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Found 44 papers, 18 papers with code
Learning to See in the Dark with Events
A detail enhancing branch is proposed to reconstruct day light-specific features from the domain-invariant representations in a residual manner, regularized by a ranking loss.
Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection
Detecting leaks in water networks is a costly challenge.
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory
In Part 1, the theory and framework of this novel market design philosophy are proposed.
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications
This two-part paper develops a paradigmatic theory and detailed methods of the joint electricity market design using reinforcement-learning (RL)-based simulation.
Improving Knowledge Graph Entity Alignment with Graph Augmentation
Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion.
RIS-Assisted Self-Interference Mitigation for In-Band Full-Duplex Transceivers
The wireless in-band full-duplex (IBFD) technology can in theory double the system capacity over the conventional frequency division duplex (FDD) or time-division duplex (TDD) alternatives.
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning
In this work, we propose a stance detection approach that can efficiently adapt to unseen targets, the core of which is to capture target-invariant syntactic expression patterns as transferable knowledge.
Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies
The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide.
Inter- and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting
In Inter-Series Embedding Module, a multi-scale unified convolution component called Region-Aware Convolution is proposed, which cooperates with self-attention to capture dynamic dependencies between time series obtained from multiple regions.
EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health.
Aesthetics Driven Autonomous Time-Lapse Photography Generation by Virtual and Real Robots
Then we propose a time-lapse photography interface to facilitate users to view and adjust parameters and use virtual robots to conduct virtual photography in a three-dimensional scene.
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review
The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms.
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors
Given a multivariate big time series, can we detect anomalies as soon as they occur?
Autofocus for Event Cameras
Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios.
Learning Semantic Abstraction of Shape via 3D Region of Interest
This is in contrast to current methods, which focus solely on either 3D shape abstraction or semantic analysis.
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing
Recent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling language.
Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images
Different from existing methods that represent an indoor scene with the type, location, and other properties of objects in the room and learn the scene layout from a collection of complete 3D indoor scenes, our method models each indoor scene as a 3D semantic scene volume and learns a volumetric generative adversarial network (GAN) from a collection of 2. 5D partial observations of 3D scenes.
A Cloud-Edge-Terminal Collaborative System for Temperature Measurement in COVID-19 Prevention
Then, a mobile detection model based on a multi-task cascaded convolutional network (MTCNN) is proposed to realize face alignment and mask detection on the RGB images.
Mobile Optical Communications Using Second Harmonic of Intra-Cavity Laser
In this paper, we propose an intra-cavity SHG RBCom system to simplify the system design and improve the energy conversion efficiency.
Learning Fine-Grained Segmentation of 3D Shapes without Part Labels
Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags.
Robust 2D/3D Vehicle Parsing in CVIS
We combine these novel algorithms and datasets to develop a robust approach for 2D/3D vehicle parsing for CVIS.
Hybrid Interference Mitigation Using Analog Prewhitening
This paper proposes a novel scheme for mitigating strong interferences, which is applicable to various wireless scenarios, including full-duplex wireless communications and uncoordinated heterogenous networks.
Topological excitonic corner states and nodal phase in bilayer quantum spin Hall insulators
Interaction induced topological states remain one of the fascinating phases in condensed matter physics.
Mesoscale and Nanoscale Physics Superconductivity
Robust 2D/3D Vehicle Parsing in Arbitrary Camera Views for CVIS
We combine these novel algorithms and datasets to develop a robust approach for 2D/3D vehicle parsing for CVIS.
Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation
For autonomous driving, the dynamics and states of vehicle parts such as doors, the trunk, and the bonnet can provide meaningful semantic information and interaction states, which are essential to ensuring the safety of the self-driving vehicle.
Nature of $X(6900)$ and its production mechanism at LHCb
We show that the Pomeron exchanges play a unique role in vector charmonium scatterings.
High Energy Physics - Phenomenology
Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment
Our model can judge whether a UAV video was shot by professional photographers or amateurs together with the scene type classification.
Self-Supervised Learning of Part Mobility from Point Cloud Sequence
To the best of our knowledge, this is the first study on deep learning to extract part mobility from point cloud sequence of a dynamic object.
CSTNet: A Dual-Branch Convolutional Network for Imaging of Reactive Flows using Chemical Species Tomography
Chemical Species Tomography (CST) has been widely used for in situ imaging of critical parameters, e. g. species concentration and temperature, in reactive flows.
Incomplete Utterance Rewriting as Semantic Segmentation
Recent years the task of incomplete utterance rewriting has raised a large attention.
Ranked #1 on
Dialogue Rewriting
on Rewrite
PerMO: Perceiving More at Once from a Single Image for Autonomous Driving
We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving.
PIE-NET: Parametric Inference of Point Cloud Edges
We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data.
Compositional Generalization by Learning Analytical Expressions
Compositional generalization is a basic and essential intellective capability of human beings, which allows us to recombine known parts readily.
You Impress Me: Dialogue Generation via Mutual Persona Perception
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors.
Ranked #2 on
Dialogue Generation
on Persona-Chat
How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context
Recently semantic parsing in context has received considerable attention, which is challenging since there are complex contextual phenomena.
FLATM: A Fuzzy Logic Approach Topic Model for Medical Documents
One of the challenges for text analysis in medical domains is analyzing large-scale medical documents.
A Split-and-Recombine Approach for Follow-up Query Analysis
To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information.
Aesthetic Attributes Assessment of Images
This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute.
Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes
For the task of mobility analysis of 3D shapes, we propose joint analysis for simultaneous motion part segmentation and motion attribute estimation, taking a single 3D model as input.
Sample Efficient Deep Neuroevolution in Low Dimensional Latent Space
Current deep neuroevolution models are usually trained in a large parameter search space for complex learning tasks, e. g. playing video games, which needs billions of samples and thousands of search steps to obtain significant performance.
Learning to Group and Label Fine-Grained Shape Components
We propose to generate part hypotheses from the components based on a hierarchical grouping strategy, and perform labeling on those part groups instead of directly on the components.
Weaving Multi-scale Context for Single Shot Detector
For 320x320 input of batch size = 8, WeaveNet reaches 79. 5% mAP on PASCAL VOC 2007 test in 101 fps with only 4 fps extra cost, and further improves to 79. 7% mAP with more iterations.
Primary Video Object Segmentation via Complementary CNNs and Neighborhood Reversible Flow
By applying CCNN on each video frame, the spatial foregroundness and backgroundness maps can be initialized, which are then propagated between various frames so as to segment primary video objects and suppress distractors.
Fuzzy Approach Topic Discovery in Health and Medical Corpora
The majority of medical documents and electronic health records (EHRs) are in text format that poses a challenge for data processing and finding relevant documents.
