Search Results for author:
Found 360 papers, 132 papers with code
Exploration on Generating Traditional Chinese Medicine Prescription from Symptoms with an End-to-End method
Traditional Chinese Medicine (TCM) is an influential form of medical treatment in China and surrounding areas.
Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition
The layers between the source and target feature extractor are partially untied during the training stage to take both training efficiency and domain adaptation into consideration.
TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation
For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because of complex remote sensing content and optical conditions.
Generative Model for Heterogeneous Inference
However, the learning and inference of BN model are NP-hard thus the number of stochastic variables in BN is highly constrained.
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification
Most existing person re-identification (re-id) methods require supervised model learning from a separate large set of pairwise labelled training data for every single camera pair.
Ranked #22 on
Unsupervised Domain Adaptation
on Market to Duke
Automatic Translating between Ancient Chinese and Contemporary Chinese with Limited Aligned Corpora
In this paper, we propose to build an end-to-end neural model to automatically translate between ancient and contemporary Chinese.
Improving Word Vector with Prior Knowledge in Semantic Dictionary
Using low dimensional vector space to represent words has been very effective in many NLP tasks.
Deep Residual Text Detection Network for Scene Text
Scene text detection is a challenging problem in computer vision.
Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models
Traditional Chinese Medicine (TCM) has accumulated a big amount of precious resource in the long history of development.
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models
We develop streaming keyword spotting systems using a recurrent neural network transducer (RNN-T) model: an all-neural, end-to-end trained, sequence-to-sequence model which jointly learns acoustic and language model components.
Attribute Recognition by Joint Recurrent Learning of Context and Correlation
Recognising semantic pedestrian attributes in surveillance images is a challenging task for computer vision, particularly when the imaging quality is poor with complex background clutter and uncontrolled viewing conditions, and the number of labelled training data is small.
Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering
In this paper, we focus on the problem of answer triggering ad-dressed by Yang et al. (2015), which is a critical component for a real-world question answering system.
WebVision Database: Visual Learning and Understanding from Web Data
Our new WebVision database and relevant studies in this work would benefit the advance of learning state-of-the-art visual models with minimum supervision based on web data.
SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks
However, there is a limitation in current deep learning based ATR solution that each learning process only handle one SAR image, namely learning the static scattering information, while missing the space-varying information.
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone.
Ranked #103 on
Person Re-Identification
on Market-1501
WebVision Challenge: Visual Learning and Understanding With Web Data
The 2017 WebVision challenge consists of two tracks, the image classification task on WebVision test set, and the transfer learning task on PASCAL VOC 2012 dataset.
Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing
Action Unit (AU) detection becomes essential for facial analysis.
Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI
A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge.
EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection
For the enhancing layers, we designed an attention map based on facial landmark features and applied it to a pretrained neural network to conduct enhanced learning (The E-Net).
Fast color transfer from multiple images
Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different regions in the source image.
Recurrent Neural Network Language Model Adaptation Derived Document Vector
In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector.
Learning and Fusing Multimodal Features from and for Multi-task Facial Computing
We have found that features trained for one task can be used for other related tasks.
Tuning parameter calibration for $\ell_1$-regularized logistic regression
Feature selection is a standard approach to understanding and modeling high-dimensional classification data, but the corresponding statistical methods hinge on tuning parameters that are difficult to calibrate.
Turing learning: a metric-free approach to inferring behavior and its application to swarms
We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method.
A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset
In order to increase the expression recognition accuracy, we also fine-tune the CNN model and thus obtain a better CNN facial expression recognition model.
Facial Expression Recognition
Facial Expression Recognition (FER)
Recycle deep features for better object detection
Aiming at improving the performance of existing detection algorithms developed for different applications, we propose a region regression-based multi-stage class-agnostic detection pipeline, whereby the existing algorithms are employed for providing the initial detection proposals.
Towards an "In-the-Wild" Emotion Dataset Using a Game-based Framework
To evaluate the dataset, we compared the performance of two deep learning models trained on both GaMo and CIFE.
Bearing fault diagnosis based on spectrum images of vibration signals
Bearing fault diagnosis has been a challenge in the monitoring activities of rotating machinery, and it's receiving more and more attention.
Improved Image Deblurring based on Salient-region Segmentation
Image deblurring techniques play important roles in many image processing applications.
