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Found 170 papers, 55 papers with code
OID: Outlier Identifying and Discarding in Blind Image Deblurring
Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process.
Learning Event-Driven Video Deblurring and Interpolation
Event-based sensors, which have a response if the change of pixel intensity exceeds a triggering threshold, can capture high-speed motion with microsecond accuracy.
Text Graph Transformer for Document Classification
Text classification is a fundamental problem in natural language processing.
Beyond Text: Unveiling Multimodal Proficiency of Large Language Models with MultiAPI Benchmark
The proliferation of Large Language Models like ChatGPT has significantly advanced language understanding and generation, impacting a broad spectrum of applications.
3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion
We propose the 3DifFusionDet framework in this paper, which structures 3D object detection as a denoising diffusion process from noisy 3D boxes to target boxes.
FusionViT: Hierarchical 3D Object Detection via LiDAR-Camera Vision Transformer Fusion
Different from the existing 3D object detection approaches, FusionViT is a pure-ViT based framework, which adopts a hierarchical architecture by extending the transformer model to embed both images and point clouds for effective representation learning.
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
In recent years, federated minimax optimization has attracted growing interest due to its extensive applications in various machine learning tasks.
Driving behavior-guided battery health monitoring for electric vehicles using machine learning
To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions.
A Health Monitoring System Based on Flexible Triboelectric Sensors for Intelligence Medical Internet of Things and its Applications in Virtual Reality
The Internet of Medical Things (IoMT) is a platform that combines Internet of Things (IoT) technology with medical applications, enabling the realization of precision medicine, intelligent healthcare, and telemedicine in the era of digitalization and intelligence.
Unsupervised Multi-document Summarization with Holistic Inference
SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners.
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation
To overcome the indiscriminate updating issue, we introduce the Adaptive Short-term Updater module that will automatically discard the useless or noisy edges, ensuring iLoRE's effectiveness and instant ability.
DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing
Diffusion models have been leveraged to perform adversarial purification and thus provide both empirical and certified robustness for a standard model.
SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding
Simultaneously, resource consumption of a single-GPU can be diminished by up to 69%, thus enabling the multiple GPU-based training and acceleration encompassing millions of nodes and billions of edges.
Generic Attention-model Explainability by Weighted Relevance Accumulation
In this paper, we propose a weighted relevancy strategy, which takes the importance of token values into consideration, to reduce distortion when equally accumulating relevance.
RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs
In this work, we propose RestoreFormer++, which on the one hand introduces fully-spatial attention mechanisms to model the contextual information and the interplay with the priors, and on the other hand, explores an extending degrading model to help generate more realistic degraded face images to alleviate the synthetic-to-real-world gap.
Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring
In this paper, we develop a data-driven approach to model the saturated pixels by a learned latent map.
Dual Intents Graph Modeling for User-centric Group Discovery
Therefore, user-centric group discovery task, i. e., recommending groups to users can help both users' online experiences and platforms' long-term developments.
Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis
This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size.
Contrastive Hierarchical Discourse Graph for Scientific Document Summarization
The extended structural context has made scientific paper summarization a challenging task.
SummIt: Iterative Text Summarization via ChatGPT
Text summarization systems have made significant progress in recent years, but typically generate summaries in one single step.
DiffuSum: Generation Enhanced Extractive Summarization with Diffusion
This paper proposes DiffuSum, a novel paradigm for extractive summarization, by directly generating the desired summary sentence representations with diffusion models and extracting sentences based on sentence representation matching.
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT
To address such challenges, in this paper, we will investigate the principles, methodologies and algorithms to empower existing LLMs with graph reasoning ability, which will have tremendous impacts on the current research of both LLMs and graph learning.
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPT
Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer (Graph Reasoning oriented Toolformer) framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools.
Extractive Summarization via ChatGPT for Faithful Summary Generation
In addition, we explore the effectiveness of in-context learning and chain-of-thought reasoning for enhancing its performance.
Graph Collaborative Signals Denoising and Augmentation for Recommendation
Moreover, we show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions.
AMC-Net: An Effective Network for Automatic Modulation Classification
Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems.
Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks.
Immune Defense: A Novel Adversarial Defense Mechanism for Preventing the Generation of Adversarial Examples
Therefore, we propose a novel adversarial defense mechanism, which is referred to as immune defense and is the example-based pre-defense.
Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise
For this class of problems, we propose the first variance-reduced accelerated algorithm and establish that it guarantees a high-probability convergence rate of $O(\log(T/\delta)T^{\frac{1-p}{2p-1}})$ under a mild condition, which is faster than $\Omega(T^{\frac{1-p}{3p-2}})$.
HumanMAC: Masked Motion Completion for Human Motion Prediction
In the training stage, we learn a motion diffusion model that generates motions from random noise.
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task.
