4 citations · 4 across the 3 of their papers we have counts for
8 papers
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition
Zhitong Xiong, Yuan Yuan, Qi Wang
Indoor scene images usually contain scattered objects and various scene layouts, which make RGB-D scene classification a challenging task. Existing methods still have limitations f…
Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection
Zhinan Cai, Zhiyu Jiang, Yuan Yuan
Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. However, most of the existing CNN-based change detect…
Deep feature selection-and-fusion for RGB-D semantic segmentation
Yuejiao Su, Yuan Yuan, Zhiyu Jiang
Scene depth information can help visual information for more accurate semantic segmentation. However, how to effectively integrate multi-modality information into representative fe…
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
Tao Han, Junyu Gao, Yuan Yuan +1
Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learn…
Focus on Semantic Consistency for Cross-domain Crowd Understanding
Tao Han, Junyu Gao, Yuan Yuan +1
For pixel-level crowd understanding, it is time-consuming and laborious in data collection and annotation. Some domain adaptation algorithms try to liberate it by training models w…
SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting
Junyu Gao, Qi Wang, Yuan Yuan
Recently, crowd counting is a hot topic in crowd analysis. Many CNN-based counting algorithms attain good performance. However, these methods only focus on the local appearance fea…