Search Results for author:
Found 9 papers, 3 papers with code
Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles
Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation.
VLTSeg: Simple Transfer of CLIP-Based Vision-Language Representations for Domain Generalized Semantic Segmentation
We thus propose a new vision-language approach for domain generalized segmentation, which improves the domain generalization SOTA by 7. 6% mIoU when training on the synthetic GTA5 dataset.
Ranked #1 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior
This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice.
Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration
Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm.
Spatio-temporal Tendency Reasoning for Human Body Pose and Shape Estimation from Videos
Different from them, our STR aims to learn accurate and natural motion sequences in an unconstrained environment through temporal and spatial tendency and to fully excavate the spatio-temporal features of existing video data.
Ranked #55 on
3D Human Pose Estimation
on MPI-INF-3DHP
OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction
Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text.
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis.
Ranked #3 on
Medical Image Segmentation
on ACDC
Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection
Extensive experiments on two public grasping datasets, Cornell and Jacquard demonstrate the state-of-the-art performance of our method in balancing accuracy and inference speed.
Ranked #1 on
Robotic Grasping
on Jacquard dataset
Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset
Based on the Event-Stream dataset, we develop a deep neural network for grasping detection which consider the angle learning problem as classification instead of regression.