Macroblock Classification Method for Video Applications Involving Motions
We demonstrate that this low-computation-complexity method can efficiently catch the characteristics of the frame.
Real-time Decolorization using Dominant Colors
Decolorization is the process to convert a color image or video to its grayscale version, and it has received great attention in recent years.
A Novel Face Recognition Method using Nearest Line Projection
Instead of projecting an image to its nearest image, we try to project it to its nearest line spanned by two different face images.
Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference
Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference.
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks
To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference.
NMT-based Cross-lingual Document Embeddings
This paper further adds a distance constraint to the training objective function of NV so that the two embeddings of a parallel document are required to be as close as possible.
Cross-Lingual Document Classification
Document Classification
+5
Primal Meaning Recommendation via On-line Encyclopedia
We define the primal meaning of an expression to be a frequently used sense of that expression from which its other frequent senses can be deduced.
Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
In the experiments, we take a real-world sememe knowledge base HowNet and the corresponding descriptions of the words in Baidu Wiki for training and evaluation.
Adapting Blockchain Technology for Scientific Computing
Blockchain stores information into a chain of blocks, whose integrity is usually guaranteed by Proof of Work (PoW).
Cryptography and Security Distributed, Parallel, and Cluster Computing
Knowing Where to Look? Analysis on Attention of Visual Question Answering System
Attention mechanisms have been widely used in Visual Question Answering (VQA) solutions due to their capacity to model deep cross-domain interactions.
Learning to discover and localize visual objects with open vocabulary
To alleviate the cost of obtaining accurate bounding boxes for training today's state-of-the-art object detection models, recent weakly supervised detection work has proposed techniques to learn from image-level labels.
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling
Information selection is the most important component in document summarization task.
Ranked #32 on
Abstractive Text Summarization
on CNN / Daily Mail
Improving Neural Abstractive Document Summarization with Structural Regularization
Recent neural sequence-to-sequence models have shown significant progress on short text summarization.
Ranked #43 on
Abstractive Text Summarization
on CNN / Daily Mail
Derivation of Document Vectors from Adaptation of LSTM Language Model
In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector.
Abstractive News Summarization based on Event Semantic Link Network
This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation.
Exploring Differential Topic Models for Comparative Summarization of Scientific Papers
This paper investigates differential topic models (dTM) for summarizing the differences among document groups.
Improved Expressivity Through Dendritic Neural Networks
In this study, we model such local nonlinearity of dendritic trees with our dendritic neural network (DENN) structure and apply this structure to typical machine learning tasks.
Generalizing GANs: A Turing Perspective
In this paper we examine how these algorithms relate to the Turing test, and derive what - from a Turing perspective - can be considered their defining features.
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems
Furthermore, DeepBillboard is sufficiently robust and resilient for generating physical-world adversarial billboard tests for real-world driving under various weather conditions.
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features
To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy.
Locally Aligned Feature Transforms across Views
In this paper, we propose a new approach for matching images observed in different camera views with complex cross-view transforms and apply it to person reidentification.
DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification
In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter.
Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification
Particularly, long short-term memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs.
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios.
Deformable Tube Network for Action Detection in Videos
To perform action detection, we design a 3D convolution network with skip connections for tube classification and regression.
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks.
Ranked #52 on
Node Classification
on Cora
Recursive Graphical Neural Networks for Text Classification
Graph is a natural structure to describe the complicated relation between tokens.
A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting
Very short-term convective storm forecasting, termed nowcasting, has long been an important issue and has attracted substantial interest.
Multi-level Gated Recurrent Neural Network for Dialog Act Classification
In this paper we focus on the problem of dialog act (DA) labelling.
Dynamic Upsampling of Smoke through Dictionary-based Learning
We propose a novel dictionary-based neural network which learns both a fast evaluation of sparse patch encoding and a dictionary of corresponding coarse and fine patches from a sequence of example simulations computed with any numerical solver.
Graphics
High Temporal Resolution Rainfall Runoff Modelling Using Long-Short-Term-Memory (LSTM) Networks
Accurate and efficient models for rainfall runoff (RR) simulations are crucial for flood risk management.
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.
kDecay: Just adding k-decay items on Learning-Rate Schedule to improve Neural Networks
On the ImageNet, accuracy is improved by 1. 25\%.
