Search Results for author:
Found 18 papers, 3 papers with code
Image Captioning in news report scenario
Image captioning strives to generate pertinent captions for specified images, situating itself at the crossroads of Computer Vision (CV) and Natural Language Processing (NLP).
Rumor Detection with a novel graph neural network approach
In this paper, we propose a new detection model, that jointly learns both the representations of user correlation and information propagation to detect rumors on social media.
Particle Filter SLAM for Vehicle Localization
Simultaneous Localization and Mapping (SLAM) presents a formidable challenge in robotics, involving the dynamic construction of a map while concurrently determining the precise location of the robotic agent within an unfamiliar environment.
News Recommendation with Attention Mechanism
This paper explores the area of news recommendation, a key component of online information sharing.
Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning
By developing a differentiable mesh-to-image rasterizer, the method is able to leverage the anatomical shape information from 2D multi-view CMR images for 3D motion estimation.
Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames given an object template.
TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection
3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics.
MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI
To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart.
DURRNet: Deep Unfolded Single Image Reflection Removal Network
With the deep unrolling technique, we build the DURRNet with ProxNets to model natural image priors and ProxInvNets which are constructed with invertible networks to impose the exclusion prior.
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud.
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts.
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames.
A framework for learned sparse sketches
In this work, we consider the problem of optimizing sketches to obtain low approximation error over a data distribution.
Learning the Positions in CountSketch
Despite the growing body of work on this paradigm, a noticeable omission is that the locations of the non-zero entries of previous algorithms were fixed, and only their values were learned.
Ultrasound Video Summarization using Deep Reinforcement Learning
We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network.
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions.
SAM-RCNN: Scale-Aware Multi-Resolution Multi-Channel Pedestrian Detection
Convolutional neural networks (CNN) have enabled significant improvements in pedestrian detection owing to the strong representation ability of the CNN features.
