Search Results for author:
Found 115 papers, 42 papers with code
Global and Local Attention-Based Transformer for Hyperspectral Image Change Detection
The global attention component employs global attention on downsampled feature maps to capture low-frequency information, while the local attention component focuses on high-frequency details using non-overlapping window-based local attention.
Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedback
In this paper, we propose a novel framework, Neural Internal Model Control, which integrates model-based control with RL-based control to enhance robustness.
ARM: Appearance Reconstruction Model for Relightable 3D Generation
Recent image-to-3D reconstruction models have greatly advanced geometry generation, but they still struggle to faithfully generate realistic appearance.
A novel and efficient parameter estimation of the Lognormal-Rician turbulence model based on k-Nearest Neighbor and data generation method
The Kolmogorov-Smirnov (KS) goodness-of-fit statistical tools are employed to investigate the validity of $k$NN approximation under different channel conditions and it is shown that the choice of $k$ plays a significant role in the approximation accuracy.
MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification
Moreover, to alleviate the heterogeneous gap between HSI and LiDAR/SAR data, we design Fus-Mamba block for multi-source feature fusion.
Hierarchical Attention and Parallel Filter Fusion Network for Multi-Source Data Classification
To solve the problem, we propose a hierarchical attention and parallel filter fusion network for multi-source data classification.
Hardware Architecture Design of Model-Based Image Reconstruction Towards Palm-size Photoacoustic Tomography
We conducted experiments by FPGA implementation of the algorithm, using both phantoms and in vivo human finger data to verify the feasibility of the proposed method.
Wavelet-based Bi-dimensional Aggregation Network for SAR Image Change Detection
Synthetic aperture radar (SAR) image change detection is critical in remote sensing image analysis.
Aligning Human Motion Generation with Human Perceptions
Human motion generation is a critical task with a wide range of applications.
Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting
However, most existing methods pay more attention to learning domain-adaptive inductive bias (meta-knowledge) through feature-wise manipulation or task diversity improvement while neglecting the phenomenon that deep networks tend to rely more on high-frequency cues to make the classification decision, which thus degenerates the robustness of learned inductive bias since high-frequency information is vulnerable and easy to be disturbed by noisy information.
Boosting Spatial-Spectral Masked Auto-Encoder Through Mining Redundant Spectra for HSI-SAR/LiDAR Classification
Although recent masked image modeling (MIM)-based HSI-LiDAR/SAR classification methods have gradually recognized the importance of the spectral information, they have not adequately addressed the redundancy among different spectra, resulting in information leakage during the pretraining stage.
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism
Arctic Sea Ice Concentration (SIC) is the ratio of ice-covered area to the total sea area of the Arctic Ocean, which is a key indicator for maritime activities.
Sparse Focus Network for Multi-Source Remote Sensing Data Classification
Multi-source remote sensing data classification has emerged as a prominent research topic with the advancement of various sensors.
LSKSANet: A Novel Architecture for Remote Sensing Image Semantic Segmentation Leveraging Large Selective Kernel and Sparse Attention Mechanism
In this paper, we proposed large selective kernel and sparse attention network (LSKSANet) for remote sensing image semantic segmentation.
Dual-Stream Attention Network for Hyperspectral Image Unmixing
Therefore, we adopt a "many to one" strategy to estimate the abundance of the central pixel.
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
We validate our approach for various linear inverse problems, such as super-resolution, deblurring, inpainting, and compressed sensing, and demonstrate that we can outperform other methods based on flow matching.
SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation
In conventional remote sensing change detection (RS CD) procedures, extensive manual labeling for bi-temporal images is first required to maintain the performance of subsequent fully supervised training.
Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication
Existing diffusion-based text-to-3D generation methods primarily focus on producing visually realistic shapes and appearances, often neglecting the physical constraints necessary for downstream tasks.
GarmentDreamer: 3DGS Guided Garment Synthesis with Diverse Geometry and Texture Details
In this paper, we propose GarmentDreamer, a novel method that leverages 3D Gaussian Splatting (GS) as guidance to generate wearable, simulation-ready 3D garment meshes from text prompts.
Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism
Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels, enhancing the performance of each individual task.
Multi-Objective Trajectory Planning with Dual-Encoder
In this paper, we propose a two-stage approach to accelerate time-jerk optimal trajectory planning.
Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation
We hypothesize that the underlying phenomenological coverage has not been proportionally scaled up, leading to a skew of the presented phenomenon which harms generalization.
PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling
To facilitate the development of these fields, in this paper, we present PKU-DyMVHumans, a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos.
Annotation-Efficient Polyp Segmentation via Active Learning
To minimize annotation costs, we propose a deep active learning framework for annotation-efficient polyp segmentation.
Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising
To enhance the modeling of both global and local features, we have devised a convolution and attention fusion module aimed at capturing long-range dependencies and neighborhood spectral correlations.
In Defense and Revival of Bayesian Filtering for Thermal Infrared Object Tracking
Deep learning-based methods monopolize the latest research in the field of thermal infrared (TIR) object tracking.
Vision-Language Navigation with Embodied Intelligence: A Survey
As a long-term vision in the field of artificial intelligence, the core goal of embodied intelligence is to improve the perception, understanding, and interaction capabilities of agents and the environment.
VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality
As consumer Virtual Reality (VR) and Mixed Reality (MR) technologies gain momentum, there's a growing focus on the development of engagements with 3D virtual content.
Moirai: Towards Optimal Placement for Distributed Inference on Heterogeneous Devices
This paper presents Moirai that better exploits runtime inter-operator fusion in a model to render a coarsened computation graph, reducing the search space while maintaining the inter-operator optimization provided by inference backends.
Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty
In this paper, we aim to tackle this problem with a unified framework consisting of an end-to-end trainable method and a planning algorithm.
Particle density and critical point for studying site percolation by finite size scaling
It is generally believed that the latent variables of unsupervised learning can capture the information related to phase transitions, which is usually achieved through the so-called order parameter.
SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification
Existing MIM-based methods mostly focus on spatial feature modeling, neglecting spectral feature modeling.
Ranking-based Adaptive Query Generation for DETRs in Crowded Pedestrian Detection
Moreover, to train the rank prediction head better, we propose Soft Gradient L1 Loss.
OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control
In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim.
Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection
In this letter, we explore Transformer-like architecture for SAR change detection to incorporate global attention.
TSI-Net: A Timing Sequence Image Segmentation Network for Intracranial Artery Segmentation in Digital Subtraction Angiography
Therefore, 2D DSA segmentation methods are unable to capture the complete IA information and treatment of cerebrovascular diseases.
Efficient option pricing with unary-based photonic computing chip and generative adversarial learning
In the modern financial industry system, the structure of products has become more and more complex, and the bottleneck constraint of classical computing power has already restricted the development of the financial industry.
Enhancing Cell Proliferation and Migration by MIR-Carbonyl Vibrational Coupling: Insights from Transcriptome Profiling
Cell proliferation and migration highly relate to normal tissue self-healing, therefore it is highly significant for artificial controlling.
MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
Additionally, to capture global contexts, we propose the linear complexity attention-based global correlations capturing by leveraging the decomposition of the softmax operation.
Ranked #1 on
Image Compression
on kodak
Gradient-based adaptive wavelet de-noising method for photoacoustic imaging in vivo
The proposed de-noising method provides potential to improve the SNR of PA signal under single-shot low-power laser illumination for biomedical applications in vivo.
Human Motion Generation: A Survey
In this survey, we present a comprehensive literature review of human motion generation, which, to the best of our knowledge, is the first of its kind in this field.
Machine-Learning-based Colorectal Tissue Classification via Acoustic Resolution Photoacoustic Microscopy
Colorectal cancer is a deadly disease that has become increasingly prevalent in recent years.
Regulating the Hydrophobic Domain in Peptide-Catecholamine Coassembled Nanostructures for Fluorescence Enhancement
Hydrophobic domains provide specific microenvironment for essential functional activities in life.
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences.
Masked Path Modeling for Vision-and-Language Navigation
In this paper, we introduce a masked path modeling (MPM) objective, which pretrains an agent using self-collected data for downstream navigation tasks.
Selecting Learnable Training Samples is All DETRs Need in Crowded Pedestrian Detection
However, in crowded pedestrian detection, the performance of DETRs is still unsatisfactory due to the inappropriate sample selection method which results in more false positives.
SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images
To this end, we put forward a spatial attention weighted unmixing network, dubbed as SAWU-Net, which learns a spatial attention network and a weighted unmixing network in an end-to-end manner for better spatial feature exploitation.
Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction
To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models.
Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising
Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module.
LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression
To tackle this issue, we propose Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression (LLIC).
Structure Embedded Nucleus Classification for Histopathology Images
Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations.
Differentiable Arbitrating in Zero-sum Markov Games
We initiate the study of how to perturb the reward in a zero-sum Markov game with two players to induce a desirable Nash equilibrium, namely arbitrating.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
The OSrisk for the prediction of 5-year survival status achieved AUC of 0. 784 (0. 746-0. 819) in the TCGA cohort, which was further verified in the independent General cohort and the CPTAC cohort, with AUC of 0. 774 (0. 723-0. 820) and 0. 702 (0. 632-0. 765), respectively.
