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Found 124 papers, 34 papers with code
PixelThink: Towards Efficient Chain-of-Pixel Reasoning
Existing reasoning segmentation approaches typically fine-tune multimodal large language models (MLLMs) using image-text pairs and corresponding mask labels.
Holistic White-light Polyp Classification via Alignment-free Dense Distillation of Auxiliary Optical Chromoendoscopy
Without resorting to explicit image alignment, ADD learns pixel-wise cross-domain affinities to establish correspondences between feature maps, guiding the distillation along the most relevant pixel connections.
MRI motion correction via efficient residual-guided denoising diffusion probabilistic models
Res-MoCoDiff consistently achieved the highest SSIM and the lowest NMSE values, with a PSNR of up to 41. 91+-2. 94 dB for minor distortions.
Structure Causal Models and LLMs Integration in Medical Visual Question Answering
Medical Visual Question Answering (MedVQA) aims to answer medical questions according to medical images.
FreeInsert: Disentangled Text-Guided Object Insertion in 3D Gaussian Scene without Spatial Priors
This enables unsupervised and flexible object insertion in 3D scenes without spatial priors.
Automatically Generating Rules of Malicious Software Packages via Large Language Model
Today's security tools predominantly rely on predefined rules crafted by experts, making them poorly adapted to the emergence of software supply chain attacks.
Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling
A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests.
NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images.
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Our exploration demonstrates that Ascend NPUs are capable of efficiently and effectively training dense models with more than 100 billion parameters.
Functional Correspondences in the Human and Marmoset Visual Cortex During Movie Watching: Insights from Correlation, Redundancy, and Synergy
The world of beauty is deeply connected to the visual cortex, as perception often begins with vision in both humans and marmosets.
Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation
Then in practice, we refactor three crucial components through GS, to leverage its explicit scene representation and real-time rendering: (1) choosing the 2D neural Gaussian representation for physics-compliant scene and sensor modeling, (2) proposing a scene editing pipeline to leverage Gaussian primitives library for data augmentation, and (3) coupling a controllable diffusion model for scene expansion and harmonization.
CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and Recognition
Notably, the DDPM branch is discarded during inference, enabling real-time predictions without sacrificing accuracy. Experiments on the Cholec80 dataset show that for the anticipation task, our method achieves a 16% reduction in eMAE compared to state-of-the-art approaches, and for phase recognition, it improves the Jaccard score by 1. 0%.
Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning
The state space consists of the current congestion state, which is represented by the queue lengths of each link, and the current signal phase scheme of intersections.
DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance
Reinforcement learning (RL) has evolved into a widely investigated technology for the development of smart TSC strategies.
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting
Objective:This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details, thus accelerating MRI reconstruction.
Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging
We evaluate Triad across three tasks, namely, organ/tumor segmentation, organ/cancer classification, and medical image registration, in two data modalities (within-domain and out-of-domain) settings using 25 downstream datasets.
A Turing Test for Artificial Nets devoted to model Human Vision
In this 2022 work we argued that, despite claims about successful modeling of the visual brain using artificial nets, the problem is far from being solved (even for low-level vision).
A Unified Approach to Interpreting Self-supervised Pre-training Methods for 3D Point Clouds via Interactions
Recently, many self-supervised pre-training methods have been proposed to improve the performance of deep neural networks (DNNs) for 3D point clouds processing.
First-frame Supervised Video Polyp Segmentation via Propagative and Semantic Dual-teacher Network
Specifically, PSDNet adopts a teacher-student framework but employs two distinct types of teachers: the propagative teacher and the semantic teacher.
Temporal Action Localization with Cross Layer Task Decoupling and Refinement
Temporal action localization (TAL) involves dual tasks to classify and localize actions within untrimmed videos.
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation
Secondly, a novel multi-scale transformer architecture equipped with multi-grained user preference extraction is proposed to encode the interaction-aware sequential pattern enhanced by capturing temporal behavior-aware multi-grained preference .
