activity
20182021
most citedDiscovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identification

5 citations · 5 across the 2 of their papers we have counts for

collaborators

9 papers

cs.CV2021

Confidence-guided Adaptive Gate and Dual Differential Enhancement for Video Salient Object Detection

Peijia Chen, Jianhuang Lai, Guangcong Wang +1

Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences. However, sp…

cs.CV2021

Solving Inefficiency of Self-supervised Representation Learning

Guangrun Wang, Keze Wang, Guangcong Wang +2

Self-supervised learning (especially contrastive learning) has attracted great interest due to its huge potential in learning discriminative representations in an unsupervised mann…

cs.CV2021

Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition

Guangrun Wang, Liang Lin, Rongcong Chen +2

Current state-of-the-art visual recognition systems usually rely on the following pipeline: (a) pretraining a neural network on a large-scale dataset (e.g., ImageNet) and (b) finet…

cs.LG2019

Learnable Parameter Similarity

Guangcong Wang, Jianhuang Lai, Wenqi Liang +1

Most of the existing approaches focus on specific visual tasks while ignoring the relations between them. Estimating task relation sheds light on the learning of high-order semanti…

cs.CV2019

Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New Benchmark

Guangrun Wang, Guangcong Wang, Xujie Zhang +3

Person re-identification (Re-ID) benefits greatly from the accurate annotations of existing datasets (e.g., CUHK03 [1] and Market-1501 [2]), which are quite expensive because each…

cs.CV20195 cited

Discovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identification

Guangcong Wang, Jianhuang Lai, Zhenyu Xie +1

Modeling the underlying person structure for person re-identification (re-ID) is difficult due to diverse deformable poses, changeable camera views and imperfect person detectors.…