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Found 81 papers, 15 papers with code
Gradations of Error Severity in Automatic Image Descriptions
Because our manipulated descriptions form minimal pairs with the reference descriptions, we are able to assess the impact of different kinds of errors on the perceived quality of the descriptions.
标签先验知识增强的方面类别情感分析方法研究(Aspect-Category based Sentiment Analysis Enhanced by Label Prior Knowledge)
“当前, 基于方面类别的情感分析研究旨在将方面类别检测和面向类别的情感分类两个任务协同进行。然而, 现有研究未能有效关注情感数据集中存在的噪声标签, 影响了情感分析的质量。基于此, 本文提出一种标签先验知识增强的方面类别情感分析方法(AP-LPK)。首先本文为面向类别的情感分类构建了自回归提示训练方式, 可以激发预训练语言模型的潜力。同时该方式通过自回归生成标签词, 以期获得比非自回归更好的语义一致性。其次, 每个类别的标签分布作为标签先验知识引入, 并通过伯努利分布对其进行进一步精炼, 以用于减轻噪声标签的干扰。然后, AP-LPK将上述两个步骤分别得到的情感类别分布进行融合, 以获得最终的情感类别预测概率。最后, 本文提出的AP-LPK方法在五个数据集上进行评估, 包括SemEval 2015和2016的四个基准数据集和AI Challenger 2018的餐厅领域大规模数据集。实验结果表明, 本文提出的方法在F1指标上优于现有方法。”
Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches
This paper presents our work in long and short form choice, a significant question of lexical choice, which plays an important role in many Natural Language Understanding tasks.
Data Augmentation Alone Can Improve Adversarial Training
Data augmentation, which is effective at preventing overfitting in standard training, has been observed by many previous works to be ineffective in mitigating overfitting in adversarial training.
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
The adoption of artificial intelligence (AI) in healthcare is growing rapidly.
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization
Adversarial training is widely used to improve the robustness of deep neural networks to adversarial attack.
Graph Contrastive Learning for Materials
By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks.
Towards A Unified Conformer Structure: from ASR to ASV Task
Transformer has achieved extraordinary performance in Natural Language Processing and Computer Vision tasks thanks to its powerful self-attention mechanism, and its variant Conformer has become a state-of-the-art architecture in the field of Automatic Speech Recognition (ASR).
Antibody Representation Learning for Drug Discovery
Therapeutic antibody development has become an increasingly popular approach for drug development.
Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting
This paper describes a spatial-aware speaker diarization system for the multi-channel multi-party meeting.
Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation
To this end, we propose a novel model-agnostic Label Semantic Knowledge Distillation (LS-KD) for unbiased SGG.
NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation
To this end, we propose a novel NoIsy label CorrEction and Sample Training strategy for SGG: NICEST.
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i. e. the synthesis of programming language solutions given a natural language problem description.
Learning to Prove Trigonometric Identities
We define the normalized form of trigonometric identities, design a set of rules for the proof and put forward a method which can generate theoretically infinite trigonometric identities.
Hiding Your Signals: A Security Analysis of PPG-based Biometric Authentication
It reduces the success rate of rPPG spoofing attacks in user authentication to 0. 05.
Trichomonas Vaginalis Segmentation in Microscope Images
Trichomoniasis is a common infectious disease with high incidence caused by the parasite Trichomonas vaginalis, increasing the risk of getting HIV in humans if left untreated.
Towards Real-Time Visual Tracking with Graded Color-names Features
MeanShift algorithm has been widely used in tracking tasks because of its simplicity and efficiency.
The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation
Then, in Pos-NSD, we use a clustering-based algorithm to divide all positive samples into multiple sets, and treat the samples in the noisiest set as noisy positive samples.
Deep Representation Decomposition for Rate-Invariant Speaker Verification
While promising performance for speaker verification has been achieved by deep speaker embeddings, the advantage would reduce in the case of speaking-style variability.
MealRec: A Meal Recommendation Dataset
A publicly available dataset specialising in meal recommendation research for the research community is in urgent demand.
Graph Convolutional Network Based Semi-Supervised Learning on Multi-Speaker Meeting Data
In this work, we present a GCN-based approach for semi-supervised learning.
Improving Cross-lingual Speech Synthesis with Triplet Training Scheme
Recent advances in cross-lingual text-to-speech (TTS) made it possible to synthesize speech in a language foreign to a monolingual speaker.
What is Next when Sequential Prediction Meets Implicitly Hard Interaction?
The experiences in the form of the unlikelihood of correct responses are drawn from each other by MED, which provides mutual exclusivity knowledge to figure out implicitly hard interactions.