Analytic connectivity of $k$-uniform hypergraphs
In this paper, we study the analytic connectivity of a $k$-uniform hypergraph $H$, denoted by $\alpha(H)$.
Combinatorics 05C65 (Primary), 05C40, 05B05, 26D15 (Secondary)
Improving Accent Conversion with Reference Encoder and End-To-End Text-To-Speech
Accent conversion (AC) transforms a non-native speaker's accent into a native accent while maintaining the speaker's voice timbre.
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively re-exploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss.
Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network
In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site.
PerMO: Perceiving More at Once from a Single Image for Autonomous Driving
We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving.
Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor
We then propose a structure-aware network for lane marker extraction in DVS images.
Transformer based Multilingual document Embedding model
One of the current state-of-the-art multilingual document embedding model LASER is based on the bidirectional LSTM neural machine translation model.
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi
Current computer vision tasks based on deep learning require a huge amount of data with annotations for model training or testing, especially in some dense estimation tasks, such as optical flow segmentation and depth estimation.
Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification
Transfer learning-based methods address this problem by pre-training in the source domain and fine-tuning on the target domain.
General Classification
Hyperspectral Image Classification
+1
Parallel Rescoring with Transformer for Streaming On-Device Speech Recognition
The 2nd-pass model plays a key role in the quality improvement of the end-to-end model to surpass the conventional model.
Towards Efficient Coarse-to-Fine Networks for Action and Gesture Recognition
State-of-the-art approaches to video-based action and gesture recognition often employ two key concepts: First, they employ multistream processing; second, they use an ensemble of convolutional networks.
Ranked #1 on
Action Classification
on Jester test
Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction
Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.
Hidden Markov Models for Pipeline Damage Detection Using Piezoelectric Transducers
However, the negative pressure waves or guided stress waves may not be easily detected with environmental interference, e. g., the oil and gas pipelines in offshore environment.
Rethinking Graph Neural Networks for Graph Coloring
To characterize the power of GNNs for the graph coloring problem, we first formalize the discrimination power of GNNs as the capability to assign nodes different colors.
Detection Booster Training: A detection booster training method for improving the accuracy of classifiers.
In this paper, we probe this direction by deriving a relationship between the estimation of unknown parameters of the probability density function (pdf) of input data and classification accuracy.
Investigation of Singing Voice Separation for Singing Voice Detection in Polyphonic Music
Singing voice detection (SVD), to recognize vocal parts in the song, is an essential task in music information retrieval (MIR).
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth
In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial odometry (VIO) system, which can provide accurate state estimates and dense depth maps of the immediate surroundings.
Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization
In order to remove this factor, we first develop gradient descent averaging (GDA), which is a general projection-based dual averaging algorithm in the strongly convex setting.
Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling
In the context of trade liberalisation and market harmonisation in the European markets, accurate price forecasting becomes difficult for electricity market participants to obtain because electricity forecasting requires the consideration of features from ever-growing coupling markets.
Correlated interaction effects in three-dimensional semi-Dirac semimetal
The physical essences of the quantum critical points are determined by analyzing the susceptibility exponents for all of the source terms in particle-hole and particle-particle channels.
Strongly Correlated Electrons Materials Science
Phase Diagram of Triangular Lattice Quantum Ising Model under External Field
Quantum Ising model on a triangular lattice hosts a finite temperature Berezinskii-Kosterlitz-Thouless (BKT) phase with emergent U(1) symmetry, and it will transit into an up-up-down (UUD) phase with $C_3$ symmetry breaking upon an infinitesimal external field along the longitudinal direction, but the overall phase diagram spanned by the axes of external field and temperature remains opaque due to the lack of systematic invesitgations with controlled methodologies.
Strongly Correlated Electrons Statistical Mechanics
LEAD: LiDAR Extender for Autonomous Driving
In this paper, we propose LEAD, i. e., LiDAR Extender for Autonomous Driving, to extend the MEMS LiDAR by coupled image w. r. t both FoV and range.
Significant Inverse Magnetocaloric Effect induced by Quantum Criticality
Strong fluctuations in the low-$T$ quantum critical regime can give rise to a large thermal entropy change and thus significant cooling effect when approaching the QCP.
Strongly Correlated Electrons
A Universal Urbach Rule for Disordered Organic Semiconductors
A simple model is presented that explains absorption line-shapes of disordered systems, and we also provide a strategy to determine the excitonic disorder energy.
