6 papers · 1 filter
YOLO-DS: Fine-Grained Feature Decoupling via Dual-Statistic Synergy Operator for Object Detection
Lin Huang, Yujuan Tan, Weisheng Li +6
One-stage object detection, particularly the YOLO series, strikes a favorable balance between accuracy and efficiency. However, existing YOLO detectors lack explicit modeling of he…
YOLO-PRO: Enhancing Instance-Specific Object Detection with Full-Channel Global Self-Attention
Lin Huang, Yujuan Tan, Weisheng Li +5
This paper addresses the inherent limitations of conventional bottleneck structures (diminished instance discriminability due to overemphasis on batch statistics) and decoupled hea…
Trustworthy Self-Attention: Enabling the Network to Focus Only on the Most Relevant References
Yu Jing, Tan Yujuan, Ren Ao +1
The prediction of optical flow for occluded points is still a difficult problem that has not yet been solved. Recent methods use self-attention to find relevant non-occluded points…
YOIO: You Only Iterate Once by mining and fusing multiple necessary global information in the optical flow estimation
Yu Jing, Tan Yujuan, Ren Ao +1
Occlusions pose a significant challenge to optical flow algorithms that even rely on global evidences. We consider an occluded point to be one that is imaged in the reference frame…
High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection
Lin Huang, Weisheng Li, Yujuan Tan +2
In this study, the structural problems of the YOLOv5 model were analyzed emphatically. Based on the characteristics of fine defects in artificial leather, four innovative structure…
YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification
Lin Huang, Weisheng Li, Yujuan Tan +3
In this study, we examine the associations between channel features and convolutional kernels during the processes of feature purification and gradient backpropagation, with a focu…