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer
To address this limitation and incorporate TME spatial histology interactions in predicting NAC response for TNBC patients, we developed a histology context-aware transformer graph convolution network (NACNet).
Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification
Radio Frequency Fingerprint Identification (RFFI) technology uniquely identifies emitters by analyzing unique distortions in the transmitted signal caused by non-ideal hardware.
The Dynamics of Triple Interactions in Resting fMRI: Insights into Psychotic Disorders
We first estimated dynamic triple interactions using resting-state fMRI.
Correlation of Correlation Networks: High-Order Interactions in the Topology of Brain Networks
Moreover, after applying topological network analysis to the correlation of correlation networks, we observed that some high-order interaction hubs predominantly occurred in primary and high-level cognitive areas, such as the visual and fronto-parietal regions.
DACAT: Dual-stream Adaptive Clip-aware Time Modeling for Robust Online Surgical Phase Recognition
In the other stream, DACAT fine-tunes a new frame encoder to extract the frame-wise feature at the current moment.
Towards Robust Uncertainty-Aware Incomplete Multi-View Classification
Existing Evidential Deep Learning (EDL) based approaches attempt to address these issues, but they often struggle with conflicting evidence due to the limitations of the Dempster-Shafer combination rule, leading to unreliable decisions.
Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor Segmentation
Multi-modal brain tumor segmentation typically involves four magnetic resonance imaging (MRI) modalities, while incomplete modalities significantly degrade performance.
A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-Shot
We propose OneShotLP, a training-free framework for video-based license plate detection and recognition, leveraging these advanced models.
Prompt-Assisted Semantic Interference Cancellation on Moderate Interference Channels
The common parts of different users are transmitted simultaneously in a shared medium, resulting in superposition.
CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models
(3) Financial Practice: whether LLMs can fulfill the practical financial jobs, such as tax consultant, junior accountant and securities analyst.
Cross-Modal Spherical Aggregation for Weakly Supervised Remote Sensing Shadow Removal
To fill this gap, we propose a weakly supervised shadow removal network with a spherical feature space, dubbed S2-ShadowNet, to explore the best of both worlds for visible and infrared modalities.
Cost-Effective RF Fingerprinting Based on Hybrid CVNN-RF Classifier with Automated Multi-Dimensional Early-Exit Strategy
However, DL algorithms face the computational cost problem as the difficulty of the RFF task and the size of the DNN have increased dramatically.
SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation
In this context, we focus on robust video polyp segmentation by enhancing the adjacent feature consistency and rebuilding the reliable polyp representation.
Ranked #3 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Text-only Synthesis for Image Captioning
In this paper, we propose Text-only Synthesis for Image Captioning (ToCa), which further advances this relaxation with fewer human labor and less computing time.
Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing Segmentation
Few-shot segmentation (FSS) for remote sensing (RS) imagery leverages supporting information from limited annotated samples to achieve query segmentation of novel classes.
StyleSeg V2: Towards Robust One-shot Segmentation of Brain Tissue via Optimization-free Registration Error Perception
One-shot segmentation of brain tissue requires training registration-segmentation (reg-seg) dual-model iteratively, where reg-model aims to provide pseudo masks of unlabeled images for seg-model by warping a carefully-labeled atlas.
MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images
Photometric constraint is indispensable for self-supervised monocular depth estimation.
Clipped SGD Algorithms for Performative Prediction: Tight Bounds for Clipping Bias and Remedies
When the loss function is strongly convex, we quantify the lower and upper bounds for this clipping bias and demonstrate a bias amplification phenomenon with the sensitivity of data distribution.
Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation
To address this issue, we formulate the cross-receiver RFFI as a model adaptation problem, which adapts the trained model to unlabeled signals from a new receiver.
MonoBox: Tightness-free Box-supervised Polyp Segmentation using Monotonicity Constraint
We propose MonoBox, an innovative box-supervised segmentation method constrained by monotonicity to liberate its training from the user-unfriendly box-tightness assumption.
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners
Our experiments across 11 arithmetic and commonsense reasoning tasks show that RankPrompt significantly enhances the reasoning performance of ChatGPT and GPT-4, with improvements of up to 13%.