Optics Disordered Systems and Neural Networks
Learning Word-Level Confidence For Subword End-to-End ASR
We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Modelling brain based on canonical ensemble with functional MRI: A thermodynamic exploration on neural system
(3) The detection of neural disease was demonstrated to be benefit from thermodynamic model, implying the immense potential of thermodynamics in auxiliary diagnosis.
Path-specific Underwater Acoustic Channel Tracking and its Application in Passive Time Reversal Mirror
With the path-specific parameters obtained by the proposed channel tracking, the proposed PTRM can not only match the time dispersion as conventional PTRM, but also the doubly-spread channel, since the path-specific delay and Doppler scaler factor can help to match the channel in both time and frequency domain.
Morpho-evolution with learning using a controller archive as an inheritance mechanism
This causes a potential mismatch between the structure of an inherited controller and the new body.
Dynamic Underwater Acoustic Channel Tracking for Correlated Rapidly Time-varying Channels
A model based on the underwater acoustic channel's correlation can be used as the state-space model in the Kalman filter to improve the underwater acoustic channel tracking compared that without a model.
Temporal optical neurons for serial deep learning
This concept represents a novel one-dimensional realization of artificial neural networks, enabling an efficient application of optical deep learning methods to the analysis and processing of serial data signals, while offering a new overall perspective for the temporal signal processing.
Discover the Hidden Attack Path in Multi-domain Cyberspace Based on Reinforcement Learning
To achieve these results, we pose discovering attack paths as a Reinforcement Learning (RL) problem and train an agent to discover multi-domain cyberspace attack paths.
MUSE: Multi-faceted Attention for Signed Network Embedding
Signed network embedding is an approach to learn low-dimensional representations of nodes in signed networks with both positive and negative links, which facilitates downstream tasks such as link prediction with general data mining frameworks.
BASS: Boosting Abstractive Summarization with Unified Semantic Graph
Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts.
Generative Adversarial Networks: A Survey Towards Private and Secure Applications
Generative Adversarial Networks (GAN) have promoted a variety of applications in computer vision, natural language processing, etc.
MST: Masked Self-Supervised Transformer for Visual Representation
More importantly, the masked tokens together with the remaining tokens are further recovered by a global image decoder, which preserves the spatial information of the image and is more friendly to the downstream dense prediction tasks.
Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models
We integrate this ranking scheme with two frequency models and a GPT-2 styled language model, along with the acceptance model to yield 27. 80% and 37. 64% increase in TOP1 and TOP5 accuracy, respectively.
Semi-TCL: Semi-Supervised Track Contrastive Representation Learning
We design a new instance-to-track matching objective to learn appearance embedding that compares a candidate detection to the embedding of the tracks persisted in the tracker.
Video 3D Sampling for Self-supervised Representation Learning
As a result, we can leverage the spatial information (the size of objects), temporal information (the direction and magnitude of motions) as our learning target.
Residual Attention Based Network for Automatic Classification of Phonation Modes
Phonation mode is an essential characteristic of singing style as well as an important expression of performance.
A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection
The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years.
Multi defect detection and analysis of electron microscopy images with deep learning
Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis.
ASAT: Adaptively Scaled Adversarial Training in Time Series
Besides the security concerns of potential adversarial examples, adversarial training can also improve the generalization ability of neural networks, train robust neural networks, and provide interpretability for neural networks.
Musical Tempo Estimation Using a Multi-scale Network
Recently, some single-step systems without onset detection have shown their effectiveness in automatic musical tempo estimation.
UoB\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.
This paper describes our submission to SemEval 2021 Task 2.
Tied & Reduced RNN-T Decoder
Previous works on the Recurrent Neural Network-Transducer (RNN-T) models have shown that, under some conditions, it is possible to simplify its prediction network with little or no loss in recognition accuracy (arXiv:2003. 07705 [eess. AS], [2], arXiv:2012. 06749 [cs. CL]).
A Simple Feature Augmentation for Domain Generalization
The topical domain generalization (DG) problem asks trained models to perform well on an unseen target domain with different data statistics from the source training domains.
MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection
Moreover, we make a key observation that subtle forgery artifacts can be further exposed in the patch-wise phase and amplitude spectrum and exhibit different clues.
