Search Results for author:
Found 253 papers, 90 papers with code
Bayesian active learning for choice models with deep Gaussian processes
In this paper, we propose an active learning algorithm and models which can gradually learn individual's preference through pairwise comparisons.
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
Experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches.
Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules
Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis.
Robustness of classification ability of spiking neural networks
The numerical results show that there is not significant reduction in the classification ability of the network if the input signals are subject to sinusoidal and Gaussian perturbations.
Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
In robotic surgery, task automation and learning from demonstration combined with human supervision is an emerging trend for many new surgical robot platforms.
Interacting Attention-gated Recurrent Networks for Recommendation
In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions.
Indefinite Kernel Logistic Regression with Concave-inexact-convex Procedure
Since the concave-convex procedure has to solve a sub-problem in each iteration, we propose a concave-inexact-convex procedure (CCICP) algorithm with an inexact solving scheme to accelerate the solving process.
Robust Guided Image Filtering
In this paper, we propose a general framework for Robust Guided Image Filtering (RGIF), which contains a data term and a smoothness term, to solve the two issues mentioned above.
Semi-supervised Learning for Discrete Choice Models
Two classic semi-supervised learning algorithms, the expectation maximization algorithm and the cluster-and-label algorithm, have been adapted to our choice modeling problem setting.
Scalable Nearest Neighbor Search based on kNN Graph
In this paper, a scalable solution based on hill-climbing strategy with the support of k-nearest neighbor graph (kNN) is presented.
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content.
Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study
Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD).
4D Cardiac Ultrasound Standard Plane Location by Spatial-Temporal Correlation
The proposed method is comprised of three stages, the frame smoothing, spatial-temporal embedding and final classification.
A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification
In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data.
Notes on Low-rank Matrix Factorization
The key idea of MF is that there exists latent structures in the data, by uncovering which we could obtain a compressed representation of the data.
Mapping Temporal Variables into the NeuCube for Improved Pattern Recognition, Predictive Modelling and Understanding of Stream Data
This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture.
Robust Visual Tracking via Inverse Nonnegative Matrix Factorization
It utilizes both the foreground and background information, and imposes a local coordinate constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors.
Data Driven Robust Image Guided Depth Map Restoration
Depth maps captured by modern depth cameras such as Kinect and Time-of-Flight (ToF) are usually contaminated by missing data, noises and suffer from being of low resolution.
Visual Tracking via Nonnegative Regularization Multiple Locality Coding
Weights of these various dictionaries are also learned from approximated LLC in the similar framework.
Robust High Quality Image Guided Depth Upsampling
A popular solution is upsampling the obtained noisy low resolution depth map with the guidance of the companion high resolution color image.
Cross-Modality Hashing with Partial Correspondence
Learning a hashing function for cross-media search is very desirable due to its low storage cost and fast query speed.
Learning Data-adaptive Nonparametric Kernels
Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility.
Adversarial Attack Type I: Cheat Classifiers by Significant Changes
Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations.
Generalization Properties of hyper-RKHS and its Applications
This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the introduced regression models in an approximation theory view.
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition
The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations.
Discriminative analysis of the human cortex using spherical CNNs - a study on Alzheimer's disease diagnosis
In neuroimaging studies, the human cortex is commonly modeled as a sphere to preserve the topological structure of the cortical surface.
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring
Neural network models have recently been applied to the task of automatic essay scoring, giving promising results.
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribution of annotated datasets can be helpful for improving the supervised learning tasks, especially when the datasets are limited by size and class balance.
Dynamic Curriculum Learning for Imbalanced Data Classification
Human attribute analysis is a challenging task in the field of computer vision, since the data is largely imbalance-distributed.
Fast Patch-Based Denoising Using Approximated Patch Geodesic Paths
PatchGPs treat image patches as nodes and patch differences as edge weights for computing the shortest (geodesic) paths.
Diversity-Enhanced Condensation Algorithm and Its Application for Robust and Accurate Endoscope Three-Dimensional Motion Tracking
The paper proposes a diversity-enhanced condensation algorithm to address the particle impoverishment problem which stochastic filtering usually suffers from.
Saliency Propagation From Simple to Difficult
In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner.
Mesh-based Autoencoders for Localized Deformation Component Analysis
Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing.
Graphics
A Regularization Approach for Instance-Based Superset Label Learning
As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms.
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs).
An Effective Two-Branch Model-Based Deep Network for Single Image Deraining
Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing.
