4 citations · 4 across the 1 of their papers we have counts for
6 papers
PointTrack++ for Effective Online Multi-Object Tracking and Segmentation
Zhenbo Xu, Wei Zhang, Xiao Tan +7
Multiple-object tracking and segmentation (MOTS) is a novel computer vision task that aims to jointly perform multiple object tracking (MOT) and instance segmentation. In this work…
Perspective-Guided Convolution Networks for Crowd Counting
Zhaoyi Yan, Yuchen Yuan, Wangmeng Zuo +4
In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i.e. PGCNet), which aims to overcome the dramati…
Recognizing Part Attributes with Insufficient Data
Xiangyun Zhao, Yi Yang, Feng Zhou +4
Recognizing attributes of objects and their parts is important to many computer vision applications. Although great progress has been made to apply object-level recognition, recogn…
Deep Density-aware Count Regressor
Zhuojun Chen, Junhao Cheng, Yuchen Yuan +3
We seek to improve crowd counting as we perceive limits of currently prevalent density map estimation approach on both prediction accuracy and time efficiency. We leverage multilev…
Compact Generalized Non-local Network
Kaiyu Yue, Ming Sun, Yuchen Yuan +3
The non-local module is designed for capturing long-range spatio-temporal dependencies in images and videos. Although having shown excellent performance, it lacks the mechanism to…
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
Ming Sun, Yuchen Yuan, Feng Zhou +1
Attention-based learning for fine-grained image recognition remains a challenging task, where most of the existing methods treat each object part in isolation, while neglecting the…