Deep Dynamic Scene Deblurring from Optical Flow
In this paper, we explore optical flow to remove dynamic scene blur by using the multi-scale spatially variant recurrent neural network (RNN).
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
Offline reinforcement learning (RL) aims to find an optimal policy for sequential decision-making using a pre-collected dataset, without further interaction with the environment.
Mutimodal Ranking Optimization for Heterogeneous Face Re-identification
Secondly, we propose linear and non-linear fusion strategies to aggregate initial ranking lists of multimodal face pairs and acquire the optimized re-ranked list based on modal complementarity.
Robot Kinematics: Motion, Kinematics and Dynamics
As to some more advanced topics about robot control, we will introduce them in the following tutorial articles for readers instead.
Coordinating CAV Swarms at Intersections with a Deep Learning Model
Connected and automated vehicles (CAVs) are viewed as a special kind of robots that have the potential to significantly improve the safety and efficiency of traffic.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Robot Basics: Representation, Rotation and Velocity
In this article, we plan to provide an introduction about some basics about robots for readers.
Self-Supervised Intensity-Event Stereo Matching
Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption.
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work
Third, we consider a constrained optimization formulation where the feasible region is the nice local region, and prove that every KKT point is a nearly global minimizer.
Degeneracy is OK: Logarithmic Regret for Network Revenue Management with Indiscrete Distributions
We develop an online algorithm that achieves $O(\log^2 T)$ regret under this model, with no further assumptions.
HEGEL: Hypergraph Transformer for Long Document Summarization
Extractive summarization for long documents is challenging due to the extended structured input context.
CARE: Certifiably Robust Learning with Reasoning via Variational Inference
In particular, we propose a certifiably robust learning with reasoning pipeline (CARE), which consists of a learning component and a reasoning component.
Learning Diverse Tone Styles for Image Retouching
In particular, the style encoder predicts the target style representation of an input image, which serves as the conditional information in the RetouchNet for retouching, while the TSFlow maps the style representation vector into a Gaussian distribution in the forward pass.
Learning to Order for Inventory Systems with Lost Sales and Uncertain Supplies
We aim to minimize the $T$-period cost, a problem that is known to be computationally intractable even under known distributions of demand and supply.
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation Learning
Brain graph representation learning serves as the fundamental technique for brain diseases diagnosis.
Double Sampling Randomized Smoothing
To overcome this hurdle, we propose a Double Sampling Randomized Smoothing (DSRS) framework, which exploits the sampled probability from an additional smoothing distribution to tighten the robustness certification of the previous smoothed classifier.
What is a Good Metric to Study Generalization of Minimax Learners?
Minimax optimization has served as the backbone of many machine learning (ML) problems.
VPNets: Volume-preserving neural networks for learning source-free dynamics
We propose volume-preserving networks (VPNets) for learning unknown source-free dynamical systems using trajectory data.
Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks
The destruction to DNNs brought by the adversarial attack sparks the potential that adversarial examples serve as a new protection mechanism for privacy security in social networks.
TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos
The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments.
An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images
This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy.
Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective
The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points.
A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements
This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.
RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs
Blind face restoration is to recover a high-quality face image from unknown degradations.
Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network
We demonstrate the effectiveness and efficiency of MeGuide in training various GNNs on multiple datasets.
A Self-supervised Mixed-curvature Graph Neural Network
Instead of working on one single constant-curvature space, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces and design hierarchical attention mechanisms for learning and fusing the representations across these component spaces.
Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback
Leveraging self-concordant barrier functions, we first construct a new bandit learning algorithm and show that it achieves the single-agent optimal regret of $\tilde{\Theta}(n\sqrt{T})$ under smooth and strongly concave reward functions ($n \geq 1$ is the problem dimension).
Collaborative Graph Contrastive Learning: Data Augmentation Composition May Not be Necessary for Graph Representation Learning
Extensive experiments demonstrate the advantages of CGCL in unsupervised graph representation learning and the non-necessity of handcrafted data augmentation composition for graph representation learning.
EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation
The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set, including the original Environmental Microorganism images (EMs) and the corresponding object labeling files in ". XML" format file.
CARD: Certifiably Robust Machine Learning Pipeline via Domain Knowledge Integration
In particular, we express the domain knowledge as first-order logic rules and embed these logic rules in a probabilistic graphical model.
A Multi-Task Learning Algorithm for Non-personalized Recommendations
In this paper, we introduce a multi-task learning (MTL) algorithm for recommending non-personalized videos to watch next on industrial video sharing platforms.
Targeted Cross-Validation
A standard approach is to find the globally best modeling method from a set of candidate methods.
ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection
Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation.
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision
To diminish the effect of color inconsistency in image alignment, we introduce to use a global color mapping (GCM) module to generate an initial sRGB image given the input raw image, which can keep the spatial location of the pixels unchanged, and the target sRGB image is utilized to guide GCM for converting the color towards it.