A Review of Semi Supervised Learning Theories and Recent Advances
Semi-supervised learning, which has emerged from the beginning of this century, is a new type of learning method between traditional supervised learning and unsupervised learning.
LibN3L:A Lightweight Package for Neural NLP
We present a light-weight machine learning tool for NLP research.
Estimating brain age based on a healthy population with deep learning and structural MRI
Numerous studies have established that estimated brain age, as derived from statistical models trained on healthy populations, constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases.
AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks
Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation.
SDM-NET: Deep Generative Network for Structured Deformable Mesh
At the structural level, we train a Structured Parts VAE (SP-VAE), which jointly learns the part structure of a shape collection and the part geometries, ensuring a coherence between global shape structure and surface details.
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.
LaplacianNet: Learning on 3D Meshes with Laplacian Encoding and Pooling
3D models are commonly used in computer vision and graphics.
Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content
Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection.
Cluster-wise Unsupervised Hashing for Cross-Modal Similarity Search
Large-scale cross-modal hashing similarity retrieval has attracted more and more attention in modern search applications such as search engines and autopilot, showing great superiority in computation and storage.
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation.
Adaptive Initialization Method for K-means Algorithm
(c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets.
A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection
Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training.
CNN-Based Invertible Wavelet Scattering for the Investigation of Diffusion Properties of the In Vivo Human Heart in Diffusion Tensor Imaging
The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing.
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection
Deep learning demands a huge amount of well-labeled data to train the network parameters.
Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection
In addition, the results also demonstrate that the higher-precision bottom layers could boost the 1-bit network performance appreciably due to a better preservation of the original image information while the lower-precision posterior layers contribute to the regularization of $k-$bit networks.
Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval
Due to the superiority in similarity computation and database storage for large-scale multiple modalities data, cross-modal hashing methods have attracted extensive attention in similarity retrieval across the heterogeneous modalities.
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet
AoA enjoys a significant increase in transferability when the traditional cross entropy loss is replaced with the attention loss.
A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images
This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images.
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention
In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules.
Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks
It improves learning from imbalanced data by incorporating the majority distribution structure in the generation of new minority samples.
Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration based Approach
To overcome this limitation, in this paper, we propose a sparse generalized canonical correlation analysis (GCCA), which could detect the latent relations of multiview data with sparse structures.
Clustering via torque balance with mass and distance
Grouping similar objects is a fundamental tool of scientific analysis, ubiquitous in disciplines from biology and chemistry to astronomy and pattern recognition.
Hierarchical Feature Embedding for Attribute Recognition
Attribute recognition is a crucial but challenging task due to viewpoint changes, illumination variations and appearance diversities, etc.
Analysis of Regularized Least Squares in Reproducing Kernel Krein Spaces
In this paper, we study the asymptotic properties of regularized least squares with indefinite kernels in reproducing kernel Krein spaces (RKKS).
FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network
Significant progress has been made for estimating optical flow using deep neural networks.
Ranked #7 on
Optical Flow Estimation
on KITTI 2012
Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning
Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy.
Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis
In particular, we restore the balance in the imbalanced dataset by generating faulty samples from the proposed mixture of data distribution.
Sparse Data Driven Mesh Deformation
This along with a suitably chosen deformation basis including spatially localized deformation modes leads to significant advantages, including more meaningful, reliable, and efficient deformations because fewer and localized deformation modes are applied.
Graphics
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification
Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification.
One-shot Distributed Algorithm for Generalized Eigenvalue Problem
Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy.
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases
To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States.
A New Nonparametric Estimate of the Risk-Neutral Density with Applications to Variance Swaps
We develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem.
Integrated Communication and Localization in mmWave Systems
As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation mobile communication systems have been conducted.
Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders
The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales.
Multi-scale convolutional neural network for multi-focus image fusion
To address the above issues, we proposed a new MFIF method, which aims to learn feature extraction, fusion and reconstruction components together to produce a complete unsupervised end-to-end trainable deep CNN.
WDR FACE: The First Database for Studying Face Detection in Wide Dynamic Range
Furthermore, we show the effect of different face detection procedures on the WDRIs in our database.
A Survey on Extraction of Causal Relations from Natural Language Text
Existing causality extraction techniques include knowledge-based, statistical machine learning(ML)-based, and deep learning-based approaches.
OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow
Optical flow estimation is an essential step for many real-world computer vision tasks.
Mobile-end Tone Mapping based on Integral Image and Integral Histogram
It is especially crucial for mobile devices because most of the images taken today are from mobile phones, hence such technology is highly demanded in the consumer market of mobile devices and is essential for a good customer experience.
