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Found 161 papers, 41 papers with code
RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips
We collected and annotated a wide range of meta-data with respect to medical dialogue including doctor profiles, hospital departments, diseases and symptoms for fine-grained analysis on language usage pattern and clinical diagnosis.
Domain-specific knowledge distillation yields smaller and better models for conversational commerce
We demonstrate that knowledge distillation can be used not only to reduce model size, but to simultaneously adapt a contextual language model to a specific domain.
多特征融合的越英端到端语音翻译方法(A Vietnamese-English end-to-end speech translation method based on multi-feature fusion)
“语音翻译的编码器需要同时编码语音中的声学和语义信息, 单一的Fbank或Wav2vec2语音特征表征能力存在不足。本文通过分析人工的Fbank特征与自监督的Wav2vec2特征间的差异性, 提出基于交叉注意力机制的声学特征融合方法, 并探究了不同的自监督特征和融合方式, 加强模型对语音中声学和语义信息的学习。结合越南语语音特点, 以Fbank特征为主、Pitch特征为辅混合编码Fbank表征, 构建多特征融合的越-英语音翻译模型。实验表明, 使用多特征的语音翻译模型相比单特征翻译效果更优, 与简单的特征拼接方法相比更有效, 所提的多特征融合方法在越-英语音翻译任务上提升了1. 97个BLEU值。”
Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition
Chinese Named Entity Recognition (NER) has continued to attract research attention.
Graph Contrastive Learning for Skeleton-based Action Recognition
In this paper, we propose a graph contrastive learning framework for skeleton-based action recognition (\textit{SkeletonGCL}) to explore the \textit{global} context across all sequences.
Scene-aware Egocentric 3D Human Pose Estimation
To this end, we propose an egocentric depth estimation network to predict the scene depth map from a wide-view egocentric fisheye camera while mitigating the occlusion of the human body with a depth-inpainting network.
COLA: Improving Conversational Recommender Systems by Collaborative Augmentation
Inspired by collaborative filtering, we propose a collaborative augmentation (COLA) method to simultaneously improve both item representation learning and user preference modeling to address these issues.
WAIR-D: Wireless AI Research Dataset
The statistical data hinder the trained AI models from further fine-tuning for a specific scenario, and ray-tracing data with limited environments lower down the generalization capability of the trained AI models.
Privacy-Preserving Visual Localization with Event Cameras
We present a robust, privacy-preserving visual localization algorithm using event cameras.
Attention-based Class Activation Diffusion for Weakly-Supervised Semantic Segmentation
An intuitive solution is ``coupling'' the CAM with the long-range attention matrix of visual transformers (ViT) We find that the direct ``coupling'', e. g., pixel-wise multiplication of attention and activation, achieves a more global coverage (on the foreground), but unfortunately goes with a great increase of false positives, i. e., background pixels are mistakenly included.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
CAE v2: Context Autoencoder with CLIP Target
That is to say, the smaller the model, the lower the mask ratio needs to be.
Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining
The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO.
Ranked #4 on
Object Detection
on COCO test-dev
A survey on the development status and application prospects of knowledge graph in smart grids
With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention.
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers
We introduce a newly designed framework that (i) simultaneously derives features from both image and latent shape spaces with large intra-class variations; and (ii) gains increased model interpretability by allowing direct access to the underlying geometric features of image data.
Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion
Deep Learning-based 2D/3D registration enables fast, robust, and accurate X-ray to CT image fusion when large annotated paired datasets are available for training.
U-HRNet: Delving into Improving Semantic Representation of High Resolution Network for Dense Prediction
Therefore, we designed a U-shaped High-Resolution Network (U-HRNet), which adds more stages after the feature map with strongest semantic representation and relaxes the constraint in HRNet that all resolutions need to be calculated parallel for a newly added stage.
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
Recently, transformer-based networks have shown impressive results in semantic segmentation.
Ranked #2 on
Real-Time Semantic Segmentation
on CamVid
Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach
Deploying ultra-dense networks that operate on millimeter wave (mmWave) band is a promising way to address the tremendous growth on mobile data traffic.
