114 citations · 315 across the 13 of their papers we have counts for
14 papers
Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer
Shaoyu Chen, Tianheng Cheng, Xinggang Wang +3
Learning Bird's Eye View (BEV) representation from surrounding-view cameras is of great importance for autonomous driving. In this work, we propose a Geometry-guided Kernel Transfo…
Cross-Image Relational Knowledge Distillation for Semantic Segmentation
Chuanguang Yang, Helong Zhou, Zhulin An +3
Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher's structured information generated from individual data samples.…
Road-aware Monocular Structure from Motion and Homography Estimation
Wei Sui, Teng Chen, Jiaxin Zhang +2
Structure from motion (SFM) and ground plane homography estimation are critical to autonomous driving and other robotics applications. Recently, much progress has been made in usin…
Monocular Road Planar Parallax Estimation
Haobo Yuan, Teng Chen, Wei Sui +4
Estimating the 3D structure of the drivable surface and surrounding environment is a crucial task for assisted and autonomous driving. It is commonly solved either by using 3D sens…
Hierarchical Aggregation for 3D Instance Segmentation
Shaoyu Chen, Jiemin Fang, Qian Zhang +2
Instance segmentation on point clouds is a fundamental task in 3D scene perception. In this work, we propose a concise clustering-based framework named HAIS, which makes full use o…
Deep Online Correction for Monocular Visual Odometry
Jiaxin Zhang, Wei Sui, Xinggang Wang +3
In this work, we propose a novel deep online correction (DOC) framework for monocular visual odometry. The whole pipeline has two stages: First, depth maps and initial poses are ob…
ResizeMix: Mixing Data with Preserved Object Information and True Labels
Jie Qin, Jiemin Fang, Qian Zhang +3
Data augmentation is a powerful technique to increase the diversity of data, which can effectively improve the generalization ability of neural networks in image recognition tasks.…
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Xinyu Gong, Wuyang Chen, Yifan Jiang +5
We present AutoPose, a novel neural architecture search(NAS) framework that is capable of automatically discovering multiple parallel branches of cross-scale connections towards ac…
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
Jiemin Fang, Yuzhu Sun, Qian Zhang +4
Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural n…
FasterSeg: Searching for Faster Real-time Semantic Segmentation
Wuyang Chen, Xinyu Gong, Xianming Liu +3
We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neura…
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search
Jiemin Fang, Yuzhu Sun, Kangjian Peng +4
Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural n…
AugFPN: Improving Multi-scale Feature Learning for Object Detection
Chaoxu Guo, Bin Fan, Qian Zhang +2
Current state-of-the-art detectors typically exploit feature pyramid to detect objects at different scales. Among them, FPN is one of the representative works that build a feature…
Learning Where to Focus for Efficient Video Object Detection
Zhengkai Jiang, Yu Liu, Ceyuan Yang +5
Transferring existing image-based detectors to the video is non-trivial since the quality of frames is always deteriorated by part occlusion, rare pose, and motion blur. Previous a…
Densely Connected Search Space for More Flexible Neural Architecture Search
Jiemin Fang, Yuzhu Sun, Qian Zhang +3
Neural architecture search (NAS) has dramatically advanced the development of neural network design. We revisit the search space design in most previous NAS methods and find the nu…