activity
20182022
most citedGETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection

572 citations · 3.2k across the 46 of their papers we have counts for

collaborators

46 papers

cs.CV2022★ 2 cited

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…

cs.CV2022★ 1 cited

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…

cs.CV2022★ 23 cited

Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher

Juncheng Wang, Junyu Gao, Yuan Yuan +1

Crowd localization is to predict each instance head position in crowd scenarios. Since the distance of instances being to the camera are variant, there exists tremendous gaps among…

cs.CV2021★ 9 cited

THE Benchmark: Transferable Representation Learning for Monocular Height Estimation

Zhitong Xiong, Wei Huang, Jingtao Hu +1

Generating 3D city models rapidly is crucial for many applications. Monocular height estimation is one of the most efficient and timely ways to obtain large-scale geometric informa…

cs.CV2021

Adaptive Shrink-Mask for Text Detection

Chuang Yang, Mulin Chen, Yuan Yuan +2

Existing real-time text detectors reconstruct text contours by shrink-masks directly, which simplifies the framework and can make the model run fast. However, the strong dependence…

cs.CV2021★ 2 cited

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…