Design of the PID temperature controller for an alkaline electrolysis system with time delays
This paper focuses on the design of the PID temperature controller for an alkaline electrolysis system to achieve fast and stable temperature control.
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control
By pretraining with non-expert demonstrations, PwD-MARL improves sample efficiency in the process of online MARL with a warm start.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control
Flocking control is a challenging problem, where multiple agents, such as drones or vehicles, need to reach a target position while maintaining the flock and avoiding collisions with obstacles and collisions among agents in the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition
In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel classes, particularly for fine-grained actions.
IVT: An End-to-End Instance-guided Video Transformer for 3D Pose Estimation
In particular, we firstly formulate video frames as a series of instance-guided tokens and each token is in charge of predicting the 3D pose of a human instance.
Ranked #10 on
3D Multi-Person Pose Estimation
on Panoptic
Follow Me: Conversation Planning for Target-driven Recommendation Dialogue Systems
Recommendation dialogue systems aim to build social bonds with users and provide high-quality recommendations.
UnrealEgo: A New Dataset for Robust Egocentric 3D Human Motion Capture
We present UnrealEgo, i. e., a new large-scale naturalistic dataset for egocentric 3D human pose estimation.
Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment
Detection Transformer (DETR) relies on One-to-One assignment, i. e., assigning one ground-truth object to only one positive object query, for end-to-end object detection and lacks the capability of exploiting multiple positive object queries.
Seeing Far in the Dark with Patterned Flash
Flash illumination is widely used in imaging under low-light environments.
Dynamic Graph Reasoning for Multi-person 3D Pose Estimation
Finally, the 3D poses are decoded according to dynamic decoding graphs for each detected person.
UFO: Unified Feature Optimization
UFO aims to benefit each single task with a large-scale pretraining on all tasks.
Action Quality Assessment with Temporal Parsing Transformer
Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences.
Learning Granularity-Unified Representations for Text-to-Image Person Re-identification
In PGU, we adopt a set of shared and learnable prototypes as the queries to extract diverse and semantically aligned features for both modalities in the granularity-unified feature space, which further promotes the ReID performance.
Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach
In this method, the integrated power is decomposed into individual device power by non-intrusive load monitoring, and the power of individual appliances is predicted separately using a federated deep learning model.
MME-CRS: Multi-Metric Evaluation Based on Correlation Re-Scaling for Evaluating Open-Domain Dialogue
Firstly, we build an evaluation metric composed of 5 groups of parallel sub-metrics called Multi-Metric Evaluation (MME) to evaluate the quality of dialogue comprehensively.
Structured Light with Redundancy Codes
We propose three applications using our redundancy codes: (1) Self error-correction for SL imaging under strong ambient light, (2) Error detection for adaptive reconstruction under global illumination, and (3) Interference filtering with device-specific projection sequence encoding, especially for event camera-based SL and light curtain devices.
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
In this paper, we rethink the paradigm and explore a new regime: {\em fine-tuning a small part of parameters in the backbone}.
Ranked #3 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map
Place recognition technology endows a SLAM algorithm with the ability to eliminate accumulated errors and to relocalize itself.
PP-Matting: High-Accuracy Natural Image Matting
Also, we propose a semantic context branch (SCB) that adopts a semantic segmentation subtask.
Ranked #1 on
Image Matting
on Distinctions-646
Human-Object Interaction Detection via Disentangled Transformer
To associate the predictions of disentangled decoders, we first generate a unified representation for HOI triplets with a base decoder, and then utilize it as input feature of each disentangled decoder.
Implicit Sample Extension for Unsupervised Person Re-Identification
Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy.
Glass Segmentation with RGB-Thermal Image Pairs
Due to the large difference between the transmission property of visible light and that of the thermal energy through the glass where most glass is transparent to the visible light but opaque to thermal energy, glass regions of a scene are made more distinguishable with a pair of RGB and thermal images than solely with an RGB image.
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks
Inspired by software testing, a number of structural coverage criteria are designed and proposed to measure the test adequacy of DNNs.
