1,341 Result(s)
within
Chapter
**hu Li
English
Artificial Intelligence
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
-
Chapter and Conference Paper
MPANet: Multi-scale Pyramid Attention Network for Collaborative Modeling Spatio-Temporal Patterns of Default Mode Network
The functional activity of the default mode network (DMN) in the resting state is complex and spontaneous. Modeling spatio-temporal patterns of DMN based on four-dimensional Resting-state functional Magnetic R...
-
Chapter and Conference Paper
S5TR: Simple Single Stage Sequencer for Scene Text Recognition
As an active research topic in computer vision, scene text recognition (STR) aims to recognize character sequences in natural scenes. Currently, mainstream STR approaches consist of two main modules: a visual ...
-
Chapter and Conference Paper
Context-Based Masking for Spontaneous Venous Pulsations Detection
Spontaneous retinal venous pulsations (SVP) serve as vital dynamic biomarkers, representing rhythmic changes of the central retinal vein observed at the optic disc region (ODR) within an eye. SVPs serve as vit...
-
Chapter and Conference Paper
Impact of Fidelity and Robustness of Machine Learning Explanations on User Trust
EXplainable machine learning (XML) has recently emerged as a promising approach to address the inherent opacity of machine learning (ML) systems by providing insights into their reasoning processes. This paper...
-
Chapter and Conference Paper
SAR2EO: A High-Resolution Image Translation Framework with Denoising Enhancement
Synthetic Aperture Radar (SAR) to electro-optical (EO) image translation is a fundamental task in remote sensing that can enrich the dataset by fusing information from different sources. Recently, many methods...
-
Chapter and Conference Paper
Hybrid CNN-Interpreter: Interprete Local and Global Contexts for CNN-Based Models
Convolutional neural network (CNN) models have seen advanced improvements in performance in various domains, but lack of interpretability is a major barrier to assurance and regulation during operation for acc...
-
Chapter and Conference Paper
Causal Disentanglement for Adversarial Defense
Representation learning that seeks the high accuracy of a classifier is a key contribute to the success of state-of-the-art DNNs. However, DNNs face the threat of adversarial attacks and their robustness is in...
-
Chapter and Conference Paper
No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling
Vision Transformers (ViTs) have demonstrated outstanding performance in computer vision tasks, yet their high computational complexity prevents their deployment in computing resource-constrained environments. ...
-
Chapter and Conference Paper
Towards Learning Action Models from Narrative Text Through Extraction and Ordering of Structured Events
Event models, in the form of scripts, frames, or precondition/effect axioms, allow for reasoning about the causal and motivational connections between events in a story, and thus are central to AI understandin...
-
Chapter and Conference Paper
Part-Aware Prototype-Aligned Interpretable Image Classification with Basic Feature Domain
In recent years, the interpretive this looks like that structure has gained significant attention. It refers to the human tendency to break down images into key parts and make classification decisions by comparin...
-
Chapter and Conference Paper
Aging Contrast: A Contrastive Learning Framework for Fish Re-identification Across Seasons and Years
The fields of biology, ecology, and fisheries management are witnessing a growing demand for distinguishing individual fish. In recent years, deep learning methods have emerged as a promising tool for image-ba...
-
Chapter and Conference Paper
Gemini: A Dual-Task Co-training Model for Partial Label Learning
Partial-Label Learning (PLL) is an important weakly supervised learning task that assumes each training instance is annotated with a set of candidate labels. In recent years, self-training PLL models, which le...
-
Chapter and Conference Paper
Mining Label Distribution Drift in Unsupervised Domain Adaptation
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain. Although tremendous efforts have been made to minimize domain divergence, most e...
-
Chapter and Conference Paper
CTKM: Crypto-Based User Clustering on Web Transaction Data
User transaction data are rich, valuable, but sensitive. With the huge amounts of transaction data, data mining algorithms can make many applications practical, such as customer-behavior analysis, marketing, a...
-
Chapter and Conference Paper
Optimizing pcsCPD with Alternating Rank-R and Rank-1 Least Squares: Application to Complex-Valued Multi-subject fMRI Data
Complex-valued shift-invariant canonical polyadic decomposition (CPD) under a spatial phase sparsity constraint (pcsCPD) showed satisfying separation performance of decomposing three-way multi-subject fMRI dat...
-
Chapter and Conference Paper
Pessimistic Adversarially Regularized Learning for Graph Embedding
Autoencoder frameworks have been effectively employed for graph embedding, resulting in successful analysis of graph in low-dimensional space. Recently, generative models (GANs), which learn data distribution ...
-
Chapter and Conference Paper
Difficulty-Controlled Question Generation in Adaptive Education for Few-Shot Learning
Adaptive education aims to achieve common educational goals by implementing targeted education based on differences in student status. However, existing intelligent teaching methods cannot be applied in data-l...
-
Chapter and Conference Paper
ND-NER: A Named Entity Recognition Dataset for OSINT Towards the National Defense Domain
The public data on the Internet contains a large amount of high-value open source intelligence (OSINT) for the national defense. As the fundamental information extraction task, Named Entity Recognition (NER) p...
-
Chapter and Conference Paper
Development and Application of Flight Parameter Data Analysis Based on Multi-text Timescale Alignment
Flight parameter data plays a crucial role in comprehensively capturing the operational characteristics of various aircraft components and recording essential flight status information. This data holds signifi...
-
Chapter and Conference Paper
Graph Convolution Recurrent Denoising Diffusion Model for Multivariate Probabilistic Temporal Forecasting
The probabilistic estimation for multivariate time series forecasting has recently become a trend in various research fields, such as traffic, climate, and finance. The multivariate time series can be treated ...