Referring Self-supervised Learning on 3D Point Cloud
In this paper, we study a new problem named Referring Self-supervised Learning (RSL) on 3D scene understanding: Given the 3D synthetic models with labels and the unlabeled 3D real scene scans, our goal is to distinguish the identical semantic objects on an unseen scene according to the referring synthetic 3D models.
Learning UI Navigation through Demonstrations composed of Macro Actions
The action space is restricted to the UI elements plus a few global actions.
Deep Learning for UAV-based Object Detection and Tracking: A Survey
Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS).
基于统一模型的藏文新闻摘要(Abstractive Summarization of Tibetan News Based on Hybrid Model)
Seq2seq神经网络模型在中英文文本摘要的研究中取得了良好的效果, 但在低资源语言的文本摘要研究还处于探索阶段, 尤其是在藏语中。此外, 目前还没有大规模的标注语料库进行摘要提取。本文提出了一种生成藏文新闻摘要的统一模型。利用TextRank算法解决了藏语标注训练数据不足的问题。然后, 采用两层双GRU神经网络提取代表原始新闻的句子, 减少冗余信息。最后, 使用基于注意力机制的Seq2Seq来生成理解式摘要。同时, 我们加入了指针网络来处理未登录词的问题。实验结果表明, ROUGE-1评分比传统模型提高了2%。 关键词:文本摘要;藏文;TextRank; 指针网络;Bi-GRU
Neural Operator Search
We address this limitation by additionally exploiting feature self-calibration operations, resulting in a heterogeneous search space.
Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
Variational Autoencoder with CCA for Audio-Visual Cross-Modal Retrieval
On the one hand, audio encoder and visual encoder separately encode audio data and visual data into two different latent spaces.
Transfer learning of phase transitions in percolation and directed percolation
With the DANN, only a small fraction of input configurations (2d images) needs to be labeled, which is automatically chosen, in order to capture the critical point.
An Intelligent Self-driving Truck System For Highway Transportation
Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry.
Generative adversarial network for super-resolution imaging through a fiber
A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes.
Evolutionary Action Selection for Gradient-based Policy Learning
Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL) have recently been integrated to take the advantage of the both methods for better exploration and exploitation. The evolutionary part in these hybrid methods maintains a population of policy networks. However, existing methods focus on optimizing the parameters of policy network, which is usually high-dimensional and tricky for EA. In this paper, we shift the target of evolution from high-dimensional parameter space to low-dimensional action space. We propose Evolutionary Action Selection-Twin Delayed Deep Deterministic Policy Gradient (EAS-TD3), a novel hybrid method of EA and DRL. In EAS, we focus on optimizing the action chosen by the policy network and attempt to obtain high-quality actions to promote policy learning through an evolutionary algorithm.
DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images
Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation.
A Joint Morphological Profiles and Patch Tensor Change Detection for Hyperspectral Imagery
Multi-temporal hyperspectral images can be used to detect changed information, which has gradually attracted researchers' attention.
Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information
Moreover, we utilize multi-source information (e. g., MFCC and deep features) to further improve the scoring system performance.
Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
In this survey, we provide a systematic overview of the research progress on the faithfulness problem of NLG, including problem analysis, evaluation metrics and optimization methods.
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2. 5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.
Efficient universal shuffle attack for visual object tracking
Recently, adversarial attacks have been applied in visual object tracking to deceive deep trackers by injecting imperceptible perturbations into video frames.
Towards 3D Scene Understanding by Referring Synthetic Models
Promising performance has been achieved for visual perception on the point cloud.
FindIt: Generalized Localization with Natural Language Queries
We propose FindIt, a simple and versatile framework that unifies a variety of visual grounding and localization tasks including referring expression comprehension, text-based localization, and object detection.
MeMOT: Multi-Object Tracking with Memory
We propose an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span.
MS-HLMO: Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration
Considering the characteristics and differences of multi-source remote sensing images, a feature-based registration algorithm named Multi-scale Histogram of Local Main Orientation (MS-HLMO) is proposed.
SEAL: A Large-scale Video Dataset of Multi-grained Spatio-temporally Action Localization
SEAL consists of two kinds of annotations, SEAL Tubes and SEAL Clips.
Faster-TAD: Towards Temporal Action Detection with Proposal Generation and Classification in a Unified Network
In this paper, we propose a unified network for TAD, termed Faster-TAD, by re-purposing a Faster-RCNN like architecture.
A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification
The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing.
Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention
Although few-shot learning has attracted much attention from the fields of image and audio classification, few efforts have been made on few-shot speaker identification.
Answer-Me: Multi-Task Open-Vocabulary Visual Question Answering
We present Answer-Me, a task-aware multi-task framework which unifies a variety of question answering tasks, such as, visual question answering, visual entailment, visual reasoning.
$(O,G)$-granular variable precision fuzzy rough sets based on overlap and grouping functions
In this paper, the depiction of $(O, G)$-granular variable precision fuzzy rough sets ($(O, G)$-GVPFRSs for short) is first given based on overlap and grouping functions.
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation
The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy.
Some neighborhood-related fuzzy covering-based rough set models and their applications for decision making
In order to further generalize the FRS theory to more complicated data environments, we firstly propose four types of fuzzy neighborhood operators based on fuzzy covering by overlap functions and their implicators in this paper.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Benchmarking Unsupervised Anomaly Detection and Localization
From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation.
Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-Objective Optimization?
Constraint violation has been a building block to design evolutionary multi-objective optimization algorithms for solving constrained multi-objective optimization problems.
PPDL: Predicate Probability Distribution Based Loss for Unbiased Scene Graph Generation
However, the application value of SG on downstream tasks is severely limited by the predicate classification bias, which is caused by long-tailed data and presented as semantic bias of predicted relation predicates.
MPANet: Multi-Patch Attention For Infrared Small Target object Detection
To this end, a multi-patch attention network (MPANet) based on the axial-attention encoder and the multi-scale patch branch (MSPB) structure is proposed.
SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes
To further leverage these two paradigms, we propose a selective and joint HDR and denoising (SJ-HD$^2$R) imaging framework, utilizing scenario-specific priors to conduct the path selection with an accuracy of more than 93. 3$\%$.
On three types of $L$-fuzzy $β$-covering-based rough sets
In this paper, we mainly construct three types of $L$-fuzzy $\beta$-covering-based rough set models and study the axiom sets, matrix representations and interdependency of these three pairs of $L$-fuzzy $\beta$-covering-based rough approximation operators.
Positive-Negative Equal Contrastive Loss for Semantic Segmentation
The contextual information is critical for various computer vision tasks, previous works commonly design plug-and-play modules and structural losses to effectively extract and aggregate the global context.
Rethinking Graph Neural Networks for the Graph Coloring Problem
Graph coloring, a classical and critical NP-hard problem, is the problem of assigning connected nodes as different colors as possible.
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification
Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing attention.
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification
Text information including extensive prior knowledge about land cover classes has been ignored in hyperspectral image classification (HSI) tasks.
LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images
Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of stacked separable convolutions, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity.
Playing Technique Detection by Fusing Note Onset Information in Guzheng Performance
Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions.
Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learning
Our key insight is that dynamic systems with different parameters provide different levels of difficulty for the policy, and the difficulty of behaving well in a system is constantly changing due to the evolution of the policy.
Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation
While Reinforcement Learning can achieve impressive results for complex tasks, the learned policies are generally prone to fail in downstream tasks with even minor model mismatch or unexpected perturbations.
《二十四史》古代汉语语义依存图库构建(Construction of Semantic Dependency Graph Bank of Ancient Chinese in twenty four histories)
“语义依存图是NLP处理语义的深层分析方法, 能够对句子中词与词之间的语义进行分析。该文针对古代汉语特点, 在制定古代汉语语义依存图标注规范的基础上, 以《二十四史》为语料来源, 完成标注了规模为3000句的古代汉语语义依存图库, 标注一致性的kappa值为78. 83%。通过与现代汉语语义依存图库的对比, 对依存图库基本情况进行统计, 分析古代汉语的语义特色和规律。统计显示, 古代汉语语义分布宏观上符合齐普夫定律, 在语义事件描述上具有强烈的历史性叙事和正式文体特征, 如以人物纪传为中心, 时间、地点等周边角色描述细致, 叙事语言冷静客观, 缺少描述情态、语气、程度、时间状态等的修饰词语等。 "
基于强化学习的古今汉语句子对齐研究(Research on Sentence Alignment of Ancient and Modern Chinese based on Reinforcement Learning)
“基于深度学习的有监督机器翻译取得了良好的效果, 但训练过程中需要大量质量较高的对齐语料。对于中文古今翻译场景, 高质量的平行语料并不多, 而粗对齐的篇章、段语料比较容易获得, 因此语料对齐很有研究价值和研究必要。在传统双语平行语料的句子对齐研究中, 传统方法根据双语文本中的长度、词汇、共现文字等语法信息, 建立一个综合评判标准来衡量两个句对之间相似度。此类方法虽然在单句对齐上取得了较好的效果, 但是对于句子语义匹配的能力有限, 并且在一些多对多的对齐模式上的性能表现不佳。在本文中我们提出尝试利用现在发展迅速且具有强大语义表示能力的预训练语言模型来考虑双语的语义信息, 但是单独使用预训练语言模型只能考虑相对局部的信息, 因此我们提出采用基于动态规划算法的强化学习训练目标来整合段落全局信息, 并且进行无监督训练。实验结果证明我们提出的方法训练得到的模型性能优于此前获得最好表现的基线模型, 尤其相较于传统模型难以处理的多对多对齐模式下, 性能提升较大。”
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases
Complex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints.
