Search Results for author:
Found 36 papers, 7 papers with code
Dynamic Typography: Bringing Text to Life via Video Diffusion Prior
Through quantitative and qualitative evaluations, we demonstrate the effectiveness of our framework in generating coherent text animations that faithfully interpret user prompts while maintaining readability.
TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows.
Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks
Financial cluster analysis allows investors to discover investment alternatives and avoid undertaking excessive risks.
Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration
Data storytelling is powerful for communicating data insights, but it requires diverse skills and considerable effort from human creators.
Storyfier: Exploring Vocabulary Learning Support with Text Generation Models
Vocabulary learning support tools have widely exploited existing materials, e. g., stories or video clips, as contexts to help users memorize each target word.
VideoPro: A Visual Analytics Approach for Interactive Video Programming
Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection.
CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models
Specifically, we extract relevant commonsense knowledge in inputs as references to align model behavior with human knowledge.
ScrollTimes: Tracing the Provenance of Paintings as a Window into History
The study of cultural artifact provenance, tracing ownership and preservation, holds significant importance in archaeology and art history.
Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling
Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication.
XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis
Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries.
ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset
Since shortcuts vary in coverage, productivity, and semantic meaning, it is challenging for NLU experts to systematically understand and avoid them when creating benchmark datasets.
GestureLens: Visual Analysis of Gestures in Presentation Videos
There lacks an efficient method to help users conduct gesture exploration, which is challenging due to the intrinsically temporal evolution of gestures and their complex correlation to speech content.
Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion
In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts.
Interactive Data Analysis with Next-step Natural Language Query Recommendation
Natural language interfaces (NLIs) provide users with a convenient way to interactively analyze data through natural language queries.
NumGPT: Improving Numeracy Ability of Generative Pre-trained Models
However, those models do not consider the numerical properties of numbers and cannot perform robustly on numerical reasoning tasks (e. g., math word problems and measurement estimation).
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks.
DeHumor: Visual Analytics for Decomposing Humor
Despite being a critical communication skill, grasping humor is challenging -- a successful use of humor requires a mixture of both engaging content build-up and an appropriate vocal delivery (e. g., pause).
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis
Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels.
GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.
DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models
With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable.
Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network
Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network and further present a new GNN model, called R^2GCN, which intrinsically works for the heterogeneous networks, to achieve generalizable student performance prediction in interactive online question pools.
Visual Analysis of Discrimination in Machine Learning
The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning.
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models
In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models.
VoiceCoach: Interactive Evidence-based Training for Voice Modulation Skills in Public Speaking
The modulation of voice properties, such as pitch, volume, and speed, is crucial for delivering a successful public speech.
SirenLess: reveal the intention behind news
In this paper, we present SirenLess, a visual analytical system for misleading news detection by linguistic features.
Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms
With increasing popularity in online learning, a surge of E-learning platforms have emerged to facilitate education opportunities for k-12 (from kindergarten to 12th grade) students and with this, a wealth of information on their learning logs are getting recorded.
LassoNet: Deep Lasso-Selection of 3D Point Clouds
A hierarchical network is trained using a dataset with over 30K lasso-selection records on two different point cloud data.
Human-Computer Interaction Graphics
EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos
Our visualization system features a channel coherence view and a sentence clustering view that together enable users to obtain a quick overview of emotion coherence and its temporal evolution.
Interpretable and Steerable Sequence Learning via Prototypes
The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain.
DeepDrawing: A Deep Learning Approach to Graph Drawing
Node-link diagrams are widely used to facilitate network explorations.
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts.
DeepTracker: Visualizing the Training Process of Convolutional Neural Networks
To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of training log.
Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach
In this paper, we present a novel deep learning-based approach to evaluate the readability of graph layouts by directly using graph images.
RuleMatrix: Visualizing and Understanding Classifiers with Rules
With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable.
Understanding Hidden Memories of Recurrent Neural Networks
We propose a technique to explain the function of individual hidden state units based on their expected response to input texts.
CNNComparator: Comparative Analytics of Convolutional Neural Networks
Often, it is difficult to explore the relationships between the learned parameters and the model performance due to a large number of parameters and different random initializations.