Deep Deformation Detail Synthesis for Thin Shell Models
Moreover, as such methods only add details, they require coarse meshes to be close to fine meshes, which can be either impossible, or require unrealistic constraints when generating fine meshes.
A New Neuromorphic Computing Approach for Epileptic Seizure Prediction
Motivated by the energy-efficient spiking neural networks (SNNs), a neuromorphic computing approach for seizure prediction is proposed in this work.
Unsupervised Motion Representation Enhanced Network for Action Recognition
Learning reliable motion representation between consecutive frames, such as optical flow, has proven to have great promotion to video understanding.
A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction
Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries.
Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery
The proposed model adopts single-shot detector in parallel with a lightweight image pyramid module to extract representative features and generate regions of interest in an optimization approach.
supervised adptive threshold network for instance segmentation
Currently, instance segmentation is attracting more and more attention in machine learning region.
Liquid Sensing Using WiFi Signals
Among those services, sensing the liquid level in a container is critical to building many smart home and mobile healthcare applications that improve the quality of life.
Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset
The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22. 96, \chi^2=105. 04) and (OR=7. 06, \chi^2=290. 11).
An End-to-End Deep Learning Approach for Epileptic Seizure Prediction
Conventional seizure prediction works usually rely on features extracted from Electroencephalography (EEG) recordings and classification algorithms such as regression or support vector machine (SVM) to locate the short time before seizure onset.
Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images
Second, most of the standard methods used hand-crafted features, and do not work well on the detection of objects parts of which are missing.
Visual Anomaly Detection for Images: A Survey
Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision.
Context-guided Triple Matching for Multiple Choice Question Answering
The task of multiple choice question answering (MCQA) refers to identifying a suitable answer from multiple candidates, by estimating the matching score among the triple of the passage, question and answer.
Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning
Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning.
3D Human Pose Estimation for Free-form Activity Using WiFi Signals
Our system tracks free-form activity by estimating a 3D skeleton pose that consists of a set of joints of the human body.
Demographic Biases of Crowd Workers in Key Opinion Leaders Finding
Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions.
AEFE: Automatic Embedded Feature Engineering for Categorical Features
The challenge of solving data mining problems in e-commerce applications such as recommendation system (RS) and click-through rate (CTR) prediction is how to make inferences by constructing combinatorial features from a large number of categorical features while preserving the interpretability of the method.
Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism
The aim of this study was to investigate whether a deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs.
C$^2$SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction
Although compressive sensing (CS) can be adopted to compress the signals to reduce communication bandwidth requirement, it needs a complex reconstruction procedure before the signal can be used for seizure prediction.
OctField: Hierarchical Implicit Functions for 3D Modeling
We achieve this goal by introducing a hierarchical octree structure to adaptively subdivide the 3D space according to the surface occupancy and the richness of part geometry.
Stable and Effective One-Step Method for Person Search
In order to process divergence between detection and re-identification, we introduce switchable normalization and gradient centralization to improve the stability of the model.
The Science of Rejection: A Research Area for Human Computation
We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
Learn Robust Features via Orthogonal Multi-Path
To defend adversarial attacks, we design a block containing multiple paths to learn robust features and the parameters of these paths are required to be orthogonal with each other.
Using Conversational Artificial Intelligence to Support Children's Search in the Classroom
Further, we discuss preliminary ideas on strategies for evaluation.
On the Value of ML Models
We argue that, when establishing and benchmarking Machine Learning (ML) models, the research community should favour evaluation metrics that better capture the value delivered by their model in practical applications.
BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding
Since the volume of the peripheral bronchi may be much smaller than the large branches in an input patch, the common segmentation loss is not sensitive to the breakages among the distal branches.
Anchor Graph Structure Fusion Hashing for Cross-Modal Similarity Search
Cross-modal hashing still has some challenges needed to address: (1) most existing CMH methods take graphs as input to model data distribution.
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2).
Online Learning of Trellis Diagram Using Neural Network for Robust Detection and Decoding
As an interesting by-product, we propose an enhancement to the BLE standard by introducing a bit interleaver to its physical layer; the resultant improvement of the receiver sensitivity can make it a better fit for some Internet of Things (IoT) communications.
Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation
The proposed training scheme significantly improves the performance of patient-specific seizure predictors and bridges the gap between patient-specific and patient-independent predictors.
3D Human Pose Estimation for Free-from and Moving Activities Using WiFi
This paper presents GoPose, a 3D skeleton-based human pose estimation system that uses WiFi devices at home.
Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures
To identify the preictal region that precedes the onset of seizure, a large number of annotated EEG signals are required to train DL algorithms.
Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images
In IPSSD, single-shot detector is adopted combined with an image pyramid network to extract semantically strong features for generating candidate regions.
An Event-Driven Compressive Neuromorphic System for Cardiac Arrhythmia Detection
Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks.
Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief
Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases.
Binary Single-dimensional Convolutional Neural Network for Seizure Prediction
Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction.
Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency
Integrating multi-modal data to promote medical image analysis has recently gained great attention.
Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective
Accented speech recognition and accent classification are relatively under-explored research areas in speech technology.
Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions
To achieve a better lift return on investment (lift ROI) on the enduring effect of the promotion and improve customer retention and loyalty, we propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response.
ATCA: an Arc Trajectory Based Model with Curvature Attention for Video Frame Interpolation
Video frame interpolation is a classic and challenging low-level computer vision task.
An Online Sparse Streaming Feature Selection Algorithm
In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i. e., how to establish the uncertain relationship between sparse streaming features and labels.
PTab: Using the Pre-trained Language Model for Modeling Tabular Data
The learning of an effective contextual representation requires meaningful features and a large amount of data.
SpikeSEE: An Energy-Efficient Dynamic Scenes Processing Framework for Retinal Prostheses
In this paper, we propose an energy-efficient dynamic scenes processing framework (SpikeSEE) that combines a spike representation encoding technique and a bio-inspired spiking recurrent neural network (SRNN) model to achieve intelligent processing and extreme low-power computation for retinal prostheses.
A Compact Online-Learning Spiking Neuromorphic Biosignal Processor
In this design, a compact online learning neuromorphic hardware architecture with ultra-low power consumption designed explicitly for biosignal processing is proposed.
Rethinking and Recomputing the Value of ML Models
In this paper, we argue that the way we have been training and evaluating ML models has largely forgotten the fact that they are applied in an organization or societal context as they provide value to people.
Robust Person Identification: A WiFi Vision-based Approach
In particular, we leverage multiple antennas on next-generation WiFi devices and 2D AoA estimation of the signal reflections to enable WiFi to visualize a person in the physical environment.
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond
In this survey, we assess the eligibility of more than fifty published peer-reviewed representative transfer learning approaches for EMG applications.
Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment
Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error.
3D Human Mesh Construction Leveraging Wi-Fi
In this paper, we present, Wi-Mesh, a WiFi vision-based 3D human mesh construction system.
AI enhanced finite element multiscale modelling and structural uncertainty analysis of a functionally graded porous beam
The error range of CNN models leads to an uncertain mechanical performance, which is further evaluated in a structural uncertainty analysis on the FG porous three-layer beam consisting of two thin high-density layers and a thick low-density one, where the imprecise CNN predicted moduli are represented as triangular fuzzy numbers in double parametric form.
Progressive Motion Context Refine Network for Efficient Video Frame Interpolation
Recently, flow-based frame interpolation methods have achieved great success by first modeling optical flow between target and input frames, and then building synthesis network for target frame generation.
Sub-1ms Instinctual Interference Adaptive GaN LNA Front-End with Power and Linearity Tuning
The system permits an LNA power consumption to tune from 500 mW to 2 W (4X increase) in order to adjust the linearity from P\textsubscript{1dB, IN}=-10. 5 dBm to 0. 5 dBm (>10X increase).
A 97 fJ/Conversion Neuron-ADC with Reconfigurable Sampling and Static Power Reduction
A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work.
Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction
And, a novel multiscale STFT-based feature extraction method combined with 3D-CNN architecture is proposed to accurately capture predictive probabilities of samples.
SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry
We propose SCENEHGN, a hierarchical graph network for 3D indoor scenes that takes into account the full hierarchy from the room level to the object level, then finally to the object part level.
SoftCLIP: Softer Cross-modal Alignment Makes CLIP Stronger
During the preceding biennium, vision-language pre-training has achieved noteworthy success on several downstream tasks.
Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains
This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i. e. various levels of label granularity) to train a powerful human parsing network.
CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection
Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases.
NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering
Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering.
Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries
We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance.
A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification
Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles.
Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective
Recently, more and more GNN-based methods have been proposed for bot detection.
