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Found 22 papers, 1 papers with code
Rethinking Irregular Scene Text Recognition
Reading text from natural images is challenging due to the great variety in text font, color, size, complex background and etc..
Distributed Private Online Learning for Social Big Data Computing over Data Center Networks
An increasing number of people participate in social networks and massive online social data are obtained.
Look into My Eyes: Fine-grained Detection of Face-screen Distance on Smartphones
The detection of face-screen distance on smartphone (i. e., the distance between the user face and the smartphone screen) is of paramount importance for many mobile applications, including dynamic adjustment of screen on-off, screen resolution, screen luminance, font size, with the purposes of power saving, protection of human eyesight, etc.
Networking and Internet Architecture
ImgSensingNet: UAV Vision Guided Aerial-Ground Air Quality Sensing System
Given the increasingly serious air pollution problem, the monitoring of air quality index (AQI) in urban areas has drawn considerable attention.
Reprojection R-CNN: A Fast and Accurate Object Detector for 360° Images
Specifically, we adapt the terminologies of the traditional object detection task to the omnidirectional scenarios, and propose a novel two-stage object detector, i. e., Reprojection R-CNN by combining both ERP and perspective projection.
Symmetry-constrained Rectification Network for Scene Text Recognition
Reading text in the wild is a very challenging task due to the diversity of text instances and the complexity of natural scenes.
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach
Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning.
A New Perspective for Flexible Feature Gathering in Scene Text Recognition Via Character Anchor Pooling
Irregular scene text recognition has attracted much attention from the research community, mainly due to the complexity of shapes of text in natural scene.
Federated Neural Architecture Search
To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data.
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System
Specifically, AMEIR divides the complete recommendation models into three stages of behavior modeling, interaction exploration, MLP aggregation, and introduces a novel search space containing three tailored subspaces that cover most of the existing methods and thus allow for searching better models.
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings.
Differentiable Feature Aggregation Search for Knowledge Distillation
To tackle with both the efficiency and the effectiveness of knowledge distillation, we introduce the feature aggregation to imitate the multi-teacher distillation in the single-teacher distillation framework by extracting informative supervision from multiple teacher feature maps.
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search
The convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment.
TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting
Further analysis indicates that the locality and globality of the traffic networks are critical to traffic flow prediction and the proposed TSSRGCN model can adapt to the various temporal traffic patterns.
MetaLocalization: Reconfigurable Intelligent Surface Aided Multi-user Wireless Indoor Localization
As the RIS is able to customize the radio channels by adjusting the phase shifts of the signals reflected at the surface, the localization accuracy in the RIS aided scheme can be improved by choosing the proper phase shifts with significant differences of RSS values among adjacent locations.
MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning
To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals.
Towards Ubiquitous Positioning by Leveraging Reconfigurable Intelligent Surface
The received signal strength (RSS) based technique is widely utilized for ubiquitous positioning due to its advantage of simple implementability.
MetaRadar: Indoor Localization by Reconfigurable Metamaterials
However, in MetaRadar, it is challenging to build radio maps for all the radio environments generated by metamaterial units and select suitable maps from all the possible maps to realize a high accuracy localization.
Wireless Indoor Simultaneous Localization and Mapping Using Reconfigurable Intelligent Surface
In this paper, we propose a novel SLAM system assisted by a reconfigurable intelligent surface (RIS) to address this issue.
Meta-material Sensors based Internet of Things for 6G Communications
In the coming 6G communications, the internet of things (IoT) serves as a key enabler to collect environmental information and is expected to achieve ubiquitous deployment.
MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces
Semantic segmentation is a process of partitioning an image into multiple segments for recognizing humans and objects, which can be widely applied in scenarios such as healthcare and safety monitoring.
MetaRadar: Multi-target Detection for Reconfigurable Intelligent Surface Aided Radar Systems
To address this issue, we propose to use the reconfigurable intelligent surface (RIS) to improve the detection accuracy of radar systems due to its capability to customize channel conditions by adjusting its phase shifts, which is referred to as MetaRadar.