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation
After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates.
Image Steganography based on Style Transfer
Image steganography is the art and science of using images as cover for covert communications.
Thermal Modelling and Controller Design of an Alkaline Electrolysis System under Dynamic Operating Conditions
A control-oriented thermal model is established in the form of a third-order time-delay process, which is used for simulation and controller design.
Imperceptible and Multi-channel Backdoor Attack against Deep Neural Networks
Recent researches demonstrate that Deep Neural Networks (DNN) models are vulnerable to backdoor attacks.
Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision
Specifically, we first generate pseudo labels for the EgoPW dataset with a spatio-temporal optimization method by incorporating the external-view supervision.
An Adaptive Neuro-Fuzzy System with Integrated Feature Selection and Rule Extraction for High-Dimensional Classification Problems
The effectiveness of the FSRE-AdaTSK is demonstrated on 19 datasets of which five are in more than 2000 dimension including two with dimension greater than 7000.
Compression-Resistant Backdoor Attack against Deep Neural Networks
After training, the backdoor attack against DNN is robust to image compression.
Training Object Detectors From Scratch: An Empirical Study in the Era of Vision Transformer
Instead of proposing a specific vision transformer based detector, in this work, our goal is to reveal the insights of training vision transformer based detectors from scratch.
Hybrid Atlas Building with Deep Registration Priors
Registration-based atlas building often poses computational challenges in high-dimensional image spaces.
MFNet: Multi-filter Directive Network for Weakly Supervised Salient Object Detection
The multiple accurate cues from multiple DFs are then simultaneously propagated to the saliency network with a multi-guidance loss.
3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image
Our key intuition is that style transfer and view synthesis have to be jointly modeled for this task.
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning
In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework
Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers.
URIR: Recommendation algorithm of user RNN encoder and item encoder based on knowledge graph
This implies that URIR can effectively use knowledge graph to obtain better user codes and item codes, thereby obtaining better recommendation results.
Zero-bias Deep Neural Network for Quickest RF Signal Surveillance
In this paper, we provide a deep learning framework for RF signal surveillance.
One-Bit Matrix Completion with Differential Privacy
In this paper, we propose a unified framework for ensuring a strong privacy guarantee of one-bit matrix completion with DP.
Applying Differential Privacy to Tensor Completion
Tensor completion aims at filling the missing or unobserved entries based on partially observed tensors.
SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System
In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation.
A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks
Biphasic facial age translation aims at predicting the appearance of the input face at any age.
To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection
Despite of the success of previous works, explorations on an effective training strategy for the saliency network and accurate matches between image-level annotations and salient objects are still inadequate.
Mining Contextual Information Beyond Image for Semantic Segmentation
To address this, this paper proposes to mine the contextual information beyond individual images to further augment the pixel representations.
Learning to Detect: A Data-driven Approach for Network Intrusion Detection
With massive data being generated daily and the ever-increasing interconnectivity of the world's Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security.
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound
In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously.
Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition
The task of skeleton-based action recognition remains a core challenge in human-centred scene understanding due to the multiple granularities and large variation in human motion.
Flip Learning: Erase to Segment
Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome manual annotation.
Distributed Learning for Time-varying Networks: A Scalable Design
The wireless network is undergoing a trend from "onnection of things" to "connection of intelligence".
A Novel Interactive Two-stage Joint Retail Electricity Market for Multiple Microgrids
To accommodate the advent of microgrids (MG) managing distributed energy resources (DER) in distribution systems, an interactive two-stage joint retail electricity market mechanism is proposed to provide an effective platform for these prosumers to proactively join in retail transactions.
Deep Iterative 2D/3D Registration
A refinement step using the classical optimization-based 2D/3D registration method applied in combination with Deep Learning-based techniques can provide the required accuracy.
Bayesian Atlas Building with Hierarchical Priors for Subject-specific Regularization
This paper presents a novel hierarchical Bayesian model for unbiased atlas building with subject-specific regularizations of image registration.
HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration
Our experimental results show that the proposed method is able to perform high-quality restoration for unconstrained underwater images without any supervision.
Seeing in Extra Darkness Using a Deep-Red Flash
We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source.
Detect and remove watermark in deep neural networks via generative adversarial networks
Experimental evaluations on the MNIST and CIFAR10 datasets demonstrate that, the proposed method can effectively remove about 98% of the watermark in DNN models, as the watermark retention rate reduces from 100% to less than 2% after applying the proposed attack.
Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations
Experimental results show that, the backdoor detection rate of the proposed defense method is 99. 63%, 99. 76% and 99. 91% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively.
AdvParams: An Active DNN Intellectual Property Protection Technique via Adversarial Perturbation Based Parameter Encryption
Moreover, the proposed method only needs to encrypt an extremely low number of parameters, and the proportion of the encrypted parameters of all the model's parameters is as low as 0. 000205%.
One Shot Face Swapping on Megapixels
Extensive experiments demonstrate the superiority of MegaFS and the first megapixel level face swapping database is released for research on DeepFake detection and face image editing in the public domain.
Ranked #7 on
Face Swapping
on FaceForensics++
Class-Incremental Learning for Wireless Device Identification in IoT
The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services.
Estimating Egocentric 3D Human Pose in Global Space
Furthermore, these methods suffer from limited accuracy and temporal instability due to ambiguities caused by the monocular setup and the severe occlusion in a strongly distorted egocentric perspective.
Unsupervised Multi-Source Domain Adaptation for Person Re-Identification
Although achieving great success, most of them only use limited data from a single-source domain for model pre-training, making the rich labeled data insufficiently exploited.
Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Images
To address these challenges, in this paper, we propose a method to protect the intellectual properties of DNN models by using an additional class and steganographic images.
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World
In this paper, we propose a robust physical backdoor attack method, PTB (physical transformations for backdoors), to implement the backdoor attacks against deep learning models in the real physical world.
Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT
Abnormality detection is essential to the performance of safety-critical and latency-constrained systems.
Drug Discovery Approaches using Quantum Machine Learning
Traditional drug discovery pipeline takes several years and cost billions of dollars.
Robust Reinforcement Learning under model misspecification
Reinforcement learning has achieved remarkable performance in a wide range of tasks these days.
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks
We not only verify the performance gain achieved, but also provide implementation-friend designs, i. e., a scalable neural network design for the agent and a virtual environment training framework.
ActiveGuard: An Active DNN IP Protection Technique via Adversarial Examples
For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of the DNN model will not be affected.
Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process.
Learning the Update Operator for 2D/3D Image Registration
We aim to address this gap by incorporating traditional methods in deep neural networks using known operator learning.
Incentive-based Decentralized Routing for Connected and Autonomous Vehicles using Information Propagation
The first stage incorporates a decentralized local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles' knowledge of local traffic information.
Autonomous Vehicles
Physics and Society
Computer Science and Game Theory
Systems and Control
Systems and Control
Two-sided Dirichlet heat estimates of symmetric stable processes on horn-shaped regions
In this paper, we consider symmetric $\alpha$-stable processes on (unbounded) horn-shaped regions which are non-uniformly $C^{1, 1}$ near infinity.
Probability
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey
In this paper, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators.
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
Adaptive Remote Sensing Image Attribute Learning for Active Object Detection
In recent years, deep learning methods bring incredible progress to the field of object detection.
Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive-based learning frameworks.
Detection of magnetic gap in the topological surface states of MnBi2Te4
An essential ingredient to realize these quantum states is the magnetic gap in the topological surface states induced by the out-of-plane ferromagnetism on the surface of MnBi2Te4.
Materials Science
A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms.
Distant Domain Transfer Learning for Medical Imaging
In this paper, we propose a distant domain transfer learning (DDTL) method for medical image classification.
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy
In this paper, we consider the problem of discrete distribution estimation under local differential privacy constraints.
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems
To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager.
