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Found 21 papers, 3 papers with code
SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses.
Deep Equilibrium Diffusion Restoration with Parallel Sampling
Due to the inherent property of diffusion models, most existing methods need long serial sampling chains to restore HQ images step-by-step, resulting in expensive sampling time and high computation costs.
The bionic neural network for external simulation of human locomotor system
The results demonstrate that the proposed deep learning method can effectively identify subject-specific MSK physiological parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion and muscle forces predictions.
The online learning architecture with edge computing for high-level control for assisting patients
To address these challenges, this paper introduces a novel online adversarial learning architecture integrated with edge computing for high-level lower-limb exoskeleton control.
FocalDreamer: Text-driven 3D Editing via Focal-fusion Assembly
While text-3D editing has made significant strides in leveraging score distillation sampling, emerging approaches still fall short in delivering separable, precise and consistent outcomes that are vital to content creation.
A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics
This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data.
A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data
Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress.
SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces
To enhance the visual accuracy of generated lip movement while reducing the dependence on labeled data, we propose a novel framework SelfTalk, by involving self-supervision in a cross-modals network system to learn 3D talking faces.
USR: Unsupervised Separated 3D Garment and Human Reconstruction via Geometry and Semantic Consistency
To address these issues, we propose an unsupervised separated 3D garments and human reconstruction model (USR), which reconstructs the human body and authentic textured clothes in layers without 3D models.
GARF:Geometry-Aware Generalized Neural Radiance Field
To address these issues, we propose Geometry-Aware Generalized Neural Radiance Field (GARF) with a geometry-aware dynamic sampling (GADS) strategy to perform real-time novel view rendering and unsupervised depth estimation on unseen scenes without per-scene optimization.
A Supervisory Volt/VAR Control Scheme for Coordinating Voltage Regulators with Smart Inverters on a Distribution System
This paper focuses on the effective use of smart inverters for Volt/Var control (VVC) on a distribution system.
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution
To alleviate the problem of mode collapse, this work has proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can map the generated spectral-spatial features from the image space to the latent space and produce a coupling component to regularise the generated samples.
Generative Adversarial Network
Hyperspectral Image Super-Resolution
+1
A Novel CropdocNet for Automated Potato Late Blight Disease Detection from the Unmanned Aerial Vehicle-based Hyperspectral Imagery
Late blight disease is one of the most destructive diseases in potato crop, leading to serious yield losses globally.
Geometric Granularity Aware Pixel-To-Mesh
Pixel-to-mesh has wide applications, especially in virtual or augmented reality, animation and game industry.
Fast Object Detection with Latticed Multi-Scale Feature Fusion
In this work, we present a novel module, the Fluff block, to alleviate drawbacks of current multi-scale fusion methods and facilitate multi-scale object detection.
Nematic Fluctuations in an Orbital Selective Superconductor Fe$_{1+y}$Te$_{1-x}$Se$_{x}$
Near $x = 0$, in proximity to the double-stripe magnetic order of Fe$_{1+y}$Te, a diverging $B_{1g}$ nematic susceptibility is observed.
Superconductivity Strongly Correlated Electrons
A Biologically Interpretable Two-stage Deep Neural Network (BIT-DNN) For Vegetation Recognition From Hyperspectral Imagery
Spectral-spatial based deep learning models have recently proven to be effective in hyperspectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring.
On Sampling Strategies for Neural Network-based Collaborative Filtering
In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework.
Joint Text Embedding for Personalized Content-based Recommendation
While latent factors of items can be learned effectively from user interaction data, in many cases, such data is not available, especially for newly emerged items.
Learning Subclass Representations for Visually-varied Image Classification
The novelty of the approach is that subclass representations make use of not only the content of the photos themselves, but also information on the co-occurrence of their tags, which determines membership in both subclasses and top-level classes.
Exploiting Social Tags for Cross-Domain Collaborative Filtering
A key question to be answered in the context of CDCF is what common characteristics can be deployed to link different domains for effective knowledge transfer.
