Search Results for author:
Found 73 papers, 29 papers with code
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
We interact with the world with our hands and see it through our own (egocentric) perspective.
Modeling Unified Semantic Discourse Structure for High-quality Headline Generation
To consolidate the efficacy of S3 graphs, we further devise a hierarchical structure pruning mechanism to dynamically screen the redundant and nonessential nodes within the graph.
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Server
LI integrates a loop topology with layer-wise and end-to-end training, compatible with various neural network models.
CMNER: A Chinese Multimodal NER Dataset based on Social Media
Multimodal Named Entity Recognition (MNER) is a pivotal task designed to extract named entities from text with the support of pertinent images.
Reverse Multi-Choice Dialogue Commonsense Inference with Graph-of-Thought
With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions.
Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach
Our experiments show that this data augmentation approach significantly improves the compositional generalization capabilities of classification models on our benchmarks, with both generation models surpassing other text generation baselines.
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition
On the other hand, during the feature fusion stage, we propose a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism (CRM) for multimodal and context integration, respectively.
Ranked #5 on
Emotion Recognition in Conversation
on IEMOCAP
DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs
Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference.
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition
The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously.
ECQED: Emotion-Cause Quadruple Extraction in Dialogs
The existing emotion-cause pair extraction (ECPE) task, unfortunately, ignores extracting the emotion type and cause type, while these fine-grained meta-information can be practically useful in real-world applications, i. e., chat robots and empathic dialog generation.
TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition
Few-shot named entity recognition (NER) exploits limited annotated instances to identify named mentions.
Revisiting Conversation Discourse for Dialogue Disentanglement
Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute.
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing
Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval.
Ranked #2 on
Human Judgment Correlation
on Flickr8k-Expert
Reasoning Implicit Sentiment with Chain-of-Thought Prompting
While sentiment analysis systems try to determine the sentiment polarities of given targets based on the key opinion expressions in input texts, in implicit sentiment analysis (ISA) the opinion cues come in an implicit and obscure manner.
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment.
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM.
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking
In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing.
Multi-Frequency Representation Enhancement with Privilege Information for Video Super-Resolution
CNN's limited receptive field restricts its ability to capture long-range spatial-temporal dependencies, leading to unsatisfactory performance in video super-resolution.
DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags
Concretely, we construct tag representations and embed them into TREM, so that TREM can treat tag and word representations as queries/keys/values and utilize self-attention to model their relationships.
Entity-centered Cross-document Relation Extraction
First, we focus on input construction for our RE model and propose an entity-based document-context filter to retain useful information in the given documents by using the bridge entities in the text paths.
Learning to screen Glaucoma like the ophthalmologists
GAMMA Challenge is organized to encourage the AI models to screen the glaucoma from a combination of 2D fundus image and 3D optical coherence tomography volume, like the ophthalmologists.
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction
We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature.
OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction
Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text.
Multi-Scale Multi-Target Domain Adaptation for Angle Closure Classification
Based on these domain-invariant features at different scales, the deep model trained on the source domain is able to classify angle closure on multiple target domains even without any annotations in these domains.
Global inference with explicit syntactic and discourse structures for dialogue-level relation extraction
In our global reasoning framework, D2G and ARG work collaboratively, iteratively performing lexical, syntactic and semantic information exchange and representation learning over the entire dialogue context.
Ranked #3 on
Dialog Relation Extraction
on DialogRE
Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment Analysis
So far, aspect-based sentiment analysis (ABSA) has involved with total seven subtasks, in which, however the interactions among them have been left unexplored sufficiently.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Dataset and Evaluation algorithm design for GOALS Challenge
Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma. OCT imaging modality is an essential technique for assessing glaucomatous damage since it aids in quantifying fundus structures.
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis
The essential label set consists of the basic labels for this task, which are relatively balanced and applied in the prediction layer.
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2).
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery
Therefore, we conjecture that the multimodal and local-global combination strategies can be treated as the guideline of multi-task SSL for drug discovery.
Relative velocity-based reward functions for crowd navigation of robots
In order to increase the appli-cable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc.
Unified Named Entity Recognition as Word-Word Relation Classification
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka.
Ranked #2 on
Chinese Named Entity Recognition
on OntoNotes 4
A Survey of Fish Tracking Techniques Based on Computer Vision
Fish tracking is a key technology for obtaining movement trajectories and identifying abnormal behavior.
Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling
Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text.
Ranked #1 on
Fine-Grained Opinion Analysis
on MPQA
(F1 (Opinion) metric)
Modularized Interaction Network for Named Entity Recognition
Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks.
A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession.
Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer Networks
A majority of research interests in irregular (eg, nested or discontinuous) named entity recognition (NER) have been paid on nested entities, while discontinuous entities received limited attention.
