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Found 35 papers, 9 papers with code
YOLO-MED : Multi-Task Interaction Network for Biomedical Images
Object detection and semantic segmentation are pivotal components in biomedical image analysis.
Federated Reinforcement Learning for Uplink Centric Broadband Communication Optimization over Unlicensed Spectrum
In this paper, we first develop a centralized double Deep Q-Network (DDQN) algorithm to optimize the uplink system throughput, where the agent is deployed at the central server to dynamically adjust the ED thresholds for NR-U and WiFi networks.
BDEC:Brain Deep Embedded Clustering model
An essential premise for neuroscience brain network analysis is the successful segmentation of the cerebral cortex into functionally homogeneous regions.
LSSED: A Robust Segmentation Network for Inflamed Appendix from CT Images
Acute appendicitis (AA) is one of the most prevalent surgical acute abdominal condition diseases.
ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds
In this paper, we propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation.
Energy-Efficient Cellular-Connected UAV Swarm Control Optimization
To maximize the number of UAVs that receive the message successfully within the latency and energy constraints, we formulate the problem as a Constrained Markov Decision Process to find the optimal policy.
Goal-Oriented Semantic Communications for 6G Networks
In this article, we propose a generic goal-oriented semantic communication framework for various tasks with diverse data types, which incorporates both semantic level information and effectiveness-aware performance metrics.
LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Network
In this work, we address the task of LiDAR-based panoptic segmentation, which aims to parse both objects and scenes in a unified manner.
AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach
Compared to previous methods, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline.
Code Clone Detection based on Event Embedding and Event Dependency
To address this issue, we propose a code clone detection method based on semantic similarity.
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception
In this paper, we benchmark our model on these three tasks.
Revisiting Recursive Least Squares for Training Deep Neural Networks
In this paper, to overcome these drawbacks, we propose three novel RLS optimization algorithms for training feedforward neural networks, convolutional neural networks and recurrent neural networks (including long short-term memory networks), by using the error backpropagation and our average-approximation RLS method, together with the equivalent gradients of the linear least squares loss function with respect to the linear outputs of hidden layers.
LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation
However, two major issues of the fusion between camera and LiDAR hinder its performance, \ie, how to effectively fuse these two modalities and how to precisely align them (suffering from the weak spatiotemporal synchronization problem).
Machine Learning for Massive Industrial Internet of Things
With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements.
Analyzing Novel Grant-Based and Grant-Free Access Schemes for Small Data Transmission
Motivated by this, 3GPP has proposed 4/2-step SDT RA schemes based on the existing grant-based (4-step) and grant-free (2-step) RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state.
Real-time Video Streaming and Control of Cellular-Connected UAV System: Prototype and Performance Evaluation
Unmanned aerial vehicles (UAVs) play an increasingly important role in military, public, and civilian applications, where providing connectivity to UAVs is crucial for its real-time control, video streaming, and data collection.
LiDAR-based Panoptic Segmentation via Dynamic Shifting Network
2) Dynamic Shifting for complex point distributions.
Ranked #2 on
Panoptic Segmentation
on SemanticKITTI
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited.
Ranked #2 on
3D Semantic Segmentation
on ScribbleKITTI
A Vision of XR-aided Teleoperation System Towards 5G/B5G
Extended Reality (XR)-aided teleoperation has shown its potential in improving operating efficiency in mission-critical, rich-information and complex scenarios.
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space.
Ranked #11 on
LIDAR Semantic Segmentation
on nuScenes
Skinning a Parameterization of Three-Dimensional Space for Neural Network Cloth
We present a novel learning framework for cloth deformation by embedding virtual cloth into a tetrahedral mesh that parametrizes the volumetric region of air surrounding the underlying body.
Imitation Learning for Fashion Style Based on Hierarchical Multimodal Representation
In this work, we propose an adversarial inverse reinforcement learning formulation to recover reward functions based on hierarchical multimodal representation (HM-AIRL) during the imitation process.
AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching
Compared to previous methods for adaptive stereo matching, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline.
SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud
First, the semantic context information in LiDAR is seldom explored in previous works, which may help identify ambiguous vehicles.
Recovering Geometric Information with Learned Texture Perturbations
Regularization is used to avoid overfitting when training a neural network; unfortunately, this reduces the attainable level of detail hindering the ability to capture high-frequency information present in the training data.
Robust Multi-Modality Multi-Object Tracking
Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential movement of dynamic objects.
Ranked #10 on
Multiple Object Tracking
on KITTI Tracking test
3D Virtual Garment Modeling from RGB Images
The predicted landmarks are used for estimating sizing information of the garment.
Pose Guided Fashion Image Synthesis Using Deep Generative Model
In order to formulate the framework, we employ one generator and two discriminators for image synthesis.
Ranking-Based Autoencoder for Extreme Multi-label Classification
Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers of labels, features and instances could be thousands or millions.
Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation
Due to the expensive and time-consuming annotations (e. g., segmentation) for real-world images, recent works in computer vision resort to synthetic data.
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking
In addition, inter-object relations are mostly modeled in a symmetric way, which we argue is not an optimal setting.
Screening of Fungi for the Application of Self-Healing Concrete
Therefore, a screening of different species of fungi has been conducted in this study.
Interactions of Fungi with Concrete: Significant Importance for Bio-Based Self-Healing Concrete
The goal of this study is to explore a new self-healing concept in which fungi are used as a self-healing agent to promote calcium mineral precipitation to fill the cracks in concrete.
Crafting GBD-Net for Object Detection
The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
Blur-Aware Disparity Estimation From Defocus Stereo Images
We propose a blur-aware disparity estimation method that is robust to the mismatch of focus in stereo images.
