pixel level s…

cs.CV2019

Relation-Aware Global Attention for Person Re-identification

Zhizheng Zhang, Cuiling Lan, Wenjun Zeng +2

For person re-identification (re-id), attention mechanisms have become attractive as they aim at strengthening discriminative features and suppressing irrelevant ones, which matche…

cs.CV2019

Target-Tailored Source-Transformation for Scene Graph Generation

Wentong Liao, Cuiling Lan, Wenjun Zeng +2

Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performin…

cs.CV2019

Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition

Pengfei Zhang, Cuiling Lan, Wenjun Zeng +3

Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feed…