Lesion-aware Dynamic Kernel for Polyp Segmentation
Automatic and accurate polyp segmentation plays an essential role in early colorectal cancer diagnosis.
Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification
It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in existing contrastive learning framework.
GIVL: Improving Geographical Inclusivity of Vision-Language Models with Pre-Training Methods
A key goal for the advancement of AI is to develop technologies that serve the needs not just of one group but of all communities regardless of their geographical region.
CL-MVSNet: Unsupervised Multi-View Stereo with Dual-Level Contrastive Learning
On the one hand, we present an image-level contrastive branch to guide the model to acquire more context awareness, thus leading to more complete depth estimation in indistinguishable regions.
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension
Drug-drug interaction prediction is a crucial issue in molecular biology.
TPA-Net: Generate A Dataset for Text to Physics-based Animation
Recent breakthroughs in Vision-Language (V&L) joint research have achieved remarkable results in various text-driven tasks.
Adaptive De-noising of Photoacoustic Signal and Image based on Modified Kalman Filter
As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications recently, especially in revealing the functional and molecular information to improve diagnostic accuracy.
MLIC: Multi-Reference Entropy Model for Learned Image Compression
Based on MEM and MEM$^+$, we propose image compression models MLIC and MLIC$^+$.
Ranked #1 on
Image Compression
on kodak
Towards Reasoning-Aware Explainable VQA
Our method generates human-readable textual explanations while maintaining SOTA VQA accuracy on the GQA-REX (77. 49%) and VQA-E (71. 48%) datasets.
Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator
Besides, we introduce a sentence-level and a session-level score to measure the sentence fluency and session coherence in the interactive evaluation.
Learning from Students: Online Contrastive Distillation Network for General Continual Learning
The goal of General Continual Learning (GCL) is to preserve learned knowledge and learn new knowledge with constant memory from an infinite data stream where task boundaries are blurry.
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Segmenting medical images is critical to facilitating both patient diagnoses and quantitative research.
Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network
To this end, we proposed a layer attention-based noise-tolerant network, termed LANTNet.
Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation
The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity.
Ranked #2 on
Retinal Vessel Segmentation
on DRIVE
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network
We also propose the distinctive patch convolution for feature representation learning to reduce the time consumption.
SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for Hyperspectral Images
Important findings on the use of spatial and spectral information in the autoencoder framework are discussed.
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation
Semi-supervised learning (SSL), which aims at leveraging a few labeled images and a large number of unlabeled images for network training, is beneficial for relieving the burden of data annotation in medical image segmentation.
Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network
Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis.
SAR Image Change Detection Based on Multiscale Capsule Network
On the one hand, the multiscale capsule module is employed to exploit the spatial relationship of features.
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification
Second, an adaptive DropBlock (AdapDrop) is proposed as a regularization method employed in the generator and discriminator to alleviate the mode collapse issue.
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering
Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question.
Meta Convolutional Neural Networks for Single Domain Generalization
Taking meta features as reference, we propose compositional operations to eliminate irrelevant features of local convolutional features by an addressing process and then to reformulate the convolutional feature maps as a composition of related meta features.
Ranked #5 on
Single-Source Domain Generalization
on Digits-five
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering
Most previous works address the problem by first fusing the image and question in the multi-modal space, which is inflexible for further fusion with a vast amount of external knowledge.
Ranked #19 on
Visual Question Answering (VQA)
on OK-VQA
Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction
Physical modeling methods can offer the potential for extrapolation beyond observational conditions, while data-driven methods are flexible in adapting to data and are capable of detecting unexpected patterns.
Synthetic Aperture Radar Image Change Detection via Siamese Adaptive Fusion Network
Moreover, a correlation layer is designed to further explore the correlation between multitemporal images.
Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network
An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph.
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension
Multi-hop machine reading comprehension is a challenging task in natural language processing as it requires more reasoning ability across multiple documents.
SAR Image Change Detection Based on Multiscale Capsule Network
On the one hand, the capsule module is employed to exploit the spatial relationship of features.
Learning the Precise Feature for Cluster Assignment
Based on this, we propose a general-purpose deep clustering framework which radically integrates representation learning and clustering into a single pipeline for the first time.
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization
In summary, the EOQ framework is specially designed for reducing the high cost of convolution and BN in network training, demonstrating a broad application prospect of online training in resource-limited devices.
Photoacoustic-monitored laser treatment for tattoo removal: a feasibility study
Skin blemishes and diseases have attracted increasing research interest in recent decades, due to their growing frequency of occurrence and the severity of related diseases.
Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network
In addition, we further propose a multi-region convolution module, which emphasizes the central region of each patch.
