7 citations · 12 across the 6 of their papers we have counts for
8 papers
Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects
Qi Wang, Juncheng Wang, Junyu Gao +2
The mainstream crowd counting methods regress density map and integrate it to obtain counting results. Since the density representation to one head accords to its adjacent distribu…
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
Xin Yang, Michael Bi Mi, Yuan Yuan +2
Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, renderin…
Text Growing on Leaf
Chuang. Yang, Mulin. Chen, Yuan. Yuan +1
Irregular-shaped texts bring challenges to Scene Text Detection (STD). Although existing contour point sequence-based approaches achieve comparable performances, they fail to cover…
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
Yangtao Wang, Xi Shen, Shell Hu +3
Transformers trained with self-supervised learning using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects. In this…
Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification
Qi Wang, Sikai Bai, Junyu Gao +2
Person re-identification (re-ID) has gained more and more attention due to its widespread applications in intelligent video surveillance. Unfortunately, the mainstream deep learnin…
LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds
Qi wang, Tao Han, Junyu Gao +2
The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map. It also enlightens us back…