Group Contextual Encoding for 3D Point Clouds
In this work, we extended the contextual encoding layer that was originally designed for 2D tasks to 3D Point Cloud scenarios.
Deep Learning for Regularization Prediction in Diffeomorphic Image Registration
This paper presents a predictive model for estimating regularization parameters of diffeomorphic image registration.
NaturalAE: Natural and Robust Physical Adversarial Examples for Object Detectors
Experimental results demonstrate that, the generated adversarial examples are robust under various indoor and outdoor physical conditions, including different distances, angles, illuminations, and photographing.
3D Invisible Cloak
on person stealth attacks, and propose 3D transformations to generate 3D invisible cloak.
SocialGuard: An Adversarial Example Based Privacy-Preserving Technique for Social Images
After being injected with the perturbation, the social image can easily fool the object detector, while its visual quality will not be degraded.
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive learning frameworks.
Integrated Gallium Nitride Nonlinear Photonics
Thanks to its high nonlinearity and high refractive index contrast, GaN-on-insulator (GaNOI) is also a promising platform for nonlinear optical applications.
Optics Applied Physics
Robust MIMO Radar Waveform-Filter Design for Extended Target Detection in the Presence of Multipath
We resort to a sequential optimization procedure alternating between two SDR problems to optimize the covariance matrix of transmit waveform and receive filter, so we call the algorithm Duality Maximization Double Semi-Definite Relaxation(DMDSDR).
Biomedical named entity recognition using BERT in the machine reading comprehension framework
Using the sequence labeling framework to implement biomedical named entity recognition (BioNER) is currently a conventional method.
Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends
A Counter-UAS (C-UAS) system is defined as a system or device capable of lawfully and safely disabling, disrupting, or seizing control of an unmanned aircraft or unmanned aircraft system.
Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices
In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals.
Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement
In the first stage, heatmap regression network is applied to obtain a rough localization result, and a set of proposal keypoints, called guided points, are sampled.
TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition
Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues.
Quasi-Deterministic Channel Model for mmWaves: Mathematical Formalization and Validation
5G and beyond networks will use, for the first time ever, the millimeter wave (mmWave) spectrum for mobile communications.
Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.
Energy Model for UAV Communications: Experimental Validation and Model Generalization
In this paper, via extensive flight experiments, we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs, and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging, if not impossible.
DeepFLASH: An Efficient Network for Learning-based Medical Image Registration
This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration.
Sex Differences in Severity and Mortality Among Patients With COVID-19: Evidence from Pooled Literature Analysis and Insights from Integrated Bioinformatic Analysis
Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences.
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
Besides, the attack is further enhanced by adaptively tuning the translations of object and background.
Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation.
Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge
Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks.
ADCC: An Effective and Intelligent Attention Dense Color Constancy System for Studying Images in Smart Cities
The augmented images help to tell apart the edge gradients, edge pixels and non-edge ones in log-histogram, which contribute significantly to the feature extraction and color-ambiguity elimination, thereby advancing the accuracy of illuminant estimation.
Fast Color Constancy with Patch-wise Bright Pixels
In this paper, a learning-free color constancy algorithm called the Patch-wise Bright Pixels (PBP) is proposed.
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate.
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation
In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into multiple short and attractive sub-videos with each one containing an unbroken story.
Towards Good Practices for Video Object Segmentation
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning.
BIG-bench Machine Learning
One-shot visual object segmentation
+2
FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces
In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis.
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning
Aspect category detection is an essential task for sentiment analysis and opinion mining.
Turán Problems for Vertex-disjoint Cliques in Multi-partite Hypergraphs
First, we consider a multi-partite version of the Erd\H{o}s matching conjecture and determine $ex_s(K_{n_1, \ldots, n_r}^{(s)}, kK_s^{(s)})$ for $n_1\geq s^3k^2+sr$.
Combinatorics
Realistic Channel Models Pre-training
In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models.
Region-Manipulated Fusion Networks for Pancreatitis Recognition
Specifically, to effectively highlight the imperceptible lesion regions, a novel region-manipulated scheme in RMFN is proposed to force the lesion regions while weaken the non-lesion regions by ceaselessly aggregating the multi-scale local information onto feature maps.
