32 citations · 71 across the 7 of their papers we have counts for
7 papers
RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation
Ke Fan, Changan Wang, Yabiao Wang +3
Glass-like objects are widespread in daily life but remain intractable to be segmented for most existing methods. The transparent property makes it difficult to be distinguished fr…
HDNet: A Hierarchically Decoupled Network for Crowd Counting
Chenliang Gu, Changan Wang, Bin-Bin Gao +2
Recently, density map regression-based methods have dominated in crowd counting owing to their excellent fitting ability on density distribution. However, further improvement tends…
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
Zhiwei Chen, Changan Wang, Yabiao Wang +6
Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result i…
Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework
Qingyu Song, Changan Wang, Zhengkai Jiang +6
Localizing individuals in crowds is more in accordance with the practical demands of subsequent high-level crowd analysis tasks than simply counting. However, existing localization…
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting
Changan Wang, Qingyu Song, Boshen Zhang +7
Recently, the problem of inaccurate learning targets in crowd counting draws increasing attention. Inspired by a few pioneering work, we solve this problem by trying to predict the…
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
Jinlong Peng, Changan Wang, Fangbin Wan +7
Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or…
DSFD: Dual Shot Face Detector
Jian Li, Yabiao Wang, Changan Wang +6
In this paper, we propose a novel face detection network with three novel contributions that address three key aspects of face detection, including better feature learning, progres…