15 citations · 52 across the 13 of their papers we have counts for
13 papers
Towards 3D Object Detection with 2D Supervision
Jinrong Yang, Tiancai Wang, Zheng Ge +3
The great progress of 3D object detectors relies on large-scale data and 3D annotations. The annotation cost for 3D bounding boxes is extremely expensive while the 2D ones are easi…
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking
Pan Wang, Liangliang Ren, Shengkai Wu +4
The point cloud based 3D single object tracking has drawn increasing attention. Although many breakthroughs have been achieved, we also reveal two severe issues. By extensive analy…
Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking
Jinrong Yang, En Yu, Zeming Li +2
3D Multi-Object Tracking (MOT) has achieved tremendous achievement thanks to the rapid development of 3D object detection and 2D MOT. Recent advanced works generally employ a serie…
StreamYOLO: Real-time Object Detection for Streaming Perception
Jinrong Yang, Songtao Liu, Zeming Li +2
The perceptive models of autonomous driving require fast inference within a low latency for safety. While existing works ignore the inevitable environmental changes after processin…
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Jinrong Yang, Lin Song, Songtao Liu +6
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference. These strategies are typically based on fixed and handcrafted rul…
A Semantic Consistency Feature Alignment Object Detection Model Based on Mixed-Class Distribution Metrics
Lijun Gou, Jinrong Yang, Hangcheng Yu +3
Unsupervised domain adaptation is critical in various computer vision tasks, such as object detection, instance segmentation, etc. They attempt to reduce domain bias-induced perfor…
Real-time Object Detection for Streaming Perception
Jinrong Yang, Songtao Liu, Zeming Li +2
Autonomous driving requires the model to perceive the environment and (re)act within a low latency for safety. While past works ignore the inevitable changes in the environment aft…
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
Zonghang Li, Yihong He, Hongfang Yu +4
Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate o…
Carton dataset synthesis method for domain shift based on foreground texture decoupling and replacement
Lijun Gou, Shengkai Wu, Jinrong Yang +4
One major impediment in rapidly deploying object detection models for industrial applications is the lack of large annotated datasets. We currently have presented the Sacked Carton…
Gaussian Guided IoU: A Better Metric for Balanced Learning on Object Detection
Shengkai Wu, Jinrong Yang, Hangcheng Yu +2
For most of the anchor-based detectors, Intersection over Union(IoU) is widely utilized to assign targets for the anchors during training. However, IoU pays insufficient attention…
SCD: A Stacked Carton Dataset for Detection and Segmentation
Jinrong Yang, Shengkai Wu, Lijun Gou +5
Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons, the unloading o…
IoU-aware Single-stage Object Detector for Accurate Localization
Shengkai Wu, Xiaoping Li, Xinggang Wang
Due to the simpleness and high efficiency, single-stage object detectors have been widely applied in many computer vision applications . However, the low correlation between the cl…
IoU-balanced Loss Functions for Single-stage Object Detection
Shengkai Wu, Jinrong Yang, Xinggang Wang +1
Single-stage object detectors have been widely applied in computer vision applications due to their high efficiency. However, we find that the loss functions adopted by single-stag…