Search Results for author:
Found 19 papers, 4 papers with code
DualTalker: A Cross-Modal Dual Learning Approach for Speech-Driven 3D Facial Animation
In recent years, audio-driven 3D facial animation has gained significant attention, particularly in applications such as virtual reality, gaming, and video conferencing.
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data.
NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation
Motivated by the successful breakthroughs of graph neural networks (GNN) in capturing topological properties and non-Euclidean relationships across various fields, we propose NexToU, a novel hybrid architecture for medical image segmentation.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
DECOR-NET: A COVID-19 Lung Infection Segmentation Network Improved by Emphasizing Low-level Features and Decorrelating Features
Since 2019, coronavirus Disease 2019 (COVID-19) has been widely spread and posed a serious threat to public health.
Keyword Decisions in Sponsored Search Advertising: A Literature Review and Research Agenda
In sponsored search advertising (SSA), keywords serve as the basic unit of business model, linking three stakeholders: consumers, advertisers and search engines.
Causality-based CTR Prediction using Graph Neural Networks
This paper is dedicated to developing a causality-based CTR prediction model in the GNNs framework (Causal-GNN) integrating representations of feature graph, user graph and ad graph in the context of online advertising.
Anchor-free temporal action localization via Progressive Boundary-aware Boosting
The TCM aggregates the temporal context information and provides features for the IBM and the FPBM.
Accelerate adversarial training with loss guided propagation for robust image classification
In this work, we propose an efficient adversarial training method with loss guided propagation (ATLGP) to accelerate the adversarial training process.
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation
Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance.
Multi-modal Dynamic Graph Network: Coupling Structural and Functional Connectome for Disease Diagnosis and Classification
Graph neural networks have been proven to be of great importance in modeling brain connectome networks and relating disease-specific patterns.
Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching
Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions.
Estimating Brain Age with Global and Local Dependencies
The brain age has been proven to be a phenotype of relevance to cognitive performance and brain disease.
Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study
Originality: This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.
Optimal Keywords Grouping in Sponsored Search Advertising under Uncertain Environments
This suggests a warning for advertisers that it's not wise to take the number of keywords as the criterion for keywords grouping decisions.
Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework
This paper proposes a multi-level and closed-form computational framework for keyword optimization (MKOF) to support various keyword decisions.
Learning Parameters for a Generalized Vidale-Wolfe Response Model with Flexible Ad Elasticity and Word-of-Mouth
The GVW model and its deep learning-based estimation method provide a basis to support big data-driven advertising analytics and decision makings; in the meanwhile, identified properties and experimental findings of this research illuminate critical managerial insights for advertisers in various advertising forms.
Click-Through Rate Prediction in Online Advertising: A Literature Review
Specifically, we give a classification of state-of-the-art CTR prediction models in the extant literature, within which basic modeling frameworks and their extensions, advantages and disadvantages, and performance assessment for CTR prediction are presented.
Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net
We exploit the medical image segmentation uncertainty quantification by measuring segmentation performance with multiple annotations in a supervised learning manner and propose a U-Net based architecture with multiple decoders, where the image representation is encoded with the same encoder, and segmentation referring to each annotation is estimated with multiple decoders.