A Collision-Aware Cable Grasping Method in Cluttered Environment
We introduce a Cable Grasping-Convolutional Neural Network designed to facilitate robust cable grasping in cluttered environments.
SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector
To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets.
Unified Hallucination Detection for Multimodal Large Language Models
Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination.
Exploration of Adolescent Depression Risk Prediction Based on Census Surveys and General Life Issues
We introduced a method for managing severely imbalanced high-dimensional data and an adaptive predictive approach tailored to data structure characteristics.
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
(3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs.
Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications
The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration.
Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks
The human brain has a complex, intricate functional architecture.
A Survey of Methods for Handling Disk Data Imbalance
Class imbalance exists in many classification problems, and since the data is designed for accuracy, imbalance in data classes can lead to classification challenges with a few classes having higher misclassification costs.
XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation
Despite the promising performance of existing visual models on public benchmarks, the critical assessment of their robustness for real-world applications remains an ongoing challenge.
IBoxCLA: Towards Robust Box-supervised Segmentation of Polyp via Improved Box-dice and Contrastive Latent-anchors
In response, we propose two innovative learning fashions, Improved Box-dice (IBox) and Contrastive Latent-Anchors (CLA), and combine them to train a robust box-supervised model IBoxCLA.
One-Bit Channel Estimation for IRS-aided Millimeter-Wave Massive MU-MISO System
The second estimator utilizes the common row-structured sparsity induced by the IRS-to-BS channel shared among the users, and develops another type-II ML solution via the block SBL (BSBL) and the variational EM.
Integrated Robotics Networks with Co-optimization of Drone Placement and Air-Ground Communications
Terrestrial robots, i. e., unmanned ground vehicles (UGVs), and aerial robots, i. e., unmanned aerial vehicles (UAVs), operate in separate spaces.
Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery
We employed a clustering method with varying constraint conditions, ranging from strict to loose, allowing for the generation of dependable labels for a subset of unlabeled data during the training phase.
Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review
The advent of deep learning has brought a revolutionary transformation to image denoising techniques.
Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification
Mammogram image is important for breast cancer screening, and typically obtained in a dual-view form, i. e., cranio-caudal (CC) and mediolateral oblique (MLO), to provide complementary information.
Response toward Public Health Policy Ambiguity and Insurance Decisions
Adjustments to public health policy are common.
Cooperative IoT Data Sharing with Heterogeneity of Participants Based on Electricity Retail
With the development of Internet of Things (IoT) and big data technology, the data value is increasingly explored in multiple practical scenarios, including electricity transactions.
Cloud-RAIN: Point Cloud Analysis with Reflectional Invariance
We prove a theorem to explain why Cloud-RAIN can enjoy reflection symmetry.
Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy
To address this problem, we applied multivariate mutual information, specifically, Total Correlation and Dual Total Correlation to reveal higher-order information in the brain.
POSGen: Personalized Opening Sentence Generation for Online Insurance Sales
Such personalized opening sentence generation is challenging because (i) there are limited historical samples for conversation topic recommendation in online insurance sales and (ii) existing text generation schemes often fail to support customized topic ordering based on user preferences.
Continual learning on deployment pipelines for Machine Learning Systems
Following the development of digitization, a growing number of large Original Equipment Manufacturers (OEMs) are adapting computer vision or natural language processing in a wide range of applications such as anomaly detection and quality inspection in plants.
Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation
In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues.
Joint Secure Communication and Radar Beamforming: A Secrecy-Estimation Rate-Based Design
For the AN-aided SRM, by leveraging alternating optimization similar closed-form solution is obtained for the beamformer and the AN covariance matrix.
Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions
The learned knowledge flows across branches along two directions: a cross direction (disparity guides distribution in ACS) and a parallel direction (disparity guides disparity in APS).
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing
More specifically, our efforts consist of three parts: 1) a data-free training strategy to train latent mappers to bridge the latent space of CLIP and StyleGAN; 2) for more precise mapping, temporal relative consistency is proposed to address the knowledge distribution bias problem among different latent spaces; 3) to refine the mapped latent in s space, adaptive style mixing is also proposed.
