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Found 80 papers, 34 papers with code
Universal Estimation of Directed Information
Four estimators of the directed information rate between a pair of jointly stationary ergodic finite-alphabet processes are proposed, based on universal probability assignments.
Information Theory Information Theory
GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features
Our approach is not only flexible to adjust the trade-off between content and style, but also controllable between global and local.
On Compression of Unsupervised Neural Nets by Pruning Weak Connections
Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep Belif Networks(DBNs), are powerful in automatic feature extraction, unsupervised weight initialization and density estimation.
Evidence for $Z_{c}^{\pm}$ decays into the $ρ^{\pm} η_{c}$ final state
We study $e^{+}e^{-}$ collisions with a $\pi^{+}\pi^{-}\pi^{0}\eta_{c}$ final state using data samples collected with the BESIII detector at center-of-mass energies $\sqrt{s}=4. 226$, $4. 258$, $4. 358$, $4. 416$, and $4. 600$ GeV.
High Energy Physics - Experiment
Diversified Arbitrary Style Transfer via Deep Feature Perturbation
Image style transfer is an underdetermined problem, where a large number of solutions can satisfy the same constraint (the content and style).
LDMGAN: Reducing Mode Collapse in GANs with Latent Distribution Matching
Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images.
Cascade Style Transfer
This could help improve the quality and flexibility, and guide us to find domain-independent approaches.
An Investigation into the Stochasticity of Batch Whitening
Our work originates from the observation that while various whitening transformations equivalently improve the conditioning, they show significantly different behaviors in discriminative scenarios and training Generative Adversarial Networks (GANs).
An Efficient Agreement Mechanism in CapsNets By Pairwise Product
Capsule networks (CapsNets) are capable of modeling visual hierarchical relationships, which is achieved by the "routing-by-agreement" mechanism.
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution.
UCTGAN: Diverse Image Inpainting Based on Unsupervised Cross-Space Translation
Although existing image inpainting approaches have been able to produce visually realistic and semantically correct results, they produce only one result for each masked input.
Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$
We present an analysis of the process $\psi(3686) \to \Omega^- \bar{\Omega}^+$ ($\Omega^-\to K^-\Lambda$, $\bar{\Omega}^+\to K^+\bar{\Lambda}$, $\Lambda\to p\pi^-$, $\bar{\Lambda}\to \bar{p}\pi^+$) based on a data set of $448\times 10^6$ $\psi(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider.
High Energy Physics - Experiment
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
Our study is based on UC Toutiao (a news feed service integrated with the UC Browser App, serving hundreds of millions of users daily), where the source domain is the news and the target domain is the ad.
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization
Multimodal image-to-image translation (I2IT) aims to learn a conditional distribution that explores multiple possible images in the target domain given an input image in the source domain.
SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation
The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement.
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
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
Diverse Image Style Transfer via Invertible Cross-Space Mapping
Image style transfer aims to transfer the styles of artworks onto arbitrary photographs to create novel artistic images.
DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer
Gram-based and patch-based approaches are two important research lines of style transfer.
A Survey on Active Deep Learning: From Model-driven to Data-driven
Which samples should be labelled in a large data set is one of the most important problems for trainingof deep learning.
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
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
Unsupervised Dynamic Routing Via Competition Over Network Loss
Using the self-organizing map over the competition of the loss of individual neural column, we route the input to the most appropriate modules dynamically, by doing this we separate the input functional space into different sub spaces which are represented by each individual neural column.
Rapid COVID-19 Risk Screening by Eye-region Manifestations
It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts.
DualAST: Dual Style-Learning Networks for Artistic Style Transfer
Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations
The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0. 913 (95% CI, 0. 898-0. 927), with a sensitivity of 0. 695 (95% CI, 0. 643-0. 748), a specificity of 0. 904 (95% CI, 0. 891 -0. 919), an accuracy of 0. 875(0. 861-0. 889), and a F1 of 0. 611(0. 568-0. 655).
Randomized Primal-Dual Coordinate Method for Large-scale Linearly Constrained Nonsmooth Nonconvex Optimization
The large-scale linearly constrained nonsmooth nonconvex optimization finds wide applications in machine learning, including non-PSD Kernel SVM, linearly constrained Lasso with nonsmooth nonconvex penalty, etc.