Dual Space Graph Contrastive Learning
In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
Scalable Geometric Deep Learning on Molecular Graphs
Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing.
Adder Attention for Vision Transformer
With the new operation, vision transformers constructed using additions can also provide powerful feature representations.
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.
Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering
This paper introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineering in data preprocessing from training the text-image joint embedding model.
Visual-aware Attention Dual-stream Decoder for Video Captioning
Video captioning is a challenging task that captures different visual parts and describes them in sentences, for it requires visual and linguistic coherence.
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception
The existing MOTS studies face two critical challenges: 1) the published datasets inadequately capture the real-world complexity for network training to address various driving settings; 2) the working pipeline annotation tool is under-studied in the literature to improve the quality of MOTS learning examples.
An Automated Portfolio Trading System with Feature Preprocessing and Recurrent Reinforcement Learning
We propose a novel portfolio trading system, which contains a feature preprocessing module and a trading module.
Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning
Financial trading aims to build profitable strategies to make wise investment decisions in the financial market.
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks.
Generate & Rank: A Multi-task Framework for Math Word Problems
Math word problem (MWP) is a challenging and critical task in natural language processing.
Ranked #1 on
Math Word Problem Solving
on Math23K
XMUSPEECH System for VoxCeleb Speaker Recognition Challenge 2021
This paper describes the XMUSPEECH speaker recognition and diarisation systems for the VoxCeleb Speaker Recognition Challenge 2021.
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation
Deep neural networks have demonstrated remarkable performance in many data-driven and prediction-oriented applications, and sometimes even perform better than humans.
Approximation Properties of Deep ReLU CNNs
This paper focuses on establishing $L^2$ approximation properties for deep ReLU convolutional neural networks (CNNs) in two-dimensional space.
A Novel Dataset for Keypoint Detection of quadruped Animals from Images
In this paper, we studied the problem of localizing a generic set of keypoints across multiple quadruped or four-legged animal species from images.
DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants
Named entity recognition (NER) is usually developed and tested on text from well-written sources.
Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images
This paper presents a three-tier modality alignment approach to learning text-image joint embedding, coined as JEMA, for cross-modal retrieval of cooking recipes and food images.
Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
We present a Multi-modal Semantics enhanced Joint Embedding approach (MSJE) for learning a common feature space between the two modalities (text and image), with the ultimate goal of providing high-performance cross-modal retrieval services.
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning
This paper introduces a two-phase deep feature calibration framework for efficient learning of semantics enhanced text-image cross-modal joint embedding, which clearly separates the deep feature calibration in data preprocessing from training the joint embedding model.
OLR 2021 Challenge: Datasets, Rules and Baselines
In addition to the Language Identification (LID) tasks, multilingual Automatic Speech Recognition (ASR) tasks are introduced to OLR 2021 Challenge for the first time.
Oriental Language Recognition (OLR) 2020: Summary and Analysis
The fifth Oriental Language Recognition (OLR) Challenge focuses on language recognition in a variety of complex environments to promote its development.
SSC: Semantic Scan Context for Large-Scale Place Recognition
We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance.
Ranked #1 on
Visual Place Recognition
on KITTI
An Integrated Framework for Two-pass Personalized Voice Trigger
For the SV system, we proposed a multi-task learning network, where phonetic branch is trained with the character label of the utterance, and speaker branch is trained with the label of the speaker.
Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification
This paper proposes a multi-task learning network with phoneme-aware and channel-wise attentive learning strategies for text-dependent Speaker Verification (SV).
SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure
LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue.
DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction
In this paper, we propose a Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the voucher redemption rate prediction.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.
Evaluation of Transportation Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness and Sustainability
ransportation systems have revolutionized the form of society.
ReLU Deep Neural Networks from the Hierarchical Basis Perspective
We study ReLU deep neural networks (DNNs) by investigating their connections with the hierarchical basis method in finite element methods.
Compacting Deep Neural Networks for Internet of Things: Methods and Applications
Deep Neural Networks (DNNs) have shown great success in completing complex tasks.
Measurement methods of radial flow in relativistic heavy-ion collisions
Radial flow can be directly extracted from the azimuthal distribution of mean transverse rapidity.
Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment
CFNS Ad-Hoc meeting on Radiative Corrections Whitepaper
Current precision scattering experiments and even more so many experiments planed for the Electron Ion Collider will be limited by systematics.
Nuclear Theory
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information
Our second method refines word representations by aligning original and re-fined embedding spaces based on local tangent space instead of performing weighted locally linear combination twice.
Conversational Semantic Parsing for Dialog State Tracking
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog.