Functional Connectome of the Human Brain with Total Correlation
In this work we build on this idea to infer a large scale (whole brain) connectivity network based on Total Correlation and show the possibility of using this kind of networks as biomarkers of brain alterations.
Functional Connectivity via Total Correlation: Analytical results in Visual Areas
As opposed to previous empirical approaches, in this work we present analytical results to prove the advantages of Total Correlation over Mutual Information to describe the functional connectivity.
Semi-supervised Ranking for Object Image Blur Assessment
Based on this dataset, we propose a method to obtain the blur scores only with the pairwise rank labels as supervision.
Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs
Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae.
Multi-scale frequency separation network for image deblurring
MSFS-Net introduces the frequency separation module (FSM) into an encoder-decoder network architecture to capture the low- and high-frequency information of image at multiple scales.
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling
Recently, deep learning methods have shown great success in 3D point cloud upsampling.
Noise Perturbation for Saliency Prediction with Psychophysical Synthetic Images
The primary goal of this study is to investigate the performance of saliency prediction in CNN and classic models with psychophysical synthetic images under noise perturbation.
Dual-Stage Approach Toward Hyperspectral Image Super-Resolution
Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution.
Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition
Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations.
Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression
In this paper, we emphatically summarize that learning an adaptive label distribution on ordinal regression tasks should follow three principles.
ITTR: Unpaired Image-to-Image Translation with Transformers
Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
CRIS: CLIP-Driven Referring Image Segmentation
In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances.
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
Specifically, we first train a self-supervised style encoder on the generic artistic dataset to extract the representations of arbitrary styles.
Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion
Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e. g., subcortical nuclei, etc.
Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition
Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network with the proposed regional and temporal based auxiliary task learning (RTATL) framework.
Exploring Set Similarity for Dense Self-supervised Representation Learning
By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks.
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications
In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air.
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring
To accurately pour drinks into various containers is an essential skill for service robots.
Symbol-Level Precoding Through the Lens of Zero Forcing and Vector Perturbation
We examine how insights arising from this perturbed ZF and VP interpretations can be leveraged to i) substantially simplify the optimization of certain SLP design criteria, namely, total or peak power minimization subject to SEP quality guarantees; and ii) draw connections with some existing SLP designs.
Contrast Sensitivity Functions in Autoencoders
Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images.
Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition
In order to incorporate the intra-level AU relation and inter-level AU regional relevance simultaneously, a multi-level AU relation graph is constructed and graph convolution is performed to further enhance AU regional features of each level.
Gravitational effect of a plasma on the shadow of Schwarzschild black holes
Considering a Schwarzschild black hole surrounded by a fully ionized hydrogen plasma, we study the refractive effect and the pure gravitational effect of the plasma on the shadow.
General Relativity and Quantum Cosmology Astrophysics of Galaxies
Image deblurring based on lightweight multi-information fusion network
In the encoding stage, the image feature is reduced to various smallscale spaces for multi-scale information extraction and fusion without a large amount of information loss.
Magnetoelectric coupling and decoupling in multiferroic hexagonal YbFeO3 thin films
The coupling between ferroelectric and magnetic orders in multiferroic materials and the nature of magnetoelectric (ME) effects are enduring experimental challenges.
Materials Science Other Condensed Matter
A Psychophysical Oriented Saliency Map Prediction Model
Finally, the proposed model also can be applied to spatial-temporal saliency prediction and got better performance.
Experimental Evidence that Zn Impurities Pin Pair-Density-Wave Order in La$_{2-x}$Ba$_x$CuO$_4$
Both Zn-doping and $c$-axis magnetic fields have been observed to increase the spin stripe order in La$_{2-x}$Ba$_x$CuO$_4$ with $x$ close to 1/8.
Superconductivity
Loop-induced $ZZ$ production at the LHC: an improved description by matrix-element matching
Loop-induced $ZZ$ production can be enhanced by the large gluon flux at the LHC, and thus should be taken into account in relevant experimental analyses.
