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Found 39 papers, 12 papers with code
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains).
Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model
Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies.
ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
Data-driven weather forecast based on machine learning (ML) has experienced rapid development and demonstrated superior performance in the global medium-range forecast compared to traditional physics-based dynamical models.
Is Artificial Intelligence Providing the Second Revolution for Weather Forecasting?
The rapid advancement of artificial intelligence technologies, particularly in recent years, has led to the emergence of several large parameter artificial intelligence weather forecast models.
FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting
Therefore, building a kilometer-scale global forecast model is a persistent pursuit in the meteorology domain.
Towards an end-to-end artificial intelligence driven global weather forecasting system
Initial states are typically generated by traditional data assimilation components, which are computational expensive and time-consuming.
FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation
Weather forecasting is a crucial yet highly challenging task.
Traffic Sign Interpretation in Real Road Scene
Following the above issues, we propose a traffic sign interpretation (TSI) task, which aims to interpret global semantic interrelated traffic signs (e. g.,~driving instruction-related texts, symbols, and guide panels) into a natural language for providing accurate instruction support to autonomous or assistant driving.
STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning
Scale variation is a deep-rooted problem in object counting, which has not been effectively addressed by existing scale-aware algorithms.
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning
Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts composed of known knowledge without training samples.
FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead
We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI).
Atlas: Automate Online Service Configuration in Network Slicing
First, we design a learning-based simulator to reduce the sim-to-real discrepancy, which is accomplished by a new parameter searching method based on Bayesian optimization.
Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition
One of limitations in end-to-end automatic speech recognition (ASR) framework is its performance would be compromised if train-test utterance lengths are mismatched.
DR.VIC: Decomposition and Reasoning for Video Individual Counting
Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame.
DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets
In this paper, we propose DeepMix, a mobility-aware, lightweight, and hybrid 3D object detection framework for improving the user experience of AR/MR on mobile headsets.
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing
Knowledge Tracing is the process of tracking mastery level of different skills of students for a given learning domain.
OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning
As online learning is converged, OnSlicing reduces 12. 5% usage without any violations as compared to the state-of-the-art online DRL solution.
LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds
The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
Supervised Contrastive Learning for Accented Speech Recognition
Neural network based speech recognition systems suffer from performance degradation due to accented speech, especially unfamiliar accents.
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
MutualNet is a general training methodology that can be applied to various network structures (e. g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e. g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets.
References of References: How Far is the Knowledge Ancestry
Scientometrics studies have extended from direct citations to high-order citations, as simple citation count is found to tell only part of the story regarding scientific impact.
Information Retrieval
Digital Libraries
Electroweak Phase Transition with an SU(2) Dark Sector
We investigate two different scenarios of the dark sector scalars with $Z_2$ symmetry, with Higgs portal interactions that can introduce mixing between the SM Higgs boson and the SM singlet scalars in the dark sector.
High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics
LiveMap: Real-Time Dynamic Map in Automotive Edge Computing
In this paper, we propose LiveMap, a real-time dynamic map, that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second.
Learning Independent Instance Maps for Crowd Localization
Furthermore, to improve the segmentation quality for different density regions, we present a differentiable Binarization Module (BM) to output structured instance maps.
Answer-checking in Context: A Multi-modal FullyAttention Network for Visual Question Answering
From the human perspective, to answer a visual question, one needs to read the question and then refer to the image to generate an answer.
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating.
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering
Positional information of text is underused and there is a lack of evidence for the generated answer.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View
Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks.
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond.
Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting
Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn domain shift parameters.
Focus on Semantic Consistency for Cross-domain Crowd Understanding
According to the semantic consistency, a similar distribution in deep layer's features of the synthetic and real-world crowd area, we first introduce a semantic extractor to effectively distinguish crowd and background in high-level semantic information.
Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction
Recently, crowd counting using supervised learning achieves a remarkable improvement.
Robust Shape Estimation for 3D Deformable Object Manipulation
Existing shape estimation methods for deformable object manipulation suffer from the drawbacks of being off-line, model dependent, noise-sensitive or occlusion-sensitive, and thus are not appropriate for manipulation tasks requiring high precision.
Robotics
Type-II 2HDM under the Precision Measurements at the $Z$-pole and a Higgs Factory
We also find that the expected accuracies at the $Z$-pole and at a Higgs factory are quite complementary in constraining mass splittings of heavy Higgs bosons.
High Energy Physics - Phenomenology
Higgs Couplings at High Scales
We study the off-shell production of the Higgs boson at the LHC to probe Higgs physics at higher energy scales utilizing the process $g g \rightarrow h^{*} \rightarrow ZZ$.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Off-shell Higgs Probe to Naturalness
Examining the Higgs sector at high energy scales through off-shell Higgs production can potentially shed light on the naturalness problem of the Higgs mass.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Bi-objective Optimization for Robust RGB-D Visual Odometry
This paper considers a new bi-objective optimization formulation for robust RGB-D visual odometry.