A 137.5 TOPS/W SRAM Compute-in-Memory Macro with 9-b Memory Cell-Embedded ADCs and Signal Margin Enhancement Techniques for AI Edge Applications
In this paper, we propose a high-precision SRAM-based CIM macro that can perform 4x4-bit MAC operations and yield 9-bit signed output.
MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction
Extracting meaningful drug-related information chunks, such as adverse drug events (ADE), is crucial for preventing morbidity and saving many lives.
Tackling Vision Language Tasks Through Learning Inner Monologues
Visual language tasks require AI models to comprehend and reason with both visual and textual content.
ARTIST: ARTificial Intelligence for Simplified Text
Complex text is a major barrier for many citizens when accessing public information and knowledge.
Neural Network-Based Histologic Remission Prediction In Ulcerative Colitis
A total of 5105 images of 154 intestinal segments from 87 patients undergoing EC treatment at a center in China between March 2022 and March 2023 are scored according to the Geboes score.
Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models
Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of vision-language tasks.
MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement
In the MMA-Net, we first feed spine X-ray images into the segmentation network to produce multiple morphological information (spine region, centerline, and boundary) and then concatenate the original X-ray image with the resulting segmentation maps as input for the regression module to perform precise Cobb angle measurement.
ClusVPR: Efficient Visual Place Recognition with Clustering-based Weighted Transformer
VPR is particularly difficult due to the presence of duplicate regions and the lack of attention to small objects in complex scenes, resulting in recognition deviations.
Low-Dimensional Gradient Helps Out-of-Distribution Detection
To bridge this gap, in this paper, we conduct a comprehensive investigation into leveraging the entirety of gradient information for OOD detection.
READS-V: Real-time Automated Detection of Epileptic Seizures from Surveillance Videos via Skeleton-based Spatiotemporal ViG
An accurate and efficient epileptic seizure onset detection system can significantly benefit patients.
T-Code: Simple Temporal Latent Code for Efficient Dynamic View Synthesis
Novel view synthesis for dynamic scenes is one of the spotlights in computer vision.
Dynamic In-Context Learning from Nearest Neighbors for Bundle Generation
To enhance reliability and mitigate the hallucination issue, we develop (1) a self-correction strategy to foster mutual improvement in both tasks without supervision signals; and (2) an auto-feedback mechanism to recurrently offer dynamic supervision based on the distinct mistakes made by ChatGPT on various neighbor sessions.
Contrastive learning-based agent modeling for deep reinforcement learning
However, existing agent modeling approaches typically assume the availability of local observations from other agents (modeled agents) during training or a long observation trajectory for policy adaption.
Anything in Any Scene: Photorealistic Video Object Insertion
Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism.
Kernel PCA for Out-of-Distribution Detection
Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Mesh-based Gaussian Splatting for Real-time Large-scale Deformation
Based on this representation, we further introduce a large-scale Gaussian deformation technique to enable deformable GS, which alters the parameters of 3D Gaussians according to the manipulation of the associated mesh.
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL).
MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding
With appropriate data selection and training techniques, Large Language Models (LLMs) have demonstrated exceptional success in various medical examinations and multiple-choice questions.
Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds
Clinical reasoning refers to the cognitive process that physicians employ in evaluating and managing patients.
Towards the THz Networks in the 6G Era
This commentary dedicates to envision what role THz is going to play in the coming human-centric 6G era.
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs.
Recent Advances in 3D Gaussian Splatting
The emergence of 3D Gaussian Splatting (3DGS) has greatly accelerated the rendering speed of novel view synthesis.
Intelligent Reflecting Surfaces vs. Full-Duplex Relays: A Comparison in the Air
This letter aims to provide a fundamental analytical comparison for the two major types of relaying methods: intelligent reflecting surfaces and full-duplex relays, particularly focusing on unmanned aerial vehicle communication scenarios.
Exploring LLMs as a Source of Targeted Synthetic Textual Data to Minimize High Confidence Misclassifications
Natural Language Processing (NLP) models optimized for predictive performance often make high confidence errors and suffer from vulnerability to adversarial and out-of-distribution data.
One-shot Distibuted Algorithm for PCA with RBF Kernels
This letter proposes a one-shot algorithm for feature-distributed kernel PCA.
Learn What You Need in Personalized Federated Learning
Personalized federated learning aims to address data heterogeneity across local clients in federated learning.
Dynamic Weighted Adversarial Learning for Semi-Supervised Classification under Intersectional Class Mismatch
Nowadays, class-mismatch problem has drawn intensive attention in Semi-Supervised Learning (SSL), where the classes of labeled data are assumed to be only a subset of the classes of unlabeled data.
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks.