High Energy Physics - Phenomenology High Energy Physics - Experiment
One-Bit Symbol-Level Precoding for MU-MISO Downlink with Intelligent Reflecting Surface
This paper considers symbol-level precoding (SLP) for multiuser multi-input single-output (MISO) downlink transmission with the aid of intelligent reflecting surface (IRS).
Siamese Keypoint Prediction Network for Visual Object Tracking
Visual object tracking aims to estimate the location of an arbitrary target in a video sequence given its initial bounding box.
Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning
Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles.
AliCoCo: Alibaba E-commerce Cognitive Concept Net
However, user needs in e-commerce are still not well defined, and none of the existing ontologies has the enough depth and breadth for universal user needs understanding.
Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing
Whole slide imaging (WSI) is an emerging technology for digital pathology.
Trend and forecasting of the COVID-19 outbreak in China
By using the public data from Jan. 20 to Feb. 11, 2020, we perform data-driven analysis and forecasting on the COVID-19 epidemic in mainland China, especially Hubei province.
Minimum Symbol-Error Probability Symbol-Level Precoding with Intelligent Reflecting Surface
Recently, the use of intelligent reflecting surface (IRS) has gained considerable attention in wireless communications.
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution
Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently.
Sum Rate Maximization for Multiuser MISO Downlink with Intelligent Reflecting Surface
The intelligent reflecting surface (IRS) is a planar array with massive reconfigurable passive elements, which can align the reflecting signals at the receivers via controlling the phase shifts at each element independently.
Machine Learning-based Signal Detection for PMH Signals in Load-modulated MIMO System
By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector.
Adversarial Training Methods for Network Embedding
To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain.
Latency Minimization for Multiuser Computation Offloading in Fog-Radio Access Networks
This paper considers computation offloading in fog-radio access networks (F-RAN), where multiple user equipments (UEs) offload their computation tasks to the F-RAN through a number of fog nodes.
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization
As a real scenes sensing approach, depth information obtains the widespread applications.
Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation
Hyperspectral super-resolution (HSR) is a problem that aims to estimate an image of high spectral and spatial resolutions from a pair of co-registered multispectral (MS) and hyperspectral (HS) images, which have coarser spectral and spatial resolutions, respectively.
Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification
In this paper, we present correlated logistic (CorrLog) model for multilabel image classification.
Adapting Stochastic Block Models to Power-Law Degree Distributions
Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data.
StyleRemix: An Interpretable Representation for Neural Image Style Transfer
Multi-Style Transfer (MST) intents to capture the high-level visual vocabulary of different styles and expresses these vocabularies in a joint model to transfer each specific style.
End-to-end Hand Mesh Recovery from a Monocular RGB Image
In this paper, we present a HAnd Mesh Recovery (HAMR) framework to tackle the problem of reconstructing the full 3D mesh of a human hand from a single RGB image.
Polarization Fraction Measurement in same sign WW scattering using Deep Learning
Studying the longitudinally polarized fraction of $W^\pm W^\pm$ scattering at the LHC is crucial to examine the unitarization mechanism of the vector boson scattering amplitude through Higgs and possible new physics.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Receiver Design for Faster-than-Nyquist Signaling: Deep-learning-based Architectures
Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
Influence Maximization with \varepsilon-Almost Submodular Threshold Functions
Influence maximization is the problem of selecting $k$ nodes in a social network to maximize their influence spread.
Adversarial Network Embedding
Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization.
Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising
In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers.
Conditional Graphical Lasso for Multi-Label Image Classification
Multi-label image classification aims to predict multiple labels for a single image which contains diverse content.
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.
Information Content Weighting for Perceptual Image Quality Assessment
This paper aims to test the hypothesis that when viewing natural images, the optimal perceptual weights for pooling should be proportional to local information content, which can be estimated in units of bit using advanced statistical models of natural images.
Ranked #7 on
Full reference image quality assessment
on ESPL