Fractional-order Backpropagation Neural Networks: Modified Fractional-order Steepest Descent Method for Family of Backpropagation Neural Networks
This paper offers a novel mathematical approach, the modified Fractional-order Steepest Descent Method (FSDM) for training BackPropagation Neural Networks (BPNNs); this differs from the majority of the previous approaches and as such.
NIL: Learning Nonlinear Interpolants
Nonlinear interpolants have been shown useful for the verification of programs and hybrid systems in contexts of theorem proving, model checking, abstract interpretation, etc.
Deep Reinforcement Learning for Scheduling in Cellular Networks
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia.
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
Motivation: In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction.
Ranked #1 on
Named Entity Recognition
on BC4CHEMD
Re-Identification Supervised Texture Generation
The estimation of 3D human body pose and shape from a single image has been extensively studied in recent years.
Saec: Similarity-Aware Embedding Compression in Recommendation Systems
Production recommendation systems rely on embedding methods to represent various features.
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models
Accurate prediction of fading channel in future is essential to realize adaptive transmission and other methods that can save power and provide gains.
Stereoscopic Dark Flash for Low-light Photography
In this work, we present a camera configuration for acquiring "stereoscopic dark flash" images: a simultaneous stereo pair in which one camera is a conventional RGB sensor, but the other camera is sensitive to near-infrared and near-ultraviolet instead of R and B.
Rational function regression method for numerical analytic continuation
A simple method for numerical analytic continuation is developed.
Rational function regression method for numerical analytic continuation
A simple method for numerical analytic continuation is developed.
Learning Optimal Deep Projection of $^{18}$F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes.
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns.
Programmable Triangulation Light Curtains
A vehicle on a road or a robot in the field does not need a full-featured 3D depth sensor to detect potential collisions or monitor its blind spot.
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles
With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis.
Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
For single-vertebra registration, we achieve an accuracy of 0. 74$\pm$0. 26 mm and highly improved robustness.
Think Visually: Question Answering through Virtual Imagery
In this paper, we study the problem of geometric reasoning in the context of question-answering.
Neural Network Language Modeling with Letter-based Features and Importance Sampling
In this paper we describe an extension of the Kaldi software toolkit to support neural-based language modeling, intended for use in automatic speech recognition (ASR) and related tasks.
Ranked #34 on
Speech Recognition
on LibriSpeech test-other
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases
For word level task our best run achieved MAE 0. 545 (ranked 2nd), PCC 0. 892 (ranked 2nd) in valence prediction and MAE 0. 857 (ranked 1st), PCC 0. 678 (ranked 2nd) in arousal prediction.
Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths
Results In this article, we present a hierarchical recurrent neural networks (RNNs)-based method to integrate the SDP and sentence sequence for DDI extraction task.
Reflectance Capture Using Univariate Sampling of BRDFs
A collocated setup provides only a 1-D "univariate" sampling of the 4-D BRDF.
Premise Selection for Theorem Proving by Deep Graph Embedding
We propose a deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture.
Ranked #1 on
Automated Theorem Proving
on HolStep (Unconditional)
Deep Metric Learning with Angular Loss
The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images.
Crowd Counting Considering Network Flow Constraints in Videos
This paper, for the first time, introduces a quadratic programming model with the network flow constraints to improve the accuracy of crowd counting.
Photometric Stereo With Small Angular Variations
This paper studies the effect of small angular variations in illumination directions to photometric stereo.
LiSens --- A Scalable Architecture for Video Compressive Sensing
The measurement rate of cameras that take spatially multiplexed measurements by using spatial light modulators (SLM) is often limited by the switching speed of the SLMs.
Cross-Modal Similarity Learning : A Low Rank Bilinear Formulation
A new approach to the problem has been raised which intends to match features of different modalities directly.
Recovery of Sparse Signals Using Multiple Orthogonal Least Squares
We study the problem of recovering sparse signals from compressed linear measurements.
