3 citations · 4 across the 4 of their papers we have counts for
4 papers
OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection
Jinghua Hou, Tong Wang, Xiaoqing Ye +6
Accurate depth information is crucial for enhancing the performance of multi-view 3D object detection. Despite the success of some existing multi-view 3D detectors utilizing pixel-…
Exploiting Low-level Representations for Ultra-Fast Road Segmentation
Huan Zhou, Feng Xue, Yucong Li +3
Achieving real-time and accuracy on embedded platforms has always been the pursuit of road segmentation methods. To this end, they have proposed many lightweight networks. However,…
CAPE: Camera View Position Embedding for Multi-View 3D Object Detection
Kaixin Xiong, Shi Gong, Xiaoqing Ye +5
In this paper, we address the problem of detecting 3D objects from multi-view images. Current query-based methods rely on global 3D position embeddings (PE) to learn the geometric…
Anomaly Discovery in Semantic Segmentation via Distillation Comparison Networks
Huan Zhou, Shi Gong, Yu Zhou +3
This paper aims to address the problem of anomaly discovery in semantic segmentation. Our key observation is that semantic classification plays a critical role in existing approach…