Direct Signal Separation Via Extraction of Local Frequencies with Adaptive Time-Varying Parameters
In this paper, we propose an adaptive signal separation operation (ASSO) for effective and accurate separation of a single-channel blind-source multi-component signal, via introducing a time-varying parameter that adapts locally to IFs and using linear chirp (linear frequency modulation) signals to approximate components at each time instant.
A Chirplet Transform-based Mode Retrieval Method for Multicomponent Signals with Crossover Instantaneous Frequencies
We use the chirplet transform (CT) to represent a multicomponent signal in the three-dimensional space of time, frequency and chirp rate and introduce a CT-based signal separation scheme (CT3S) to retrieve modes.
Analysis of Adaptive Synchrosqueezing Transform with a Time-varying Parameter
The WSST with a time-varying parameter, called the adaptive WSST, was introduced very recently in the paper "Adaptive synchrosqueezing transform with a time-varying parameter for non-stationary signal separation".
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks.
Ranked #17 on
Few-Shot Image Classification
on FC100 5-way (5-shot)
AP20-OLR Challenge: Three Tasks and Their Baselines
Based on Kaldi and Pytorch, recipes for i-vector and x-vector systems are also conducted as baselines for the three tasks.
Noise Robust Named Entity Understanding for Voice Assistants
Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries.
Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts
Aiming at the issue, we propose a sentiment analysis and key entity detection approach based on BERT, which is applied in online financial text mining and public opinion analysis in social media.
Deep Visual Waterline Detection within Inland Marine Environment
This paper attempts to find a solution to guarantee the effectiveness of waterline detection for inland maritime applications with general digital camera sensor.
When is ACL's Deadline? A Scientific Conversational Agent
Our conversational agent UKP-ATHENA assists NLP researchers in finding and exploring scientific literature, identifying relevant authors, planning or post-processing conference visits, and preparing paper submissions using a unified interface based on natural language inputs and responses.
Choosing between Long and Short Word Forms in Mandarin
Between 80{\%} and 90{\%} of all Chinese words have long and short form such as 老虎/虎 (lao-hu/hu , tiger) (Duanmu:2013).
Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant
Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands.
Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation
To tackle the issue of preference aggregation for group recommendation, we propose a novel attentive aggregation representation learning method based on sociological theory for group recommendation, namely SIAGR (short for "Social Influence-based Attentive Group Recommendation"), which takes attention mechanisms and the popular method (BERT) as the aggregation representation for group profile modeling.
An Item Recommendation Approach by Fusing Images based on Neural Networks
There are rich formats of information in the network, such as rating, text, image, and so on, which represent different aspects of user preferences.
Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval
The Q&A community has become an important way for people to access knowledge and information from the Internet.
Deep neural network-based classification model for Sentiment Analysis
Based on the Bi-LSTM model, the classification model of word-level attention mechanism is studied.
Using Context Information to Enhance Simple Question Answering
With the rapid development of knowledge bases(KBs), question answering(QA)based on KBs has become a hot research issue.
The combination of context information to enhance simple question answering
With the rapid development of knowledge base, question answering based on knowledge base has been a hot research issue.
GPU based Parallel Optimization for Real Time Panoramic Video Stitching
In order to solve these problems, we propose a real-time panoramic video stitching framework. The framework we propose mainly consists of three algorithms, LORB image feature extraction algorithm, feature point matching algorithm based on LSH and GPU parallel video stitching algorithm based on CUDA. The experiment results show that the algorithm mentioned can improve the performance in the stages of feature extraction of images stitching and matching, the running speed of which is 11 times than that of the traditional ORB algorithm and 639 times than that of the traditional SIFT algorithm.
Image-to-Video Person Re-Identification by Reusing Cross-modal Embeddings
In this paper, we propose an end-to-end neural network framework for image-to-video person reidentification by leveraging cross-modal embeddings learned from extra information. Concretely speaking, cross-modal embeddings from image captioning and video captioning models are reused to help learned features be projected into a coordinated space, where similarity can be directly computed.
Relational Network for Skeleton-Based Action Recognition
We introduce the Recurrent Relational Network to learn the spatial features in a single skeleton, followed by a multi-layer LSTM to learn the temporal features in the skeleton sequences.
Ranked #74 on
Skeleton Based Action Recognition
on NTU RGB+D
Modeling Group Dynamics Using Probabilistic Tensor Decompositions
We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups, using data collected from multiple information sources.
Saliency Detection with Spaces of Background-based Distribution
In this letter, an effective image saliency detection method is proposed by constructing some novel spaces to model the background and redefine the distance of the salient patches away from the background.
Sensing Subjective Well-being from Social Media
Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media.
