Search Results for author:
Found 9 papers, 2 papers with code
Occlusion Aware Unsupervised Learning of Optical Flow From Video
In this paper, we proposed an unsupervised learning method for estimating the optical flow between video frames, especially to solve the occlusion problem.
Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place Recognition
Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame.
Real-time Multi-target Path Prediction and Planning for Autonomous Driving aided by FCN
Real-time multi-target path planning is a key issue in the field of autonomous driving.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition
Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge.
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization
The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework.
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery
In this paper, we propose a method to construct a boundary that discriminates safe and unsafe states.
VNI-Net: Vector Neurons-based Rotation-Invariant Descriptor for LiDAR Place Recognition
To address the significant information loss when formulating rotation-invariant descriptors, we propose computing distances between features at different layers within the Euclidean space neighborhood.
Computational Efficiency
Simultaneous Localization and Mapping
Progressive Dual Priori Network for Generalized Breast Tumor Segmentation
To promote the generalization ability of breast tumor segmentation models, as well as to improve the segmentation performance for breast tumors with smaller size, low-contrast amd irregular shape, we propose a progressive dual priori network (PDPNet) to segment breast tumors from dynamic enhanced magnetic resonance images (DCE-MRI) acquired at different sites.