RSS-based Multiple Sources Localization with Unknown Log-normal Shadow Fading
Then we utilize the K-means clustering method to obtain the rough locations of the off-grid sources as the initial feasible point of the ML estimator.
Quaternion-Based Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings.
Knowledge Enhanced Sports Game Summarization
Additionally, we also introduce a knowledge-enhanced summarizer that utilizes both live commentaries and the knowledge to generate sports news.
Enhancing Keyphrase Extraction from Academic Articles with their Reference Information
This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the following research on automatic keyphrase extraction.
Artistic Style Transfer with Internal-external Learning and Contrastive Learning
Although existing artistic style transfer methods have achieved significant improvement with deep neural networks, they still suffer from artifacts such as disharmonious colors and repetitive patterns.
Texture Reformer: Towards Fast and Universal Interactive Texture Transfer
In addition, we also introduce a novel learning-free view-specific texture reformation (VSTR) operation with a new semantic map guidance strategy to achieve more accurate semantic-guided and structure-preserved texture transfer.
Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks
Story ending generation is an interesting and challenging task, which aims to generate a coherent and reasonable ending given a story context.
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation
To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec).
Randomized Coordinate Subgradient Method for Nonsmooth Composite Optimization
For the case when $f$ is weakly convex and its subdifferential satisfies the global metric subregularity property, we derive the $\mathcal{O}(\varepsilon^{-4})$ iteration complexity in expectation.
RT-KGD: Relation Transition Aware Knowledge-Grounded Dialogue Generation
Nevertheless, the correlations between knowledge implied in the multi-turn context and the transition regularities between relations in KGs are under-explored.
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation
Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation.
AesUST: Towards Aesthetic-Enhanced Universal Style Transfer
Specifically, our approach introduces an aesthetic discriminator to learn the universal human-delightful aesthetic features from a large corpus of artist-created paintings.
Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction
A more challenging scenario is that emerging KGs consist of only unseen entities, called as disconnected emerging KGs (DEKGs).
MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning
Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i. e., texts and images), which enhance the diversity of knowledge.
Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task
Then, the machine attention values of each sentence were learned from a sentiment classification model.
A Cascaded Multi-Task Generative Framework for Detecting Aortic Dissection on 3D Non-contrast-enhanced Computed Tomography
Experiments also showed that the proposed framework could achieve an average accuracy of 0. 831, a sensitivity of 0. 938, and an F1-score of 0. 847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively.
An Investigation into Whitening Loss for Self-supervised Learning
A desirable objective in self-supervised learning (SSL) is to avoid feature collapse.
MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer
The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations.
Long-Document Cross-Lingual Summarization
Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the given documents in another language.
Rethinking Multi-Contrast MRI Super-Resolution: Rectangle-Window Cross-Attention Transformer and Arbitrary-Scale Upsampling
Recently, several methods have explored the potential of multi-contrast magnetic resonance imaging (MRI) super-resolution (SR) and obtain results superior to single-contrast SR methods.
Rethinking Fast Fourier Convolution in Image Inpainting
Recently proposed image inpainting method LaMa builds its network upon Fast Fourier Convolution (FFC), which was originally proposed for high-level vision tasks like image classification.
OppLoD: the Opponency based Looming Detector, Model Extension of Looming Sensitivity from LGMD to LPLC2
As a vital capability evolutionary survival, it has been extensively studied in neuroscience and is attracting increasing research interest in robotics due to its close relationship with collision detection and navigation.
Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning
By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes.
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning
Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability.
Sequential Recommendation with Probabilistic Logical Reasoning
To better capture the uncertainty and evolution of user tastes, SR-PLR embeds users and items with a probabilistic method and conducts probabilistic logical reasoning on users' interaction patterns.
VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs
In this paper, we propose VGOS, an approach for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10 views) to address these issues.
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks.
Revisiting Subgradient Method: Complexity and Convergence Beyond Lipschitz Continuity
In particular, when $f$ is convex, we show $\mathcal{O}(\log(k)/\sqrt{k})$ rate of convergence in terms of the suboptimality gap.
An Augmented Lagrangian Approach to Conically Constrained Non-monotone Variational Inequality Problems
Under an assumption, to be called the primal-dual variational coherence condition in the paper, we prove the convergence of ALAVI.
Do as I can, not as I get
This paper proposes a model called TMR to mine valuable information from simulated data environments.
StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models
Our work provides new insights into the C-S disentanglement in style transfer and demonstrates the potential of diffusion models for learning well-disentangled C-S characteristics.
Classification Committee for Active Deep Object Detection
The role of the classification committee is to select the most informative images according to their uncertainty values from the view of classification, which is expected to focus more on the discrepancy and representative of instances.
A Unified Analysis for the Subgradient Methods Minimizing Composite Nonconvex, Nonsmooth and Non-Lipschitz Functions
In this paper we propose a proximal subgradient method (Prox-SubGrad) for solving nonconvex and nonsmooth optimization problems without assuming Lipschitz continuity conditions.
TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style Transfer
To address this issue, we imitate the drawing process of humans and propose a Two-Stage Statistics-Aware Transformation (TSSAT) module, which first builds the global style foundation by aligning the global statistics of content and style features and then further enriches local style details by swapping the local statistics (instead of local features) in a patch-wise manner, significantly improving the stylization effects.
Design of JiuTian Intelligent Network Simulation Platform
This paper introduced the JiuTian Intelligent Network Simulation Platform, which can provide wireless communication simulation data services for the Open Innovation Platform.
BAAF: A Benchmark Attention Adaptive Framework for Medical Ultrasound Image Segmentation Tasks
The AI-based assisted diagnosis programs have been widely investigated on medical ultrasound images.
X-HRNet: Towards Lightweight Human Pose Estimation with Spatially Unidimensional Self-Attention
Inspired by this observation, we introduce a lightweight and powerful alternative, Spatially Unidimensional Self-Attention (SUSA), to the pointwise (1x1) convolution that is the main computational bottleneck in the depthwise separable 3c3 convolution.
CogVLM: Visual Expert for Pretrained Language Models
We introduce CogVLM, a powerful open-source visual language foundation model.
Ranked #4 on
Visual Question Answering (VQA)
on InfiMM-Eval
Emulators in JINSP
JINSP(Jiutian Intelligence Network Simulation Platform) describes a series of basic emulators and their combinations, such as the simulation of the protocol stack for dynamic users in a real environment, which is composed of user behavior simulation, base station simulation, and terminal simulation.
RACE-IT: A Reconfigurable Analog CAM-Crossbar Engine for In-Memory Transformer Acceleration
Transformer models represent the cutting edge of Deep Neural Networks (DNNs) and excel in a wide range of machine learning tasks.
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
Inverse Reinforcement Learning (IRL) -- the problem of learning reward functions from demonstrations of an \emph{expert policy} -- plays a critical role in developing intelligent systems.
ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank
To address the above issues, we propose ArtBank, a novel artistic style transfer framework, to generate highly realistic stylized images while preserving the content structure of the content images.
Implement services for business scenarios by combining basic emulators
This article mainly introduces how to use various basic emulators to form a combined emulator in the Jiutian Intelligence Network Simulation Platform to realize simulation service functions in different business scenarios.
3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos
Constructing photo-realistic Free-Viewpoint Videos (FVVs) of dynamic scenes from multi-view videos remains a challenging endeavor.
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation
Given that many deterministic conditional image generative models have been able to produce high-quality yet fixed results, we raise an intriguing question: is it possible for pre-trained deterministic conditional image generative models to generate diverse results without changing network structures or parameters?
PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping
3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene.
Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution
Recently, diffusion models (DM) have been applied in magnetic resonance imaging (MRI) super-resolution (SR) reconstruction, exhibiting impressive performance, especially with regard to detailed reconstruction.
Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles
By incorporating a sparse GP technique in HV modeling and adopting a dynamic GP prediction within the MPC framework, we significantly reduced the computation time of GP-MPC, marking it only 4. 6% higher than that of the conventional MPC.
Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt
To address the above problems, we propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST, which can generate highly realistic artistic stylized images while preserving the content structure of input content images well, without bringing obvious artifacts and disharmonious style patterns.
Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning
Arbitrary style transfer holds widespread attention in research and boasts numerous practical applications.
Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly
Zero pronoun resolution aims at recognizing dropped pronouns and pointing out their anaphoric mentions, while non-zero coreference resolution targets at clustering mentions referring to the same entity.
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization
For explicit consistency regularization, we minimize the difference between the prediction of the augmentation view and the prediction of the original view.