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging.
Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer
Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it.
Learning a Non-Blind Deblurring Network for Night Blurry Images
Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels.
Blind Deblurring for Saturated Images
To address this problem, we introduce a new blur model to fit both saturated and unsaturated pixels, and all informative pixels can be considered during deblurring process.
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
In this paper, we show that such efficiency highly depends on the scale at which the attack is applied, and attacking at the optimal scale significantly improves the efficiency.
EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis
For PFO diagnosis, contrast transthoracic echocardiography (cTTE) is preferred as being a more robust method compared with others.
Drill the Cork of Information Bottleneck by Inputting the Most Important Data
As a promising tool to better understand the learning dynamic of minibatch SGD, the information bottleneck (IB) theory claims that the optimization process consists of an initial fitting phase and the following compression phase.
A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches
In this review, first, we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods.
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images
As one of the most commonly ordered imaging tests, computed tomography (CT) scan comes with inevitable radiation exposure that increases the cancer risk to patients.
Pyramid U-Net for Retinal Vessel Segmentation
To further improve performance, two optimizations including pyramid inputs enhancement and deep pyramid supervision are applied to PSABs in the encoder and decoder, respectively.
Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs
To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions.
Centrality Meets Centroid: A Graph-based Approach for Unsupervised Document Summarization
Unsupervised document summarization has re-acquired lots of attention in recent years thanks to its simplicity and data independence.
A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches
In this article, we have studied the development of microorganism counting methods using digital image analysis.
Decentralized Non-Convex Learning with Linearly Coupled Constraints
Motivated by the need for decentralized learning, this paper aims at designing a distributed algorithm for solving nonconvex problems with general linear constraints over a multi-agent network.
Optimization and Control Systems and Control Systems and Control
Efficient Deep Image Denoising via Class Specific Convolution
In this paper, we propose an efficient deep neural network for image denoising based on pixel-wise classification.
Topology Learning Aided False Data Injection Attack without Prior Topology Information
False Data Injection (FDI) attacks against powersystem state estimation are a growing concern for operators. Previously, most works on FDI attacks have been performedunder the assumption of the attacker having full knowledge ofthe underlying system without clear justification.
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV
Based on $14. 7~\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at 17 different center-of-mass energies between $3. 7730~\textrm{GeV}$ and $4. 5995~\textrm{GeV}$, Born cross sections of the two processes $e^+e^- \to p\bar{p}\eta$ and $e^+e^- \to p\bar{p}\omega$ are measured for the first time.
High Energy Physics - Experiment
ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation
In addition, ObjectAug can support category-aware augmentation that gives various possibilities to objects in each category, and can be easily combined with existing image-level augmentation methods to further boost performance.
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection
AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data.
Systematic electrochemical etching of various metal tips for tunneling spectroscopy and scanning probe microscopy
To ensure the reproducibility of experimental results, the fabrication of tips should be standardized, and a controllable and convenient system should be set up.
Materials Science
Label Contrastive Coding based Graph Neural Network for Graph Classification
Graph classification is a critical research problem in many applications from different domains.
Learning To Know Where To See: A Visibility-Aware Approach for Occluded Person Re-Identification
Several mainstream methods utilize extra cues (e. g., human pose information) to distinguish human parts from obstacles to alleviate the occlusion problem.
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Online Stochastic Optimization with Wasserstein Based Non-stationarity
We propose a unified Wasserstein-distance based measure to quantify the inaccuracy of the prior estimate in setting (i) and the non-stationarity of the system in setting (ii).
Search for the reaction $e^{+}e^{-} \rightarrow π^{+}π^{-} χ_{cJ}$ and a charmonium-like structure decaying to $χ_{cJ}π^{\pm}$ between 4.18 and 4.60 GeV
We search for the process $e^{+}e^{-}\rightarrow \pi ^{+}\pi ^{-} \chi_{cJ}$ ($J=0, 1, 2$) and for a charged charmonium-like state in the $\pi ^{\pm} \chi_{cJ}$ subsystem.
High Energy Physics - Experiment
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems
While many distributed optimization algorithms have been proposed for solving smooth or convex problems over the networks, few of them can handle non-convex and non-smooth problems.
Deoscillated Graph Collaborative Filtering
Stacking multiple cross-hop propagation layers and locality layers constitutes the DGCF model, which models high-order CF signals adaptively to the locality of nodes and layers.
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems
We prove that the stabilized GDA algorithm can achieve an $O(1/\epsilon^2)$ iteration complexity for minimizing the pointwise maximum of a finite collection of nonconvex functions.
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
Instead of learning on the complete input graph data, with a novel data augmentation strategy, \textsc{Subg-Con} learns node representations through a contrastive loss defined based on subgraphs sampled from the original graph instead.
EfficientFCN: Holistically-guided Decoding for Semantic Segmentation
State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance.