Search Results for author:
Found 22 papers, 4 papers with code
A Practice of Tourism Knowledge Graph Construction based on Heterogeneous Information
So we develop two pipelines of processing methods for semi-structured data and unstructured data respectively.
Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection
In this paper, we address these problems from a view that utilizes contexts of the cracks and propose an end-to-end deep learning method to model the context information flow.
A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification
Semi-supervised learning (SSL) is a practical challenge in computer vision.
Push Quantization-Aware Training Toward Full Precision Performances via Consistency Regularization
Existing Quantization-Aware Training (QAT) methods intensively depend on the complete labeled dataset or knowledge distillation to guarantee the performances toward Full Precision (FP) accuracies.
Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression
Both semi-supervised classification and regression are practically challenging tasks for computer vision.
Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation
Semi-supervised pose estimation is a practically challenging task for computer vision.
Ghost-free High Dynamic Range Imaging via Hybrid CNN-Transformer and Structure Tensor
In this letter, we present a hybrid model consisting of a convolutional encoder and a Transformer decoder to generate ghost-free HDR images.
Learning to Kindle the Starlight
Capturing highly appreciated star field images is extremely challenging due to light pollution, the requirements of specialized hardware, and the high level of photographic skills needed.
Multimodal Image Fusion based on Hybrid CNN-Transformer and Non-local Cross-modal Attention
In this article, we present a hybrid model consisting of a convolutional encoder and a Transformer-based decoder to fuse multimodal images.
Privacy Information Classification: A Hybrid Approach
A large amount of information has been published to online social networks every day.
Athena: Constructing Dialogues Dynamically with Discourse Constraints
This report describes Athena, a dialogue system for spoken conversation on popular topics and current events.
Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System
In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven.
SlugBot: Developing a Computational Model andFramework of a Novel Dialogue Genre
One of the most interesting aspects of the Amazon Alexa Prize competition is that the framing of the competition requires the development of new computational models of dialogue and its structure.
Implicit Discourse Relation Identification for Open-domain Dialogues
Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system.
Implicit Discourse Relation Classification
Implicit Relations
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CruzAffect at AffCon 2019 Shared Task: A feature-rich approach to characterize happiness
Our results indicate that generic characteristics are shared between the classes of agency, social and concepts, suggesting it should be possible to build general models for affective classification tasks.
SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems
In dialogue systems, the tasks of named entity recognition (NER) and named entity linking (NEL) are vital preprocessing steps for understanding user intent, especially in open domain interaction where we cannot rely on domain-specific inference.
Slugbot: An Application of a Novel and Scalable Open Domain Socialbot Framework
In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics.
Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue
The greatest challenges in building sophisticated open-domain conversational agents arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need.
Data-Driven Dialogue Systems for Social Agents
In order to build dialogue systems to tackle the ambitious task of holding social conversations, we argue that we need a data driven approach that includes insight into human conversational chit chat, and which incorporates different natural language processing modules.
Learning Lexico-Functional Patterns for First-Person Affect
Informal first-person narratives are a unique resource for computational models of everyday events and people's affective reactions to them.
Linguistic Reflexes of Well-Being and Happiness in Echo
Our goal is to ground the linguistic descriptions of events that users experience in theories of well-being and happiness, and then examine the extent to which different theoretical accounts can explain the variance in the happiness scores.
Modelling Protagonist Goals and Desires in First-Person Narrative
Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives.