Improving Language Generation with Sentence Coherence Objective
Conditional story generation and contextual text continuation have become increasingly popular topics in NLP community.
Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels
Random Fourier Features (RFF) demonstrate wellappreciated performance in kernel approximation for largescale situations but restrict kernels to be stationary and positive definite.
Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications
Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges.
Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data.
Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective
Motivated by such diversities on OoD loss landscape across modes, we revisit the deep ensemble method for OoD detection through mode ensemble, leading to improved performance and benefiting the OoD detector with reduced variances.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities
Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption.
End-to-end Kernel Learning via Generative Random Fourier Features
In the second-stage process, a linear learner is conducted with respect to the mapped random features.
Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection
The proposed framework is evaluated using three real-world bot detection benchmark datasets, and it consistently exhibits superiority over the baselines.
TIDE: Test Time Few Shot Object Detection
Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object instances of novel categories within a target domain.
SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution
In this paper, we present a high-performance deep neural network for weak target image segmentation, including medical image segmentation and infrared image segmentation.
Towards Robust Neural Networks via Orthogonal Diversity
In this way, the proposed DIO augments the model and enhances the robustness of DNN itself as the learned features can be corrected by these mutually-orthogonal paths.
Eliminating Contextual Prior Bias for Semantic Image Editing via Dual-Cycle Diffusion
The proposed model incorporates a Bias Elimination Cycle that consists of both a forward path and an inverted path, each featuring a Structural Consistency Cycle to ensure the preservation of image content during the editing process.
Exploring Social Media for Early Detection of Depression in COVID-19 Patients
Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression.
Red Teaming for Large Language Models At Scale: Tackling Hallucinations on Mathematics Tasks
We consider the problem of red teaming LLMs on elementary calculations and algebraic tasks to evaluate how various prompting techniques affect the quality of outputs.
FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical Learning
Most existing methods focus on learning noise-robust models from web images while neglecting the performance drop caused by the differences between web domain and real-world domain.
On Multi-head Ensemble of Smoothed Classifiers for Certified Robustness
Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple deep neural networks (DNNs) has shown state-of-the-art performances.
AMIL: Adversarial Multi Instance Learning for Human Pose Estimation
The proposed adversarial residual multi-instance neural network that is based on pooling has been validated on two datasets for the human pose estimation task and successfully outperforms the other state-of-arts models.
GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost
Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their benefits from being shared equally amongst speakers of all the world's languages.
Modeling Image Composition for Complex Scene Generation
Compared to existing CNN-based and Transformer-based generation models that entangled modeling on pixel-level&patch-level and object-level&patch-level respectively, the proposed focal attention predicts the current patch token by only focusing on its highly-related tokens that specified by the spatial layout, thereby achieving disambiguation during training.
MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation
To break the dilemma, we propose a novel mutual distillation framework to transfer reliable knowledge back and forth between the teacher and student networks for alternate improvement.
Ranked #1 on
Optical Flow Estimation
on KITTI 2015 unsupervised
(Fl-all metric)
Semantic Difference Guidance for the Uncertain Boundary Segmentation of CT Left Atrial Appendage
In this paper, we have modeled relative relations between the LA and LAA via deep segmentation networks for the first time, and introduce a new LA & LAA CT dataset.
Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection
For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet.
RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection
This paper proposes a Random Forest boosted Graph Neural Network for social bot detection, called RF-GNN, which employs graph neural networks (GNNs) as the base classifiers to construct a random forest, effectively combining the advantages of ensemble learning and GNNs to improve the accuracy and robustness of the model.
Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation
To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method.
LightFuse: Lightweight CNN based Dual-exposure Fusion
Embedded systems have a huge market, and utilizing DCNNs' powerful functionality into them will further reduce human intervention.
Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image
In this paper, we propose a novel method for highly efficient follicular segmentation of thyroid cytopathological WSIs.
Towards Fine-grained Causal Reasoning and QA
Understanding causality is key to the success of NLP applications, especially in high-stakes domains.
Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency
By aligning the class tokens and spatial attention maps of paired NBI and WL images at different levels, the Transformer achieves the ability to keep both global and local representation consistency for the above two modalities.
Tone Mapping Based on Multi-scale Histogram Synthesis
HVS perceives luminance differently when under different adaptation levels, and therefore our algorithm uses functions built upon different scales to tone map pixels to different values.
Residual Enhanced Multi-Hypergraph Neural Network
Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various research domains.
Dynamic Frame Interpolation in Wavelet Domain
Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience.
