50 citations · 139 across the 13 of their papers we have counts for
13 papers · 1 filter
RCLane: Relay Chain Prediction for Lane Detection
Shenghua Xu, Xinyue Cai, Bin Zhao +4
Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark i…
ONCE-3DLanes: Building Monocular 3D Lane Detection
Fan Yan, Ming Nie, Xinyue Cai +7
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. Conventional 2D lane detection from a monocular image yields poor performa…
ImpDet: Exploring Implicit Fields for 3D Object Detection
Xuelin Qian, Li Wang, Yi Zhu +3
Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i.e., localization, dimension, and orientation. Despite i…
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
Likun Cai, Zhi Zhang, Yi Zhu +3
Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we cons…
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection
Zhikang Zou, Xiaoqing Ye, Liang Du +6
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. In this paper, we dig into the 3D object…
SGM3D: Stereo Guided Monocular 3D Object Detection
Zheyuan Zhou, Liang Du, Xiaoqing Ye +5
Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image. It poses a grea…
Progressive Coordinate Transforms for Monocular 3D Object Detection
Li Wang, Li Zhang, Yi Zhu +4
Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment. While significant progress has been achieved with e…
A Unified Efficient Pyramid Transformer for Semantic Segmentation
Fangrui Zhu, Yi Zhu, Li Zhang +3
Semantic segmentation is a challenging problem due to difficulties in modeling context in complex scenes and class confusions along boundaries. Most literature either focuses on co…
Delving into Data: Effectively Substitute Training for Black-box Attack
Wenxuan Wang, Bangjie Yin, Taiping Yao +6
Deep models have shown their vulnerability when processing adversarial samples. As for the black-box attack, without access to the architecture and weights of the attacked model, t…
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
Li Wang, Liang Du, Xiaoqing Ye +5
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection. We make following contributions: (i)…
Learning Dynamic Alignment via Meta-filter for Few-shot Learning
Chengming Xu, Chen Liu, Li Zhang +5
Few-shot learning (FSL), which aims to recognise new classes by adapting the learned knowledge with extremely limited few-shot (support) examples, remains an important open problem…
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Yuqian Fu, Li Zhang, Junke Wang +2
Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs. This obse…
Long-Term Cloth-Changing Person Re-identification
Xuelin Qian, Wenxuan Wang, Li Zhang +5
Person re-identification (Re-ID) aims to match a target person across camera views at different locations and times. Existing Re-ID studies focus on the short-term cloth-consistent…