388 citations · 1.3k across the 31 of their papers we have counts for
7 papers · 1 filter
Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking
Pengfei Zhu, Hongtao Yu, Kaihua Zhang +5
Recently, template-based trackers have become the leading tracking algorithms with promising performance in terms of efficiency and accuracy. However, the correlation operation bet…
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
Huan Ma, Zongbo Han, Changqing Zhang +3
Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications. However, existing methods…
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…
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…
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.…
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…
Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation
Chaohao Xie, Dongwei Ren, Lei Wang +1
Recently, several weakly supervised learning methods have been devoted to utilize bounding box supervision for training deep semantic segmentation models. Most existing methods usu…