Deep Transformers for Fast Small Intestine Grounding in Capsule Endoscope Video
In this paper, we propose a deep model to ground shooting range of small intestine from a capsule endoscope video which has duration of tens of hours.
Hyperspectral and LiDAR data classification based on linear self-attention
An efficient linear self-attention fusion model is proposed in this paper for the task of hyperspectral image (HSI) and LiDAR data joint classification.
Change Detection from SAR Images Based on Deformable Residual Convolutional Neural Networks
Convolutional neural networks (CNN) have made great progress for synthetic aperture radar (SAR) images change detection.
Disentangled Non-Local Network for Hyperspectral and LiDAR Data Classification
As the ground objects become increasingly complex, the classification results obtained by single source remote sensing data can hardly meet the application requirements.
Hyperspectral Image Denoising Based On Multi-Stream Denoising Network
Our network consists of the noise estimation subnetwork and denoising subnetwork.
Remote Sensing Image Translation via Style-Based Recalibration Module and Improved Style Discriminator
Then, a new style discriminator is designed to improve the translation performance.
Exploring epithelial-cell calcium signaling with geometric and topological data analysis
However the function of calcium signaling in epithelial cells is not well understood.
Experimentally Validated Hopping-Transport Model for Energetically Disordered Organic Semiconductors
Charge transport in disordered organic semiconductors occurs by hopping of charge carriers between localized sites that are randomly distributed in a strongly energy dependent density of states.
Materials Science
The dynamic energy balance in earthquakes expressed by fault surface morphology
The fault surface morphology is the direct result of the microscopic processes near the crack tip or on the frictional interface.
Geophysics
Superposed Wave (s-Wave): Accelerating Photoacoustic Simulation
However, it is still in a developing stage, and a lot of experiments have to be performed in a simulation setting.
Medical Physics
Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes
Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of vision-based robots.
Generalized Inverse Planning: Learning Lifted non-Markovian Utility for Generalizable Task Representation
Seeing that the proposed generalization problem has not been widely studied yet, we carefully define an evaluation protocol, with which we illustrate the effectiveness of MEIP on two proof-of-concept domains and one challenging task: learning to fold from demonstrations.
Conceptualized Representation Learning for Chinese Biomedical Text Mining
In this paper, we investigate how the recently introduced pre-trained language model BERT can be adapted for Chinese biomedical corpora and propose a novel conceptualized representation learning approach.
Deep learning for photoacoustic imaging: a survey
In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging.
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning.
When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning
In this paper, we propose an actor-critic method - Attention-based Twin Delayed Deep Deterministic policy gradient (ATD3) algorithm to approximate a driver' s action according to observations and measure the driver' s attention allocation for consecutive time steps in car-following model.
Short Text Classification via Knowledge powered Attention with Similarity Matrix based CNN
Short text is becoming more and more popular on the web, such as Chat Message, SMS and Product Reviews.
A Generative Adversarial Network for AI-Aided Chair Design
We present a method for improving human design of chairs.
Deep Learning Enabled Real-Time Photoacoustic Tomography System via Single Data Acquisition Channel
In this work, we develop a novel PACT system to provide real-time imaging, which is achieved by a 120-elements ultrasound array only using a single data acquisition (DAQ) channel.
Learning Perceptual Inference by Contrasting
"Thinking in pictures," [1] i. e., spatial-temporal reasoning, effortless and instantaneous for humans, is believed to be a significant ability to perform logical induction and a crucial factor in the intellectual history of technology development.
Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms
As a result, the proposed construction unifies and extends known theoretical results for many of the existing graph scattering architectures.
Spatiotemporal Attention Networks for Wind Power Forecasting
This model captures spatial correlations among wind farms and temporal dependencies of wind power time series.
Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds
The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of convolutional neural networks.
Graph Classification with Geometric Scattering
Furthermore, ConvNets inspired recent advances in geometric deep learning, which aim to generalize these networks to graph data by applying notions from graph signal processing to learn deep graph filter cascades.
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing
In this work, we propose a new dataset, built in the context of Raven's Progressive Matrices (RPM) and aimed at lifting machine intelligence by associating vision with structural, relational, and analogical reasoning in a hierarchical representation.
Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding
In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end.
Residual Attention based Network for Hand Bone Age Assessment
The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.
Geometric Scattering for Graph Data Analysis
We explore the generalization of scattering transforms from traditional (e. g., image or audio) signals to graph data, analogous to the generalization of ConvNets in geometric deep learning, and the utility of extracted graph features in graph data analysis.
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition.
Improving Object Detection with Region Similarity Learning
Object detection aims to identify instances of semantic objects of a certain class in images or videos.