Encoder-decoder based unified semantic role labeling with label-aware syntax
Currently the unified semantic role labeling (SRL) that achieves predicate identification and argument role labeling in an end-to-end manner has received growing interests.
Generative Actor-Critic: An Off-policy Algorithm Using the Push-forward Model
Model-free deep reinforcement learning has achieved great success in many domains, such as video games, recommendation systems and robotic control tasks.
Stochastic Model Predictive Control for Linear Systems with Unbounded Additive Uncertainties
Secondly, by utilizing the probabilistic positively invariant set, the probabilistically resolvable constant tube-based stochastic model predictive control algorithm is developed by employing the constantly tightened constraints in the entire prediction horizons.
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network
We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded.
Multiple HC$_3$N line observations towards 19 Galactic massive star-forming regions
We performed observations of the HC$_3$N (24-23, 17-16, 11-10, 8-7) lines towards a sample consisting of 19 Galactic massive star-forming regions with the Arizona Radio Observatory 12-m and Caltech Submillimeter Observatory 10. 4-m telescopes.
Astrophysics of Galaxies Solar and Stellar Astrophysics
Pedestrian Motion State Estimation From 2D Pose
The proposed method only needs the previous hidden state of GRU and current feature to evaluate the probability of current motion state, and it is computation efficient to deploy on vehicles.
Open-set Intersection Intention Prediction for Autonomous Driving
We capture map-centric features that correspond to intersection structures under a spatial-temporal graph representation, and use two MAAMs (mutually auxiliary attention module) that cover respectively lane-level and exitlevel intentions to predict a target that best matches intersection elements in map-centric feature space.
HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations
Emotion detection in conversations (EDC) is to detect the emotion for each utterance in conversations that have multiple speakers.
Ranked #19 on
Emotion Recognition in Conversation
on EmoryNLP
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding.
Cross-lingual Semantic Role Labeling with Model Transfer
In this paper, we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods.
BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab
In addition, the GUI interface enables researchers with limited computer background to compose tools into NLP pipelines and then apply the pipelines on their own datasets in a {``}what you see is what you get{''} (WYSIWYG) way.
Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT
We consider it to be the first work to detect angle-closure glaucoma by means of 3D representation.
Open-Narrow-Synechiae Anterior Chamber Angle Classification in AS-OCT Sequences
However, clinical diagnosis requires a more discriminating ACA three-class system (i. e., open, narrow, or synechiae angles) for the benefit of clinicians who seek better to understand the progression of the spectrum of angle-closure glaucoma types.
AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019.
ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network
The innovations of our model are two-folds: it utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutional layer to enlarge the receptive field.
Ranked #9 on
Medical Code Prediction
on MIMIC-III
Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs
abstract In this article, we describe our approach for the Bacteria Biotopes relation extraction (BB-rel) subtask in the BioNLP Shared Task 2019.
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
Optimal Delivery with Budget Constraint in E-Commerce Advertising
Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals.
Generating Classical Chinese Poems from Vernacular Chinese
According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models.
Cultural Vocal Bursts Intensity Prediction
Reinforcement Learning (RL)
+2
Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks
We argue that the discard of the correlated discriminative information is partially caused by the fact that the minimization of the classification loss doesn't ensure to learn the overall discriminative information but only the most discriminative information.
HYPE: A High Performing NLP System for Automatically Detecting Hypoglycemia Events from Electronic Health Record Notes
We used this annotated dataset to train and evaluate HYPE, supervised NLP systems for hypoglycemia detection.
IcoRating: A Deep-Learning System for Scam ICO Identification
For the best setting, the proposed system is able to identify scam ICO projects with 0. 83 precision.
Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary Sparse Representation
Band selection is a direct and effective method to remove redundant information and reduce the spectral dimension for decreasing computational complexity and avoiding the curse of dimensionality.
Recognizing irregular entities in biomedical text via deep neural networks
We evaluate our models on two datasets for recognizing regular and irreg- ular biomedical entities.
A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features
This model divides a sentence or text segment into five parts, namely two target entities and their three contexts.
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior
To reconstruct hyperspectral image (HSI) accurately from a few noisy compressive measurements, we present a novel manifold-structured sparsity prior based hyperspectral compressive sensing (HCS) method in this study.
A density compensation-based path computing model for measuring semantic similarity
Therefore, in order to solve the problem of non-uniformity of concept density in a large taxonomic ontology, we propose a new path computing model based on the compensation of local area density of concepts, which is equal to the number of direct hyponyms of the subsumers of concepts in their shortest path.
Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity
To address this problem, a novel reweighted Laplace prior based hyperspectral compressive sensing method is proposed in this study.
Large-Scale Category Structure Aware Image Categorization
With the emergence of structured large-scale dataset such as the ImageNet, rich information about the conceptual relationships between images, such as a tree hierarchy among various image categories, become available.
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition
We present a novel approach to efficiently learn a label tree for large scale classification with many classes.
