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Found 34 papers, 10 papers with code
PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification
The fusion of Synthetic Aperture Radar (SAR) and RGB imagery for land cover classification remains challenging due to modality heterogeneity and the underutilization of spectral complementarity.
R$^2$: A LLM Based Novel-to-Screenplay Generation Framework with Causal Plot Graphs
First, the LLM hallucinations may cause inconsistent plot extraction and screenplay generation.
Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight
This study marks the first application of deep learning techniques to poultry body dimension and weight estimation, providing a valuable reference for the intelligent and precise management of the livestock industry with far-reaching practical significance.
Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows
We curate a dataset of apo and multiple holo states of protein-ligand complexes, simulated by molecular dynamics, and propose a full-atom flow model (and a stochastic version), named DynamicFlow, that learns to transform apo pockets and noisy ligands into holo pockets and corresponding 3D ligand molecules.
CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical Image Segmentation
Semi-supervised medical image segmentation aims to leverage minimal expert annotations, yet remains confronted by challenges in maintaining high-quality consistency learning.
Bi-temporal Gaussian Feature Dependency Guided Change Detection in Remote Sensing Images
However, existing CD methods still struggle to address pseudo changes resulting from domain information differences in multi-temporal images and instances of detail errors caused by the loss and contamination of detail features during the upsampling process in the network.
CRoP: Context-wise Robust Static Human-Sensing Personalization
However, distinct patterns in human sensing, influenced by various factors or contexts, challenge the generic neural network model's performance due to natural distribution shifts.
VAE-Var: Variational-Autoencoder-Enhanced Variational Assimilation
Data assimilation refers to a set of algorithms designed to compute the optimal estimate of a system's state by refining the prior prediction (known as background states) using observed data.
UDA4Inst: Unsupervised Domain Adaptation for Instance Segmentation
Our framework enhances instance segmentation through \textit{Semantic Category Training} and \textit{Bidirectional Mixing Training}.
Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution
To alleviate these issues, we develop the first attempt to integrate the Vision State Space Model (Mamba) for RSI-SR, which specializes in processing large-scale RSI by capturing long-range dependency with linear complexity.
Guiding Attention in End-to-End Driving Models
Vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving.
Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule
In this paper, we present the first systematic survey on multimodal frameworks for molecules research.
Weaver: Foundation Models for Creative Writing
Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models.
Deep Blind Super-Resolution for Satellite Video
Therefore, this paper proposes a practical Blind SVSR algorithm (BSVSR) to explore more sharp cues by considering the pixel-wise blur levels in a coarse-to-fine manner.
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.
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
Recently, convolutional networks have achieved remarkable development in remote sensing image Super-Resoltuion (SR) by minimizing the regression objectives, e. g., MSE loss.
"Reading Between the Heat": Co-Teaching Body Thermal Signatures for Non-intrusive Stress Detection
Stress impacts our physical and mental health as well as our social life.
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Retraining a large neural network model with limited data is vulnerable to overfitting.
Classifying Rhoticity of /r/ in Speech Sound Disorder using Age-and-Sex Normalized Formants
Mispronunciation detection tools could increase treatment access for speech sound disorders impacting, e. g., /r/.
Inferring High-level Geographical Concepts via Knowledge Graph and Multi-scale Data Integration: A Case Study of C-shaped Building Pattern Recognition
To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns.
Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
To explore the global dependency in the entire frame sequence, a Long-term Temporal Difference Module (L-TDM) is proposed, where the differences between forward and backward segments are incorporated and activated to guide the modulation of the temporal feature, leading to a holistic global compensation.
Scaling Vision-based End-to-End Driving with Multi-View Attention Learning
We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning.
Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning
Smart speaker voice assistants (VAs) such as Amazon Echo and Google Home have been widely adopted due to their seamless integration with smart home devices and the Internet of Things (IoT) technologies.
VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch
This study introduces VeriCompress, a tool that automates the search and training of compressed models with robustness guarantees.
Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI Approach
We collected physiological parameters data from 70 children in the two target conditions.
Rib Suppression in Digital Chest Tomosynthesis
Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D images of a human chest for pulmonary disease screening, with 2D X-ray projections taken within an extremely limited range of angles.
Action-Based Representation Learning for Autonomous Driving
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems.
On Learning Nominal Automata with Binders
This class of automata captures nominal regular languages; analogously to the classical language theory, nominal automata have been shown to characterise nominal regular expressions with binders.
Multimodal End-to-End Autonomous Driving
On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals.
Coherent Semantic Attention for Image Inpainting
The latest deep learning-based approaches have shown promising results for the challenging task of inpainting missing regions of an image.
Ranked #1 on
Image Inpainting
on Paris StreetView
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data.
Interactive Deep Colorization With Simultaneous Global and Local Inputs
To solve this problem, we present a novel deep colorization method, which allows simultaneous global and local inputs to better control the output colorized images.
Joint convolutional neural pyramid for depth map super-resolution
The convolutional neural pyramids extract information from large receptive fields of the depth map and guidance map, while the convolutional neural network effectively transfers useful structures of the guidance image to the depth image.
