Search Results for author:
Found 43 papers, 9 papers with code
MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media
The way information is generated and disseminated has changed dramatically over the last decade.
MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction
This paper is the first to propose an end-to-end framework of mutually reinforcing images to 3D surface recurrent neural network-like for model-adaptation indoor 3D reconstruction, where multi-view dense matching and point cloud surface optimization are mutually reinforced by a RNN-like structure rather than being treated as a separate issue. The characteristics are as follows:In the multi-view dense matching module, the model-adaptation strategy is used to fine-tune and optimize a Transformer-based multi-view dense matching DNN, so that it has the higher image feature for matching and detail expression capabilities;In the point cloud surface optimization module, the 3D surface reconstruction network based on 3D implicit field is optimized by using model-adaptation strategy, which solves the problem of point cloud surface optimization without knowing normal vector of 3D surface. To improve and finely reconstruct 3D surfaces from point cloud, smooth loss is proposed and added to this module;The MRIo3DS-Net is a RNN-like framework, which utilizes the finely optimized 3D surface obtained by PCSOM to recursively reinforce the differentiable warping for optimizing MVDMM. This refinement leads to achieving better dense matching results, and better dense matching results leads to achieving better 3D surface results recursively and mutually. Hence, model-adaptation strategy can better collaborate the differences between the two network modules, so that they complement each other to achieve the better effect;To accelerate the transfer learning and training convergence from source domain to target domain, a multi-task loss function based on Bayesian uncertainty is used to adaptively adjust the weights between the two networks loss functions of MVDMM and PCSOM;In this multi-task cascade network framework, any modules can be replaced by any state-of-the-art networks to achieve better 3D reconstruction results.
ISWSST: Index-space-wave State Superposition Transformers for Multispectral Remotely Sensed Imagery Semantic Segmentation
Currently the semantic segmentation task of multispectral remotely sensed imagery (MSRSI) faces the following problems: 1) Usually, only single domain feature (i. e., space domain or frequency domain) is considered; 2) downsampling operation in encoder generally leads to the accuracy loss of edge extraction; 3) multichannel features of MSRSI are not fully considered; and 4) prior knowledge of remote sensing is not fully utilized.
UDHF2-Net: An Uncertainty-diffusion-model-based High-Frequency TransFormer Network for High-accuracy Interpretation of Remotely Sensed Imagery
MUDM could further optimize the uncertain region to improve edge extraction result by gradually removing the multiple geo-knowledge-based noises; (3) a semi-pseudo-Siamese UDHF2-Net for change detection task is proposed to reduce the pseudo change by registration error.
QA-MDT: Quality-aware Masked Diffusion Transformer for Enhanced Music Generation
In recent years, diffusion-based text-to-music (TTM) generation has gained prominence, offering an innovative approach to synthesizing musical content from textual descriptions.
Ranked #3 on
Text-to-Music Generation
on MusicCaps
Dual-modal Prior Semantic Guided Infrared and Visible Image Fusion for Intelligent Transportation System
Therefore, we propose a novel prior semantic guided image fusion method based on the dual-modality strategy, improving the performance of IVF in ITS.
FM-OV3D: Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detection
In this work, we propose FM-OV3D, a method of Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detection, which improves the open-vocabulary localization and recognition abilities of 3D model by blending knowledge from multiple pre-trained foundation models, achieving true open-vocabulary without facing constraints from original 3D datasets.
Graph Representation Learning for Infrared and Visible Image Fusion
Then, GCNs are performed on the concatenate intra-modal NLss features of infrared and visible images, which can explore the cross-domain NLss of inter-modal to reconstruct the fused image.
Graph Representation Learning
Infrared And Visible Image Fusion
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model
With the increasing importance of machine learning, the privacy and security of training data have become critical.
An Improved Artificial Fish Swarm Algorithm for Solving the Problem of Investigation Path Planning
Our objective is to minimize costs, and to achieve this, we propose a chaotic artificial fish swarm algorithm based on multiple population differential evolution (DE-CAFSA).
DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition
Graph Convolutional Networks (GCNs) have been widely used in skeleton-based human action recognition.
An Intelligent Remote Sensing Image Quality Inspection System
Due to the inevitable presence of quality problems, quality inspection of remote sensing images is indeed an indispensable step between the acquisition and the application of them.
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal human effort.
New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators
Higher-Order Influence Functions (HOIFs) provide a unified theory for constructing rate-optimal estimators for a large class of low-dimensional (smooth) statistical functionals/parameters (and sometimes even infinite-dimensional functions) that arise in substantive fields including epidemiology, economics, and the social sciences.
3DDesigner: Towards Photorealistic 3D Object Generation and Editing with Text-guided Diffusion Models
Text-guided diffusion models have shown superior performance in image/video generation and editing.
SOM-Net: Unrolling the Subspace-based Optimization for Solving Full-wave Inverse Scattering Problems
The final output of the SOM-Net is the full predicted induced current, from which the scattered field and the permittivity image can also be deduced analytically.
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning
To achieve a simple and effective model with supervised learning, we propose an efficient CNN and contrastive learning (ECNN-C) method for EEG-based emotion recognition.
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation.
Coverage Optimization of Camera Network for Continuous Deformable Object
To reduce the computational complexity, some feature points are carefully selected representing the continuous deformation process, and the visual coverage for the deformable object is transferred to cover the specific feature points.
Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity.
Optimizing Ranking Systems Online as Bandits
Optimizing ranking systems online means that the deployed system can serve user requests, e. g., queries in the web search, and optimize the ranking policy by learning from user interactions, e. g., clicks.
Elastic Tactile Simulation Towards Tactile-Visual Perception
By contrast, EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact.
Federated Unbiased Learning to Rank
Unbiased Learning to Rank (ULTR) studies the problem of learning a ranking function based on biased user interactions.
Experimental demonstration of memory-enhanced scaling for entanglement connection of quantum repeater segments
The experimental realization of entanglement connection of two quantum repeater segments with an efficient memory-enhanced scaling demonstrates a key advantage of the quantum repeater protocol, which makes a cornerstone towards future large-scale quantum networks.
Quantum Physics
Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences
We employ depth motion images (DMI) as the templates to generate the multi-scale static representation of actions.
The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning
Derived from a novel discovery that the SMDP option framework has an MDP equivalence, SA hierarchically extracts skills (abstract actions) from primary actions and explicitly encodes these knowledge into skill context vectors (embedding vectors).
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions
A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty.
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity
Relevance ranking aims at building a ranked list sorted in decreasing order of item relevance, while result diversification focuses on generating a ranked list of items that covers a broad range of topics.
Quantum Communication between Multiplexed Atomic Quantum Memories
The use of multiplexed atomic quantum memories (MAQM) can significantly enhance the efficiency to establish entanglement in a quantum network.
Quantum Physics
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media
Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task.
Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model
We consider the problem of identifying the K most attractive items and propose cascading non-stationary bandits, an online learning variant of the cascading model, where a user browses a ranked list from top to bottom and clicks on the first attractive item.
A Synaptic Neural Network and Synapse Learning
A Synaptic Neural Network (SynaNN) consists of synapses and neurons.
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Our main finding is that for large-scale Condorcet ranker evaluation problems, MergeDTS outperforms the state-of-the-art dueling bandit algorithms.
Structured Representation Learning for Online Debate Stance Prediction
Online debates can help provide valuable information about various perspectives on a wide range of issues.
Incremental Sparse Bayesian Ordinal Regression
Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning.
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists.
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
Our proposed system consists of two subsystems and one regression model for predicting STS scores.
Probabilistic Feature Selection and Classification Vector Machine
The proposed method, called probabilistic feature selection and classification vector machine (PFCVMLP ), is able to simultaneously select relevant features and samples for classification tasks.