Misaligned orientations of 4f optical neural network for image classification accuracy on various datasets
In order to comprehensively investigate the influence caused by the misalignment, we proposed a method for estimating the performance of a 4f-ONN in response to various misalignment in the context of the image classification task. The misalignment in numerical simulation is estimated by manipulating the optical intensity distributions in the fourth focus plane in the 4f system.
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning
This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps.
Zero-shot point cloud segmentation by transferring geometric primitives
To this end, we propose a novel framework to learn the geometric primitives shared in seen and unseen categories' objects and employ a fine-grained alignment between language and the learned geometric primitives.
Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control
Multiple predictive path points are dynamically generated by a deep Markov model optimized using RL approach for robot to track.
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied.
Jet tagging algorithm of graph network with HaarPooling message passing
In this paper, we introduce an approach of GNNs combined with a HaarPooling operation to analyze the events, called HaarPooling Message Passing neural network (HMPNet).
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance
Diffusion generative models have recently greatly improved the power of text-conditioned image generation.
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
We first measure a model's factual robustness by its success rate to defend against adversarial attacks when generating factual information.
A Data-driven Case-based Reasoning in Bankruptcy Prediction
In addition, we delicately examine the explainability of the CBR system in the decision-making process of bankruptcy prediction.
Transformation-Equivariant 3D Object Detection for Autonomous Driving
However, it is difficult to apply such networks to 3D object detection in autonomous driving due to its large computation cost and slow reasoning speed.
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working.
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution
Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation.
Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification
This framework comprises two main parts: (i) a leakage-free balanced sampling strategy, and (ii) a modified end-to-end fully convolutional network (FCN) architecture that optimizes the trade-off between accuracy and efficiency.
Hyperspectral Image Classification
Vocal Bursts Intensity Prediction
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network
The results show that AIIR-MIX can dynamically assign each agent a real-time intrinsic reward in accordance with their actual contribution.
An ASR-free Fluency Scoring Approach with Self-Supervised Learning
A typical fluency scoring system generally relies on an automatic speech recognition (ASR) system to obtain time stamps in input speech for either the subsequent calculation of fluency-related features or directly modeling speech fluency with an end-to-end approach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Leveraging phone-level linguistic-acoustic similarity for utterance-level pronunciation scoring
Recent studies on pronunciation scoring have explored the effect of introducing phone embeddings as reference pronunciation, but mostly in an implicit manner, i. e., addition or concatenation of reference phone embedding and actual pronunciation of the target phone as the phone-level pronunciation quality representation.
An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images
An object-level contrastive learning (OCL) strategy is employed in the object classification sub-network featuring a siamese network to realize the global features extraction, and a sub-object-level contrastive learning (SOCL) paradigm is designed in the semantic segmentation sub-network to efficiently extract salient features from boundaries of landslides.
Efficient Masked Autoencoders with Self-Consistency
However, its high random mask ratio would result in two serious problems: 1) the data are not efficiently exploited, which brings inefficient pre-training (\eg, 1600 epochs for MAE $vs.$ 300 epochs for the supervised), and 2) the high uncertainty and inconsistency of the pre-trained model, \ie, the prediction of the same patch may be inconsistent under different mask rounds.
Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation
In our method, taking the power of large-scale pre-trained multi-modal CLIP and neural rendering, T2P searches both continuous facial parameters and discrete facial parameters in a unified framework.
SeqCo-DETR: Sequence Consistency Training for Self-Supervised Object Detection with Transformers
To this end, we propose SeqCo-DETR, a novel Sequence Consistency-based self-supervised method for object DEtection with TRansformers.
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks.
Ranked #1 on
Video Captioning
on MSVD
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers
Moreover, several attentions are learned by different heads, and each head of the MHSA layer encodes the semantic context-aware representation to obtain discriminative features.
Mlinear: Rethink the Linear Model for Time-series Forecasting
In this paper, we carefully examine the opposing properties of CI and CD, and raise a practical question that has not been effectively answered, e. g.,"How to effectively mix the CI and CD properties of time series to achieve better predictive performance?"
Phonetic and Prosody-aware Self-supervised Learning Approach for Non-native Fluency Scoring
Specifically, we first pre-train the model using a reconstruction loss function, by masking phones and their durations jointly on a large amount of unlabeled speech and text prompts.
Pulling Target to Source: A New Perspective on Domain Adaptive Semantic Segmentation
To this end, we propose T2S-DA, which we interpret as a form of pulling Target to Source for Domain Adaptation, encouraging the model in learning similar cross-domain features.
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving
In this work, we propose a new multi-modal visual grounding task, termed LiDAR Grounding.
On the Value of Myopic Behavior in Policy Reuse
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
NeMO: Neural Map Growing System for Spatiotemporal Fusion in Bird's-Eye-View and BDD-Map Benchmark
We also provide a new scene-level BEV map evaluation setting along with the corresponding baseline for a more comprehensive comparison.
Hyper-pixel-wise Contrastive Learning Augmented Segmentation Network for Old Landslide Detection through Fusing High-Resolution Remote Sensing Images and Digital Elevation Model Data
To extract accurate semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from boundaries of landslides through HPCL-Net and fuses heterogeneous infromation in the semantic space from high-resolution remote sensing images and digital elevation model data.
Improving Generalization in Visual Reinforcement Learning via Conflict-aware Gradient Agreement Augmentation
In particular, CG2A develops a Gradient Agreement Solver to adaptively balance the varying gradient magnitudes, and introduces a Soft Gradient Surgery strategy to alleviate the gradient conflicts.
A Hybrid CNN-Transformer Architecture with Frequency Domain Contrastive Learning for Image Deraining
Image deraining is a challenging task that involves restoring degraded images affected by rain streaks.
A Self-supervised SAR Image Despeckling Strategy Based on Parameter-sharing Convolutional Neural Networks
The training image pairs are generated by the sub-sampler from real-word SAR image to estimate the noise distribution.
Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment
Anomaly segmentation plays a pivotal role in identifying atypical objects in images, crucial for hazard detection in autonomous driving systems.
WikiMT++ Dataset Card
WikiMT++ is an expanded and refined version of WikiMusicText (WikiMT), featuring 1010 curated lead sheets in ABC notation.
MiChao-HuaFen 1.0: A Specialized Pre-trained Corpus Dataset for Domain-specific Large Models
With the advancement of deep learning technologies, general-purpose large models such as GPT-4 have demonstrated exceptional capabilities across various domains.
Connecting Speech Encoder and Large Language Model for ASR
Q-Former-based LLMs can generalise well to out-of-domain datasets, where 12% relative WER reductions over the Whisper baseline ASR model were achieved on the Eval2000 test set without using any in-domain training data from Switchboard.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
IFT: Image Fusion Transformer for Ghost-free High Dynamic Range Imaging
By matching similar patches between frames, objects with large motion ranges in dynamic scenes can be aligned, which can effectively alleviate the generation of artifacts.
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training
In this paper, we propose Model2Scene, a novel paradigm that learns free 3D scene representation from Computer-Aided Design (CAD) models and languages.
A Holistic Evaluation of Piano Sound Quality
Unlike previous studies that focused on the effect of piano performance techniques on sound quality, this study evaluates the inherent sound quality of different pianos.
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks
Federated learning (FL) is a widely employed distributed paradigm for collaboratively training machine learning models from multiple clients without sharing local data.
An invariant feature extraction for multi-modal images matching
This paper aims at providing an effective multi-modal images invariant feature extraction and matching algorithm for the application of multi-source data analysis.
