13 citations · 21 across the 4 of their papers we have counts for
5 papers
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception
Yiming Cui, Zhiwen Cao, Yixin Xie +5
Multi-object tracking and segmentation (MOTS) is a critical task for autonomous driving applications. The existing MOTS studies face two critical challenges: 1) the published datas…
SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation
Dongfang Liu, Yiming Cui, Wenbo Tan +1
Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, le…
Hierarchical Attention Fusion for Geo-Localization
Liqi Yan, Yiming Cui, Yingjie Chen +1
Geo-localization is a critical task in computer vision. In this work, we cast the geo-localization as a 2D image retrieval task. Current state-of-the-art methods for 2D geo-localiz…
DenserNet: Weakly Supervised Visual Localization Using Multi-scale Feature Aggregation
Dongfang Liu, Yiming Cui, Liqi Yan +3
In this work, we introduce a Denser Feature Network (DenserNet) for visual localization. Our work provides three principal contributions. First, we develop a convolutional neural n…
Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze
Dongfang Liu, Yiming Cui, Xiaolei Guo +3
Accurate localization is a foundational capacity, required for autonomous vehicles to accomplish other tasks such as navigation or path planning. It is a common practice for vehicl…