activity
20182022
most citedLearning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation

93 citations · 215 across the 12 of their papers we have counts for

collaborators

18 papers

cs.CV202110 cited

Temporal-attentive Covariance Pooling Networks for Video Recognition

Zilin Gao, Qilong Wang, Bingbing Zhang +2

For video recognition task, a global representation summarizing the whole contents of the video snippets plays an important role for the final performance. However, existing video…

cs.CV2021

T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation

Ruihuang Li, Xu Jia, Jianzhong He +2

Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help…

cs.CV2021

VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results

Dawei Du, Longyin Wen, Pengfei Zhu +52

Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.…

cs.CV2021

Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark

Longyin Wen, Dawei Du, Pengfei Zhu +4

To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, n…

cs.LG202022 cited

Deep Partial Multi-View Learning

Changqing Zhang, Yajie Cui, Zongbo Han +3

Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among diffe…

cs.CV20203 cited

SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning

Junbing Li, Changqing Zhang, Pengfei Zhu +3

Although significant progress achieved, multi-label classification is still challenging due to the complexity of correlations among different labels. Furthermore, modeling the rela…